Socialist Tyranny or the Saviour of the US Economy: What was the effect of Fiscal Policy on the recovery of the US

Economy during the Great Depression in the years 1933 to 1938?

Word Count: 9, 979

London School of Economics and Political ScienceEconomic History Department

Candidate Number: 43488

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Contents ! ! ! ! ! ! ! ! ! ! ! Chapter 1: Introduction! ! ! ! ! ! ! ! ! 7

! 1.1 Overview!! ! ! ! ! ! ! ! ! 7!! 1.2 Research Aim! ! ! ! ! ! ! ! ! 9

! 1.3 Economic Background of Fiscal Policy ! ! ! ! ! 9

! 1.4 Significance of Period Under Review! ! ! ! ! 10

! 1.5 Structure of Dissertation! ! ! ! ! ! ! 10!

Chapter 2: Literature Review! ! ! ! ! ! ! ! 11

! 2.1 Importance and Nature of Fiscal Policy ! ! ! ! ! 11

! 2.2 Data Collection Issues!! ! ! ! ! ! ! 12

! 2.3 New Deal! ! ! ! ! ! ! ! ! 13

! 2.4 Effectiveness of Fiscal Policy in 1930s! ! ! ! ! 13

! 2.5 Summary ! ! ! ! ! ! ! ! ! 15!

Chapter 3: Methodology ! ! ! ! ! ! ! ! ! 16

! 3.1 Data! ! ! ! ! ! ! ! ! ! 16

! 3.2 Method! ! ! ! ! ! ! ! ! ! 16!

Chapter 4: Results! ! ! ! ! ! ! ! ! ! 23

! 4.1 Raw Data! ! ! ! ! ! ! ! ! 23

! 4.2 Spline Interpolation! ! ! ! ! ! ! ! 26

! 4.3 Chow Lin Interpolation!! ! ! ! ! ! ! 37

! 4.4 Denton Method!! ! ! ! ! ! ! ! 44

! 4.5 Hodrick Prescott Filter ! ! ! ! ! ! ! 49!

Chapter 5: Conclusion! ! ! ! ! ! ! ! ! 50

! 5.1 Evaluation! ! ! ! ! ! ! ! ! 50!!! 5.2 Conclusions on the Research Aim! ! ! ! ! ! 50

! 5.3 Limitations! ! ! ! ! ! ! ! ! 51

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!! 5.4 Suggestions for Future Research! ! ! ! ! ! 51!Appendices! ! ! ! ! ! ! ! ! ! ! 53

Bibliography!! ! ! ! ! ! ! ! ! ! 68

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List of Tables

4.1 ! Eigenvalues of the VAR model using spline interpolated data! ! 274.2 Lag selection criteria tests for the VAR model using spline data 274.3 Results of the normality tests for the VAR model! ! ! ! 284.4! Tests for autocorrelation! ! ! ! ! ! ! ! 284.5! Granger Causality Tests! ! ! ! ! ! ! ! 294.6! Eigenvalues for the VAR model using the Chow Lin Method!! ! 374.7! Lag selection criteria tests for the VAR model! ! ! ! ! 384.8! Tests for autocorrelation! ! ! ! ! ! ! ! 394.9! Tests for normality ! ! ! ! ! ! ! ! ! 394.10 ! Granger Causality Tests! ! ! ! ! ! ! ! 42

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List of Figures

1.1 ! Debt and Deficit as a percentage of GNP! ! ! ! ! 74.0 ! First differences of Output using spline data! ! ! ! ! 234.1! First differences of Government Spending using spline data!! ! 244.2! First differences of the Monetary Base using spline data! ! ! 254.3! First differences in the Federal Funds rate! ! ! ! ! 264.4! Unit roots of the VAR model using spline interpolation!! ! ! 284.5! Impulse Response Function - Government Expenditure to GDP! ! 304.6! Impulse Response Function with two orderings! ! ! ! 314.7! Impulse Response Function - Federal Funds Rate to GDP! ! ! 324.8! Impulse Response Function with two orderings ! ! ! ! 334.9! Impulse Response Function - First differenced Government Spending! 344.10! Impulse Response Function with two orderings! ! ! ! 354.11! Impulse Response function - First differenced Federal Funds Rate!! 364.12! Unit roots of the VAR model using Chow Lin Interpolation! ! ! 384.13! Impulse Response Function - Government Expenditure to GDP! ! 404.14! Impulse Response Function - Federal Funds Rate to GDP! ! ! 414.15! Impulse Response Function - First differenced Government Spending! 404.16! Impulse Response Function - First Differenced New York Fed Discount! 414.17! Impulse Response Function - Government Expenditure (Denton)! ! 454.18! Impulse Response Function - with two orderings! ! ! ! 464.19! Impulse Response Function - NY Federal Discount Rate! ! ! 464.20! Impulse Response Function - First differenced Government Spending! 474.21! Impulse Response Function - First differenced NY Federal Discount ! 48

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Appendices

Tables

A1! Coefficients for the variables in the VAR model using spline!! ! 56A2! Orderings for the Impulse Response Functions! ! ! ! 58A3 ! Indicators used for the Denton Interpolation! ! ! ! ! 59A4! Lagrange Multiplier Test for first differenced spline VAR model! ! 59A5! Tests for normality on first differenced spline VAR model! ! ! 60A6! Tests for stability on first differenced spline VAR model! ! ! 61A7! Test for stability on first differenced Chow Lin VAR model! ! ! 61A8! Tests for normality on first differenced Chow Lin VAR model!! ! 62A9! Tests for autocorrelation! ! ! ! ! ! ! ! 63A10! Tests for stability of Denton VAR model! ! ! ! ! ! 63A11! Tests for autocorrelation of Denton Model! ! ! ! ! 63A12! Tests for normality of Denton Model! ! ! ! ! ! 64A13! Tests for stability on first differenced Denton VAR model! ! ! 65A14! Tests for normality on first differenced Denton VAR model! ! ! 66A15! Tests for autocorrelation on first differenced Denton VAR model! ! 67A16 ! Granger Causality results for the Denton VAR model! ! ! ! 67!

Figures A1! First Differences of Real Consumption! ! ! ! ! ! 54A2! First Differences of Real Investment ! ! ! ! ! ! 55

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Chapter 1: Introduction

1.1 Overview

Prior to the Great Depression of the 1930s, the guiding principal behind fiscal policy in the United States1 was that a government which regularly reported surpluses in its budget would be beneficial to the long-term health of the economy. The prevailing economic thinking at the time for US policy makers was that Government borrowing would increase interest rates and thus retard growth. Therefore, according to Elmendorf and Mankiw (1999), only in times of war would the Federal Government borrow money2 up until the Great Depression and as shown in Figure (1.1)3 .

Figure (1.1): Debt and and deficit of the United States as a percentage of GNP from 1791 - 1996. (Source: Elmendorf and Mankiw (1999))

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1 Henceforth known as the US.

2 The debt was promptly repaid

3 Where there significant increases in the debt and deficits in the years of the Cvil War (1860-1865); World War I (1914-1918 - US entered in 1917); and World War II (1939-1945 - US entered in 1942)

Whilst the US had experienced recessions4 before, the shock to the economy that triggered the Great Depression was systemic5 and caused the US to report its first significant peacetime budget deficit as depicted in Figure (1.1). The continuing hardship felt by Americans meant that government intervention in the economy was perceived as a viable means of stabilisation rather than allowing a contumacious atmosphere to fester. This meant an aggrandisement of the US government was now regarded as politically possible in order to ameliorate the US economy.

Over the course of the 1930s, Government spending rose. In real terms (1958 dollars) federal spending soared from an average of $7.3 billion in the period 1930-32 to an average of $15.7 billion in the period between 1934-39 while the deficit as a percentage of GDP proliferated over the course of the 1930s according to Fishback (2007).

The recovery from 19336 was made possible by a significant change in the policy regime of the US. In prior years, the initial recession was effectively a self perpetuating downward spiral into a depression. Investors and workers made rational decisions based not on the basis of isolated government and central bank actions but on the existing body politic which led to the formation of expectations for further deflation and greater hardship according to Temin (2006). President Hoover followed the more doctrinaire view by adhering to the Gold Standard and supporting the credit markets, the administration effectively became deflationists. Whilst isolated departures from this view occurred7, for the US economy to escape the Depression a discontinuous break in the policy regime needed to occur that would bring with it fresh policies that would assist Americaʼs ailing economy.

President Roosevelt8 precipitated a discontinuous break from the past regime according to Temin (2006). The policy programs that were introduced under Roosevelt were radically different from his predecessor. Whilst he still maintained the wish of a balanced budget,

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4 For instance the Depression of 1920-21; Panic of 1910-11; 1902-04 Recession.

5 A 30 percent fall in real GDP in the years between 1929-1933

6 The year in which the United Statesʼ economy reached its trough and the start date of the dataset used in this dissertation.

7 For example, The Reconstruction Finance Corporation which promoted investment through loans that went primarily to banks

8 President from 1933 to 1945.

Rooseveltʼs policies attempted to correct the fundamental structural flaws that led to Americaʼs downfall and return unemployed Americans back to work.

In order to achieve this recovery, an array of economic policies were introduced by the Roosevelt administration including the New Deal9 and the restructuring of Americaʼs financial industry10.

1.2 Research Aim

The concern of this dissertation is to isolate the effectiveness of fiscal policy, whilst analysing monetary variables, in promoting the recovery of output from the Great Depression using a Vector Autoregressive11 model. The debate between liberal and conservative economists over the efficacy of fiscal policy continues today and this dissertation has sought to add to the body of knowledge through econometrics rather than partisan emotions around whether government spending is a choice between socialism or liberty.

1.3 Economic Background to Fiscal Policy

The effectiveness of fiscal policy in the recovery of an economy is a contentious issue within the field of economics. One school of thought, the neoclassical approach, cites that according to Ricardian equivalence, the time of taxes or spending are irrelevant to economic variables. Tax cuts and deficits have no stimulative effect given that only the net present value of government expenditures really matters but only if taxes are non-distortionary according to Barro (1979). An increase in Government spending leads to a negative wealth effect for the representative household resulting in a rise in labour supply and output. This augmentation to the labour supply leads to a lower real wage. Once the economy returns to its steady state, consumption is lower and labour supply is higher. A temporary escalation in Government spending has a smaller effect on lifetime wealth and thus leads to a smaller impact on output and consumption. Non-productive government expenditures increases output but crowds out consumption and investment leading to

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9 Introduction of new Government programmes that increased fiscal expenditure.

10 Such as the Glass Steagall Act (1933) that radically overhauled the financial industry.

11 Henceforth known as VAR model.

multipliers lower than one. Accordingly, the neoclassical approach disputes the stimulative effects of government expenditures.

Whereas, Keynesians believe that government spending or tax cuts act as a demand stimulus to boost output when output is below its natural rate. This can lead to greater consumption and wages in response to government spending. As Coddington (2010) purports this is based on the premise that government expenditures have the capacity to influence output. However, if high deficits persist in the medium term, these aforementioned deficits have the capacity to crowd out investment and can result in significant costs12. Friedman (2006) asserts that government deficits, sustained year after year even when the economy is operating at full employment, reduce net capital formation and induce foreign borrowing which are both harmful.

1.4 Significance of Period under review

The period under consideration in this dissertation is significant and has not been researched in any meaningful manner13 to the authorsʼ knowledge. According to the National Bureau of Economic Research, the US economy experienced a trough in the first quarter of March 1933 and a peak in May 1937 but to ensure the assumptions of the VAR model are satisfied the period was extended by a year to 1938.

1.5 Structure of Dissertation

In Chapter 2, the existing body of literature will be analysed for its shortcomings and how this dissertation will address these issues. Chapter 3 will present the method used throughout this dissertation along with several interpolation methods implemented to generate quarterly data for the key variables. Chapter 4 provides the findings of the VAR models and finally Chapter 5 provides a definitive conclusion to the research problem, posed in Section 1.2, and possible areas for future research.

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12 As a result of interest payments used to service the debt.

13 See Section 2.4

Chapter 2: Literature Review

2.1 Importance and nature of Fiscal Policy

Firstly, the presence of fiscal policy within policy circles during the 1930s is under dispute. Temin (2006) asserts that fiscal policy does not deserve any recognition for the recovery of the US economy from the Great Depression. Temin (2006) argues that the government budget did not alter from year to year and the fiscal deficit did not rise. These strong sentiments about US fiscal policy are in conflict with the data used in this dissertation. During the 1930s, the Federal deficit and public debt rose. Moreover, the Federal programs that were implemented by Roosevelt, for instance the New Deal which significantly expanded the functions of the government, needed to be paid for. To further disprove Teminʼs (2006) claim, nominal expenditures rose by nearly 45% in the years between 1933 to 1934. Therefore, to completely repudiate the effects of fiscal policy on the recovery of the US economy from the Great Depression is evidently incorrect.

However, it should be noted that the 1930s was not a period in which Keynesian logic reigned supreme and fiscal policy was not the expansionary stimulus that ensured factors of production were fully utilised as Keynes envisioned. Fishback (2007) includes a 1941 quote from Alva Hansen14 which states ʻdespite the fairly good showing made in the recovery up to 1937, the fact is that neither before nor since has the administration pursued a really positive expansionist program...For the most part the federal government engaged in a salvaging program and not in a program of positive expansion.ʼ

Hence, this dissertation aims to answer the question of the effectiveness of fiscal policy through a VAR model15 and does not dismiss its stimulative effect entirely whilst maintaining the notion that there was not a fully pledged stimulative plan along the lines of postwar policies.

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14 The individual partially responsible for the circulation of Keynesʼ ideas throughout the economics profession

15 See Section 1.2

2.2 Data Collection Issues

There are natural difficulties in estimating the effects of government expenditure in the economy. Ramey (2011) demonstrates that state and local governments expenditures account for a great proportion of non-defence government expenditures and there is no real exogenous factor on this type of spending16. This type of expenditure is subject to local factors caused by changes in aggregate and local economic conditions. To address this issue, this dissertation has sought to limit the period which is considered (1933-1938) while also seeking to establish the effects of government spending as a whole. It is extremely difficult to be certain that the data compiled by the Historical Statistics of the United States and NBER accurately accounts for state level data owing to the difficultly compiling such data. However, when selecting the dataset, reputable sources were always used17. This came at a sacrifice of higher frequency data but interpolation methods utilised in this dissertation18 have sought to overcome this pitfall by using indicators from the said reliable sources.

2.3 New Deal

When discussing the effects of fiscal policy during this era, President Rooseveltʼs signature policy, the New Deal naturally arises given this set of programs often led to greater Government expenditure and changed the face of the Federal Governmentʼs involvement in the USUS economy. The effectiveness of New Deal policies in the recovery of the US economy is in dispute by several scholars. Cole and Ohanian (2004) suggest that the New Deal even prolonged the Great Depression by using a General Equilibrium model. Cole and Ohanian (2004) find that Rooseveltʼs belief that excessive competition was responsible for the Great Depression led to the arrangement of collusive agreements between companies without fear of anti-trust indictment. It also led to higher wages and prices above the equilibrium set out by market forces. These two parts of the New Deal were thought to have held back the recovery of the US economy accounting for 60% of the difference between actual and trend output.

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16 Such as a war for defence spending.

17 See Section 3.1

18 See Section 3.2.1

Whereas, Temin and Wigmore (2004) found that after the inauguration of President Roosevelt in 1933, the new macroeconomic policies led to a change in expectations and stimulated investment. Crucial to the alteration in the policy regime was the devaluation of the dollar and the rise in farm prices and incomes. This new found freedom in policy making came from the abandonment of the Gold Standard which, when adhered to, caused tremendous damage to the prospects of recovery from the Depression according to Eichengreen (1996).

Additionally, the amount of discretionary power provided to the Federal Government under New Deal policies has led to charges that it was designed to enable the Democratic party win elections in swing states rather than stimulate depressed areas. Wright (1974) found that there was a relation between the high levels of spending in the swing states of the West and low levels of spending in the South even though the per capita incomes of residents in the South19 were far lower than in the West.

2.4 Effectiveness of Fiscal Policy during the 1930s

Prior attempts have been made to estimate the effect of fiscal policy during this period. One of the most prominent articles in the existing body of literature is Romer (1992). Romer (1992), argues that fiscal policy had little effect upon the recovery of the US from the Great Depression. To estimate the effect, Romer (1992) utilises lagged annual figures to estimate a regression between output changes in the current period and monetary and fiscal policy changes in the previous periods. This yields the conclusion that an influx of gold into the US substantially lowered interest rates and stimulated output. One of the principal issues with Romer (1992) is the use of annualised data which diminishes the sample size. In this dissertation, to ensure greater accuracy in the results of the VAR, a cubic spline interpolation has been used to estimate quarterly data for the period. Also, to provide support to the findings in this dissertation, the Chow-Lin interpolation20and Denton interpolation methods have been used in the estimation of quarterly figures for the variables of the VAR model.

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19 According to Wright (1996)

20 As calculated by Gordon and Krenn (2010).

Furthermore, Gordon and Krenn (2010) raise some fundamental issues relating to Romerʼs (1992) approach when considering the subject matter with regards to her rather liberal use of assumptions when considering monetary policy. One of the primary problems raised concern the assumption that changes in money supply impact upon nominal GDP with an elasticity of one. Using Gordon and Krennʼs data set, the velocity of M1 was turbulent during the period under consideration and Romer (1992) does not fully corroborate this evidence by assuming that velocity was exogenous rather than negatively correlated with an increase in money. Ergo, Romerʼs (1992) results could plausibly bias the estimation of the efficacy of monetary policy. To ensure the accuracy of this dissertation in making inferences from the data, the VAR model that has been used on the sample has been extensively tested to ensure all assumptions, especially stationarity, permit conclusions that are justified given the sample data with weaker assumptions.

Also, De Long and Summers (1988) tested the natural rate hypothesis, where output and employment have self-corrective powers that enables an economy to revert back to its original long-run growth path. They find that there is some form of mean reversion especially in the recovery from the Great Depression. One important criticism emerges from the paper, highlighted by Gordon and Krenn (2010), was that by the time World War II began, more than five-sixths of the Depressionʼs decline in output relative to trend had been made up. They assert that this cannot be attributed to the Federal Governmentʼs fiscal policy and neither deficits or surpluses on trade accounts became appreciable shares of national income prior to 1941. However, some of the judgements that De Long and Summers (1988) make are ill-judged. The continued use of deficits and surpluses can shroud the effect of fiscal policy on the economy. Given that taxation revenues rose over the period and deficits did not achieve a high percentage of GDP, it seems misguided at best to solely focus on the balance of the governmentʼs accounts rather than gross government spending within the economy21.

Gordon and Krenn (2010) have applied the VAR methodology similar to this dissertation in the period between 1937 to 1941. Whilst their is approach is rigorous, the suitability of the VAR model to the period that they consider is contentious. During the research process, one of the challenges faced was the formulation of a stable VAR model. Gordon and Krenn (2010) consider a period in which all of the variables that constitute their VAR model

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21 See Section 3.2.2

all rise thus meaning that the assumption of stationarity seems tenuous at the very least. Therefore, the VAR model presented in this dissertation was tested for stability for each dataset utilised to ensure accurate inferences.

2.5 Summary

Therefore, the current body of knowledge has not adequately considered the effects of fiscal policy in the years from the trough of the US economy in March 1933 to 1938 using a model of this type. While the deficit moderately widened, government spending increased significantly over the course of the period. As a result, the aim of this paper is to fill the void in the body of knowledge by using a stationary VAR model to answer the problem posed by this dissertation

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Chapter 3: Methodology

3.1 Data

One of the foremost challenges that researchers have faced when studying this period is the formulation of suitable techniques to counter the lack of quarterly data for the period22. This lack of high frequency data has led to many disparate data sets that are often flawed and can lead to misleading conclusions. In order to overcome this research challenge, various interpolation techniques have been used to ascertain quarterly figures for the years in the period under consideration. These techniques have permitted the use of data from very reliable sources that report figures on an annual basis for the interwar period. The datasets used originate from the NBER Macro History database and the Historical Statistics of the United States.

3.2 Method

3.2.1 Interpolation Methods!

Firstly, the spline interpolation method is used to ascertain quarterly data for Government Expenditure, Federal Funds Rate, the Monetary Base and real GDP (1996 $). It generates an approximate cubic function between two data points. For further details on this method, consult Appendix I.

This method provides a suitable approximation to the variables above given the efficient use of a reputable source rather than using regressions based upon other indicators. Trauth and Marwan (2007) highlight the fact that spline interpolation possesses the advantage over other interpolation techniques by preserving high frequency information in the data while also warning of using the technique when there is a steep gradient. In the case of the dataset used, spline interpolation can be considered appropriate to use given that while there are oscillations in the key variables, there are no significant changes akin to the years 1929-1933 and 1937-1941.

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22 See Section 2.4

However, there are some objections from the use of spline interpolated data in VAR models. Whilst splines are extensively used in the natural sciences, the method does have the capacity to inaccurately model the erratic behaviour of time series data. The low frequency information dominates other data thus resulting in non-stationarity. Hence, through interpolation from low frequency behaviour of an economic time series, weight is unnecessarily placed on periodicities near zero, thus, making high frequency interpolation unreliable. This view is applicable for some of the variables in this VAR model such as real Quarterly GDP and may require some form of indicators given that Quarterly GDP has the capacity to be erratic. However, variables such as Government Expenditure should not be affected as much when compared to contemporary standards given that the system of welfare entitlements was in its infancy in this era.

Hence, this dissertation will consider data sets calculated using cubic splines, the Chow Lin interpolation (1970) that Gordon and Krenn (2010) used in their analysis and also the Denton (1971) method of interpolation. Both of these methods use indicators, such as manufacturing indexes, to calculate quarterly figures for the variables under consideration. The indicators used are presented in Appendix I.

The final interpolation technique used is the Denton (1971) method that has not been used in studying this period. The IMF describes this method as ʻstrong and robustʼ. According to Dagum (2006), the Denton method is based on the premise of movement preservation in that the benchmarked series should replicate the movement of the original series. The Denton method minimises a quadratic loss function in the differences between the series to be created and a linear combination of the high frequency data. The Denton method was performed to attain real quarterly GDP using an indicator from the NBER. The indicator is a composite index of leading series that covers23 various factors that provide an accurate quarterly indicator for economic activity in the years 1933-1938, thus, making it a suitable indicator for estimating Real Quarterly GDP24. Also, Government Expenditure was also estimated using this procedure with the same indicator that was used by Gordon and Krenn (2010).

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23 Average work week; manufacturing; manufacturersʼ new orders; durable goods industries; number of new private non-farm dwelling units started; commercial and industrial construction contracts number of new business incorporations; and index of stock prices.

24 See Appendix II

3.2.2 The VAR model

When seeking to ascertain the effects of government spending in the period under consideration, a conventional linear regression was not a suitable model in a dynamic situation. Therefore, a Vector Autoregression was selected as the best measure to generate an accurate depiction of the impact that fiscal policy had on the US economy given that it is one of the foremost techniques to measure the dynamic movements in time series data.

The use of VARs in analysing questions such as the one posed in the dissertation bears several advantages that enable a more definitive answer than previously possible. Stock and Watson (2001) state that VARs capture co-movements in variables that would be left undetected using univariate or bivariate models as witnessed in Romer (1992). In addition, the fact that VAR is relatively free from constraining assumptions enables the VAR model to depict a more realistic picture of complex economic relationships.

The VAR model is based on the premise that the current value of a variable is a result of the value of the variable in the previous period. In this paper, the VAR model takes the following form:yt = c +Φ1yt−1 +Φ2yt−2 + ...+Φ pyt− p + βxt ! ! ! ! ! ! Equation (1)

Where c is the constant vector of (nx1), yt is the vector of endogenous variables, yt-1 is the vector of the lags, Φ is the coefficient vectors, ε is the residual vector, β denotes the

exogenous coefficients and x the exogenous variable. To ensure valid inferences can be accrued from this process, stationarity, indicating the non-presence of a unit root, must be found in the data. Stationarity ensues from the following assumptions:

! E(εt ) = 0 ! ! ! ! ! ! ! ! ! Equation (2)

!E(ε

t

2 ) = σ t2

E(εi ε j ) = 0! ! ! ! ! ! ! ! ! Equation (3)

The model that was found to meet these conditions was one that consisted of real Quarterly GDP, real Government Expenditure and Federal Funds rate as endogenous

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variables along with the exogeneity of the Monetary Base. The Monetary Base is defined to be the total stock of money in an economy at any one time.

While exogeneity seems a rather abstract concept in relation to the VAR model, Engle, Hendry and Richard (1983) exogeneity is not an absolute concept at all; rather it is defined within the context of the model. In statistical theory, when the Monetary Base is considered exogenous, a stable VAR model that meets all of the preconditions for it to be informative is formed.

This variable can be considered to be exogenous in economic thought given that a wide range of factors that can change the monetary base at any time beyond US economic policy. These factors may be completely exogenous to monetary or fiscal policy. For instance, this period was marred by political instability in Europe where investors may have sought refuge in gold held in the US25 which can be considered exogenous to US economic policy. Also, Friedman and Schwarz (1961) highlight the positive effect that the banking holiday imposed by Roosevelt after his inauguration which induced increasing numbers to reduce monetary balances and hold money in banks. The exogeneity of the Monetary Base captures the events during this period that were not determined by the Federal Funds rate or Government Expenditure26.

Furthermore, at each stage of the process, various statistical tests will be employed to ensure that the inferences gained from VAR models using the various datasets are valid. The results of the various hypothesis tests will be presented in the form of two decimal places.

One of the most important tests used in this dissertation is the test of the VARʼs stability. A VARs stability indicates the model is stationary. The stability test calculates the eigenvalues of the companion matrix A. If the modulus of each of eigenvalue is less than one, the VAR model is stable and does not have any unit roots. In this dissertation, the eigenvalues will be shown in either the main body of text or the Appendix I to prove the validity of the models presented.

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25 Romer (1992) highlights the inflow of gold during this period. This may have been one of the causes.

26 It is assumed that the variables perceived as exogenous are weak; allowing for Granger causality between y (endogenous variable) and x (exogenous variable).

In order to determine the amount of lags in the model, the Akaikeʼs Information Criterion, as stated in Akaike (1973), is used in order to ensure the correct number of lags that provide the most informed model is utilised. The AIC measures the discrepancy between the true model and the estimated model. In order for this to be accurate, the AIC needs to be minimised. Also used is the likelihood ratio to select a lag order. The likelihood ratio is based on the null hypothesis that all coefficients of endogenous variables are equal to zero on pth lags. The first value of LR that rejects the null hypothesis is denoted by a ʻ*ʼ and used as the number of lags in the model.

To test for autocorrelation, the Lagrange Multiplier test will be used. The null hypothesis denotes the presence of no autocorrelation in the errors at lag j. This enables correct inference to be made from the derivations of the VAR model. If the errors were autocorrelated, the coefficients would be unbiased but the standard errors would be very large. The results of this test will be presented in tabular form to further validate the model.

An important part of the VAR model is the assumed normality of the errors. The normality assumption forms the basis of many inferences that can be made as result of the VAR model. While the VAR model is not infinite, meaning that normality assumption is somewhat compromised, the model devised in this dissertation possesses remarkable levels of normality in the errors. The Jarque-Bera test uses the null hypothesis that the errors of the VAR model are normally distributed. To test this, the Jarque-Bera test, specified in Jarque and Bera (1987), as well as the skewness and kurtosis test will be used to test the normality assumption of the VAR model for each respective dataset.

In order to test some semblance of causality, the Granger causality test will be applied to the VAR model. Granger (1969) and Sims (1972) define causality as a scenario where a variable, xt, has an explanatory power on another variable, yt in a regression of a variable yt on lagged values of yt and xt. A useful example of this concept is provided in Appendix I. Therefore, in this context, Government Expenditure Granger causes Output if Government Expenditure helps to forecast Output, given past Output. However, one should note that other variables impact upon Output so therefore Granger Causality does not imply causality.

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In addition to the standard VAR analysis, impulse response functions will be under consideration as well. Impulse response functions enable the analysis of the effect of one variable upon another variable. More specifically, Orthogonalised Impulse Response Functions27 will be used as a means to estimate the response from an impulse of one variable whilst holding everything else constant. The OIRFs are based on the premise that shocks are orthogonal. As Cochrane (1997) notes, if the effect of government spending on GDP is to be examined then the Government Expenditure shock should not be correlated with the GDP shock otherwise the response of GDP could result from Government Expenditure or technology shocks that coincide with said Government Expenditure shock. Given that the covariance matrix of errors is often non-diagonal, the Cholesky decomposition will be used in order to transform this matrix to measure the response of one variable from an impulse in another, whilst holding other variables fixed. Different orderings, that will be informed by economic theory as advocated by Cochrane (1997), in endogenous variables will be considered to measure robustness as well as form inferences.

These impulse response functions will consider the model using the raw data, first differenced data and Hodrick-Prescott filtered data. The latter two filtering methods seek to de-trend the data. The Hodrick Prescott filter ascertains a smooth, non-linear representation of the time series where long-term behaviour has a far greater sensitivity when compared with short term fluctuations.

However, whilst there are many benefits from using VAR models, there are some detractors to the VAR methodology on either a purely theoretical standpoint or on the wider results that are produced. Summers (1991) contests the validity of the findings in econometric type papers and asserts that they do not add to economic knowledge. While many papers can be too scientific and lose sight of their hypothesis, this dissertation will seek to strike the right balance between additions to the knowledge of economics and economic history whilst also maintaining a scientific approach to ensure valid results.

Finally, Chari, Kehoe and McGratton (2008) have criticised the VAR process and more specifically SVARs in the lack of use of a priori data that would help inform decisions about how many lags to use within a model. To address this issue, this dissertation will seek to

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27 Henceforth known as OIRF

test the number of lags suitable given the number of observations. The finding that three lags were suitable seems a reasonable time frame for the effects of both monetary and fiscal policy to feed through to gross domestic product (1996 $) rather than it depend on on a model that uses only one lag akin to Romer (1992)28 which does not seem to be viable given the said articleʼs criticism.

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28 See Section 2.4

Chapter 4: Results

4.1 Raw Data

Firstly, the raw data indicates significant fluctuations in the period under consideration in all the key economic indicators for the domestic economy. These key indicators are first differenced to remove any trend from the data set. The indicators provide a preliminary idea of the expectations of US consumers and policy makers for the performance of the US economy over the course of the years 1933-1938.

The output of the US economy during this period experienced many fluctuations during this period, which is shown in Figure (4.0).

-10

010

2030

Firs

t Diff

eren

ced

Real

Qua

rterly

GDP

1932q3 1934q1 1935q3 1937q1 1938q3Quarter

!! ! Figure (4.0): First Differenced Real Quarterly GDP

Real Government Expenditure in 1996 dollars indicate that there is a correlation with a slight lag with real output29. This is highlighted by Figure (4.1) where a rise in Government Expenditures takes place before a larger rise in output. In the period from 1936-1937, a fall in Government Gxpenditure precedes a larger fall in real output. During this era the objectives for government spending evolved. Duesenbury (2006) identifies the different objectives of fiscal policy. During this era public good provision, the redistribution of income to the neediest and macroeconomic stabilisation took greater precedence than

23

29 Results for real consumption presented in Appendix I

ever before. While all government programs can be considered transfer programs, programs that were explicitly designed to transfer wealth were a New Deal innovation according to Anderson and Tollison (1991).

Figure (4.1): The first differences of real government expenditures between 1933-1938.

Fundamental to the analysis of recovery in the Great Depression posited by monetarists, is the money supply. The monetary base30 is defined to be the total monetary stock in the economy consisting of any form money from bank notes to bank deposits. The data used in this dissertation is from Gordon (1989) on the business cycle and is depicted in Figure (4.2). The results of the raw data indicate the fluctuations in money supply over the course of the period. The importance of money supply is emphasised in basic economic models 31and one can tell that recovery coincides with periods of positive growth in the money stock and periods of contraction when it is negative. Friedman and Schwarz (1963) believe that the sharp rise in the money stock in the period between June 1933 to June 1936 can be attributed to a substantial inflow of gold produced by the revaluation of gold and not by business expansion as that could only be achieved if the banks held less in reserves

-4-2

02

46

Firs

t diff

eren

ce R

eal G

over

nmen

t Exp

endi

ture

( 19

96)

1933q1 1934q3 1936q1 1937q3 1939q1Time

24

30 A variable used in the VAR model

31 For example, the Real Business Cycle.

which was not the case. In Figure (4.2) there is a positive, oscillating, growth in the money supply from 1933 to 1937. From 1937 onwards, there is a clear decline in the money supply resulting in actual contraction in the third quarter of 1937.

Figure (4.2): First differences of the monetary base in the period from 1933-1938.

Finally, the Federal Funds rate is considered in the VAR model. To form a reliable estimate for this variable, a spline interpolation has been used on the average annual rate calculated from the Historical Statistics of the United States dataset. This was due to the format of the dataset which gave yearly averages for the federal funds rate. The Federal Funds rate is the interest rate depository institutions such as banks lend to other institutions at the Federal Reserve. It provides a key indicator for not the only the health of the financial institutions but also the prospects in the economy as well32. The first differences in the federal funds rate are shown in Figure (4.3). A key inference gained from this graph is that the magnitude of declines are far greater than the inclines in the Federal Funds rate over the course of the period. This indicates that the banking system had access to cheaper funds at various times during this period which coincide with higher investment rates. The low interest rates also highlight some semblance of stability in the banking system given the relatively low federal funds rate that helped to sustain flows of

-.20

.2.4

.6Fi

rst D

iffer

ence

s of

Mon

etar

y Ba

se

1933q1 1934q3 1936q1 1937q3 1939q1Time

25

32 In terms of providing loans to businesses or servicing customer bank accounts

money throughout the system after it was saved from meltdown. However, according to Blanchard and Fischer (1989) interest rates alone do not adequately reflect the links between financial markets and the rest of the economy. Therefore, a monetary variable is used in the VAR model as well.

Figure (4.3): First differences in the Federal Funds rate in terms of percentages.

In sum, the indicators suggest that the VAR model provides a suitable method to assess the impact of fiscal policy. A VAR model that is extensively tested, using some of the above variables, can provide meaningful results and answer the question posed in this dissertation.

4.2 Spline Interpolation

The VAR model that will be considered in this dissertation consists of real quarterly GDP (1996 $), the quarterly Federal Funds rate; real quarterly Government Expenditure; and an exogenous Monetary Base with three lags. This model was found to meet all of the preconditions necessary for it to be considered informative (shown in Tables 4.1, 4.2 and Figure 4.4).

-.3-.2

-.10

.1Fi

rst d

iffer

ence

s in

the

Fede

ral F

unds

Rat

e (%

)

1933q1 1934q3 1936q1 1937q3 1939q1Time

26

As Table (4.1) shows, all eigenvalues have a modulus of less than one indicating that the time series under consideration is stationary. Stationarity is enshrined by Equation (2) and Equation (3) and is thus vitally important for inferences made from the VAR model. Table (4.2) indicates that a VAR with 3 lags provides the most informative model given the data set.

The normality of the errors were considered using the Jarque-Bera test. This uses a null hypothesis that all errors are normally distributed. As shown in Table (4.3), the p value exceeds 10% implying that we cannot reject the notion that the errors are normally distributed.

Eigenvalue Modulus

0.69+0.69i 0.97

0.69-0.69i 0.97

0.92+0.24i 0.95

0.92-0.24i 0.95

0.80+0.39i 0.89

0.80-0.39i 0.89

0.03+0.44i 0.44

0.03-0.44i 0.44

-0.36 0.36

Table (4.1): All eigenvalues lie in the unit circle implying stability.

Lag LR AIC

1 125.38 8.70

2 194.84 -0.15

3 29.16 -0.70

Table (4.2): Lag selection criteria tests that the VAR model has undergone. Three lags provide the best model for this sample.

27

-1-.5

0.5

1Im

agin

ary

-1 -.5 0 .5 1Real

Roots of the companion matrix

Figure (4.4): All eigenvalues lie inside the unit circle.

Equation chi2 Degrees of Freedom P>chi2

Quarterly GDP 0.96 2 0.62

Federal Funds 0.65 2 0.72

Government Expenditure

2.51 2 0.29

All 4.11 6 0.66

Table (4.3): Results of the Jarque-Bera that imply normality in the errors.

The level of autocorrelation in the errors were then tested. While the presence of autocorrelation does not lead to unbiased estimates of coefficients, it does pronounce their significance. Hence, to check the presence of autocorrelation, a Lagrange-multiplier test has been used with a null hypothesis that there is no autocorrelation. As depicted in Table (4.4), the null hypothesis cannot be rejected at a 10% level of significance for an error equation with a lag of 1; cannot be rejected at a 5% level of significance for a lag of 2; and cannot be rejected at a 10% level of significance for a lag of 3.

28

Lag Chi2 Degrees of Freedom

P>Chi2

1 9.76 9 0.37

2 14.89 9 0.09

3 2.81 9 0.97

Table (4.4): Lagrange Multiplier test indicating that autocorrelation is not present at a significant level.

In Table A1, the results and coefficients of the VAR model are presented. The variables used in this VAR model are of economic significance and grounded in economic theory. The preceding tests have been utilised to establish the correct specification of the model in order to form accurate inferences on the basis of the IRFs33.

One of the strongest tests that can be used with a VAR model is the Granger causality test. The null hypothesis of this test is defined to be that one time series variable does not cause another. The results of this test are shown in Table (4.5).

Equation Excluded Variable

Chi2 Degrees of Freedom

P>chi2

Real Quarterly GDP

Fed Funds 49.295 3 0

Government Spending

9.4816 3 0.024

All 70.402 6 0

Table (4.5): The Granger Causality Tests performed on the sample.

One of the most significant results from the Granger Causality test is that when analysing the real quarterly GDP data for the sample period, the federal funds rate has a P-value equal to 0 when the Federal Funds rate is excluded. This strong rejects the null hypothesis that Federal Funds rate does not affect real quarterly GDP. A Granger Causality test is appropriate given the stationary conditions of the time series are met. Whereas, for quarterly government spending, the null hypothesis cannot be rejected. This implies that

29

33 See Section 3.2.2

notion that real Government Expenditure does not Granger cause real Quarterly GDP cannot be rejected. This indicates the relative importance of monetary policy to the recovery of the US economy.

An important facet to consider when analysing the VAR model in this dissertation are the impulse response functions34. Figure (4.5) details the effect of a unit change on government expenditure on real quarterly GDP using OIRFs with different orderings (all of which are significant in the 95% significance region)35

Figure (4.5): Effect of a unit change in real government spending on real quarterly GDP.

On the evidence of Figure (4.5), the results of the OIRF are very sensitive to the way in which the endogenous variables are ordered. When variables are ordered either using Order5 or Order636, there is a slight difference in terms of effects on Quarterly GDP when there is a unit shock in government spending. However, when compared with order7, there is a significant drop in real quarterly GDP as a result of a unit shock in government spending. Figure (4.6) shows the overlaid graph when order7 is not considered.

-30

-20

-10

010

0 2 4 6 8step

order5: oirf of G_Exp -> rQGDPorder6: oirf of G_Exp -> rQGDPorder7: oirf of G_Exp -> rQGDP

Impulse (Government Spending) Response (Real Quarterly GDP)

30

34 See Section 3.2.2

35 See Appendix II

36 When policy variables are at the beginning of the ordering, it provides more robust results

Figure (4.6): Impulse responses using only orders 5 and 6

Figure (4.6) clearly shows the different effects of fiscal policy with order5 highlighting government spendingʼs capacity to have a negative effect on the economy and lead to a contraction. Whereas, when using an an OIRF with order6, then in the long-run it has the capacity to be moderately beneficial to the economy in the short and long run.

-1-.5

0.5

0 2 4 6 8step

order5: oirf of G_Exp -> rQGDPorder6: oirf of G_Exp -> rQGDP

Impulse (Government Spending) Response(Real Quarterly GDP)

31

With regards to the Federal Funds rate, Figure (4.7) depicts a one time increase in the federal funds rate while holding the other endogenous variables constant. As in the case of government spending, there are sensitivities to the same orderings. Order5 and Order6 produce a collinear impulse response function that indicates there is a moderate decrease in real quarterly GDP in response to a unit shock in the Federal Funds rate that lasts after eight steps.

Figure (4.7): Impact of a unit shock increase in the Federal Funds rate on real quarterly GDP.

Once again, order7 has produced results that, while being significant at the 95% significance level, differ substantially from the other orderings. Therefore, excluding order7, the remaining orderings are graphed. The orderings are collinear and indicate the moderate damage that contractionary monetary policy caused on the recovery from the Great Depression in Figure (4.8).

-1-.5

0.5

1

0 2 4 6 8step

order5: oirf of Fed_Funds -> rQGDPorder6: oirf of Fed_Funds -> rQGDPorder7: oirf of Fed_Funds -> rQGDP

Impulse (Federal Funds); Response (Real Quarterly GDP)

32

Figure (4.8): Impact of a unit shock in the federal funds rate on real quarterly GDP.

Therefore, the results from the VAR model indicate that both the Federal Funds rate and Government Expenditure had different effects on real quarterly GDP in the period 1933-1938. An increase in government spending was found to decrease real quarterly GDP in the short term as a result of the IRFs using both logical orderings. Nevertheless, the long-term effect differed between moderately beneficial to moderately damaging to the recovery. Whereas, a rise in the Federal Funds rate was found to be moderately damaging to the recovery of the economy in both orders. Both eventually taper off to zero. This analysis provides cautious support for the findings of the monetarist school in which the recovery from the Great Depression was dependent upon monetary policy.

To further investigate the results of the VAR model using the spline interpolated dataset, the first differences of the endogenous and exogenous variables were taken. This provides a filter on the on the data that seeks to eradicate any trend in the variables of the model. It was found using the VAR model specified in this paper, that the model is stable and it can be assumed that the normality of errors hold. A hypothesis of no autocorrelation can be rejected37.

-.6-.4

-.20

0 2 4 6 8step

order5: oirf of Fed_Funds -> rQGDPorder6: oirf of Fed_Funds -> rQGDP

Impulse (Federal Funds Rate) Response(Real Quarterly GDP)

33

37 For the results, refer to Appendix I

The impulse response functions from this dataset provide some interesting results. In Figure (4.9) the orgonalised impulse response function with an impulse of the first difference in Government Expenditure with a response of the first difference in real Quarterly GDP estimated using a spline interpolation is depicted.

Figure (4.9): Unit shock in the first differences of Government Expenditure on Real Quarterly GDP.

Figure (4.9) shows the substantial negative impact arising from fiscal policies when order9 is used. When the data is filtered using first differences, a positive change in government spending is found to proliferate real quarterly GDP in the short-term but in the long term is rather damaging.

Whereas, when order8 and the Third order are considered, depicted in Figure (4.10), in the long-term there is no effect from a unit shock in government spending. When order8 is used, there is a small positive effect in the short term indicating its stimulative effect but in both cases, using raw and filtered data, there is ambiguity in the effects of Government Expenditure on the US economy.

-50

050

0 2 4 6 8step

order8: oirf of D.G_Exp -> D.rQGDPorder9: oirf of D.G_Exp -> D.rQGDPThird: oirf of D.G_Exp -> D.rQGDP

Impulse(First Difference in Government Spending) Response(First Difference in real quarterly GDP)

34

Figure (4.10): Unit shock in real government spending to real quarterly GDP.

When analysing the first differences of the Federal Funds rate as an impulse, the results highlighted the sensitivity of the dataset to the ordering of variables. Figure (4.11) depicts this situation, whereby there is a stark contrast in the results of other orders when using order8. Whereas, with order9 and the Third order, there are different trends in the response to the impulse.

-1-.5

0.5

1

0 2 4 6 8step

order8: oirf of D.G_Exp -> D.rQGDPThird: oirf of D.G_Exp -> D.rQGDP

Impulse(First Difference in Government Spending) Response(First difference in Real Quarterly GDP)

35

Figure (4.11): OIRFs for the first difference in real quarterly GDP with the First differences in Federal Funds rates as an impulse.

Disregarding the obvious sign problem with ordering 8, order 9 and the Third order provide some interesting results in Figure (4.11). The Third order states that when there is a unit shock in the Federal Funds rate there is a moderate contraction in economic activity. This contraction declines over time but in the long-term, there is a negative impact on growth in real quarterly GDP and agreeing with the results in Figure (4.8).

Whereas, order9 states that there is a sudden decline in economic activity at the point when interest rates grow but economic activity sufficiently recovers in the long term after eight steps. When the first difference filtering is applied to the data it highlights the main of the disadvantage with the impulse response functions, the sensitivity in the ordering of endogenous variables. However, some orderings can be excluded given that the majority of the other orderings provide reasonably robust results but differ moderately in magnitudes.

Therefore, when the filter is applied, the effect of government spending is ambiguous in the short term but in the long term a unit shock does not have any affect on real quarterly GDP. The effect of the Federal Funds rate is also moderate and any rise in this rate can be

-.50

.51

0 2 4 6 8step

order8: oirf of D.Fed_Funds -> D.rQGDPorder9: oirf of D.Fed_Funds -> D.rQGDPThird: oirf of D.Fed_Funds -> D.rQGDP

Impulse (First difference of Federal Funds Rate) Response (First Difference Real Quarterly GDP)

36

damaging to economic activity. This broadly confirms the results witnessed in Ritschl and Pooyan (2009).

4.3 Chow Lin Interpolation

One of the key differences with the method of this article and Gordon and Krenn (2010) is the way in which the datasets used in the respective VAR models were interpolated. Gordon and Krenn (2010) used a Chow Lin (1970) interpolation technique. The contrasts in the results are quite stark. The same VAR model specified in this dissertation was applied to Gordon and Krennʼs (2010) data with a slight change in variables owing to the different base years used to calculate real variables as well as the Federal Funds rate being replaced with the New York Federal Discount Rate. The dataset, despite the critique of the validity of their results38, will be used in the context of the model presented in this dissertation to estimate the effects of government spending.

When the same VAR model was applied to this other dataset, it produced a stable VAR from which inferences could be formulated. This is shown in Table (4.6) and Figure (4.12) where all eigenvalues of the companion matrix have a modulus of less than 1.

Eigenvalue Modulus

0.86 + 0.25i 0.89

0.86 - 0.25i 0.89

-0.45 + 0.65i 0.79

-0.45 - 0.65i 0.79

0.58 + 0.34i 0.67

0.58 - 0.34i 0.67

-0.54 0.54

-0.12 + 0.33i 0.33

-0.12 - 0.33i 0.33

Table (4.6): Eigenvalues lie inside the unit circle.

37

38 See Section 2.4

Figure (4.12): Eigenvalues lie inside the unit circle.

In addition, the model is correctly specified with three lags according to the Likelihood Ratios and the AIC. The null hypothesis that there is no autocorrelation cannot be rejected at a 10% significance level whilst normality in the errors can be safely assumed. These results are shown in Tables (4.7), (4.8) and (4.9).

Lag Likelihood Ratio AIC

0 - 8.19

1 141.70 2.01

2 21.50 1.83

3 39.30* 0.77*

Table (4.7): Selection order criteria for the VAR model. Indicating that three lags are appropriate.

-1-.5

0.5

1Im

agin

ary

-1 -.5 0 .5 1Real

Roots of the companion matrix

38

Lag Chi2 DF P>Chi2

1 13.84 9 0.13

2 15.01 9 0.11

3 7.65 9 0.57

Table (4.8): Null hypothesis of no autocorrelation cannot be rejected at 10% significance.

Jarque-Bera Test

Equation Chi2 P>chi2

Real Quarterly GDP (Gordon)

0.04 0.98

NY Federal Discount Rate 0.93 0.63

Real Government Spending 2.23 0.33

All 3.19 0.78

Skewness Test

Equation Skewness Chi2 P>Chi2

Real Quarterly GDP (Gordon)

0.11 0.04 0.85

NY Federal Discount Rate

-0.17 0.10 0.76

Real Government Spending

0.82 2.22 0.14

All 2.36 0.50

Kurtosis Test

Equation Kurtosis Chi2 P>Chi2

Real Quarterly GDP (Gordon)

2.98 0 0.99

NY Fed Discount 2.00 0.83 0.36

Real Government Expenditure

2.91 0.01 0.93

All 0.84 0.84

Table (4.9): Results of the normality tests.

39

The contrasts are formed when analysing the impulse response functions. The impulses under consideration in this section are unit shocks to Government Expenditure and the New York Federal Discount rate.

Firstly, unit shocks to government spending created a vastly different response when when compared with spline interpolation technique. As witnessed in the spline interpolation section of this chapter, the orderings of the endogenous variables are crucial. Therefore, three different orderings will be considered in conjunction with the raw data and filtered data using first differences. Real Government Expenditure is treated as an impulse in Figure (4.13).

Figure (4.13): Effect of a unit shock in government expenditure using the OIRF.

Figure (4.13) differs markedly from Figure (4.6) in the sense that there is a very small decrease in economic activity according to the impulse factor until step 1 but a moderate enhancement in economic activity after step 1 according to the Second ordering and order11. Again, order 12 highlights the inherent disadvantage to the IRFs where results are dependent upon the the orderings of the endogenous variables. However, the results indicate there is a moderate positive impact from government spending and the magnitude of the differences between order11 and order12 is far smaller.

0.2

.4.6

.8

0 2 4 6 8step

Second: oirf of RealGovExp -> rqgdpgororder11: oirf of RealGovExp -> rqgdpgororder12: oirf of RealGovExp -> rqgdpgor

Impulse(Real Government Expenditure Gor) Response(Real Quarterly GDP Gor)

40

In relation to the New York Federal Discount rate, again there are differences in relation to the magnitude of the response from the impulse the when various orderings are used. This is shown in Figure (4.14)

Figure (4.14): OIRF for the New York Federal Discount Rate.

The results presented in Figure (4.14) contrast significantly with the previous findings, in the sense that a rise in interest rates had a moderately beneficial impact upon the recovery. There seems to be a sign problem with this data as it evidently the contradicts economic rationale relating to interest rates

Finally, the Granger Causality tests show that the New York Federal Discount rate and Real Government Expenditure and their lagged values have an affect on real quarterly GDP. The results are shown in Table (4.10). The null hypothesis, that the New York Federal Discount Rate does not Granger Cause real Quarterly GDP, can be rejected outright. Whereas, the null hypothesis where Real Government spending does not Granger-Cause real Quarterly GDP cannot be rejected.

0.2

.4.6

.8

0 2 4 6 8step

Second: oirf of NYFedDiscount -> rqgdpgororder11: oirf of NYFedDiscount -> rqgdpgororder12: oirf of NYFedDiscount -> rqgdpgor

Impulse(NY Federal Discount Rate) Response(Real Quarterly GDP Gor)

41

Equation Excluded Chi2 P>Chi2

Real Quarterly GDP (Gordon)

NY Federal Discount rate

19.66 0

Real Government Expenditure

4.85 0.183

All 26.10 0

Table (4.10): Results for the Granger test performed on the Gordon and Krenn (2010) dataset on the model outlined above.

Additionally, Gordon and Krennʼs (2010) data was then filtered to examine the effects of both fiscal policy and monetary policy using the above model. The first difference method will be used to further scrutinise this period, while also ensuring the VAR model specified in this dissertation remains stable and the assumptions are valid.

When performing the initial VAR analysis, it was found that simply differencing both endogenous and exogenous variables did not produce a stationary VAR model given that one of the eigenvalues of the composition matrix lied outside the unit circle. Therefore, differencing only the endogenous variables was attempted and it was found that a stable VAR resulted. Subsequent tests found that there is no autocorrelation in the errors and the assumption of normality in the errors hold39.

Subsequently, the OIRFs were found in relation to this model. Again, the orderings of the endogenous variables are extremely important and thus the same three orders will be considered as above but instead using first differences on each of the variables.

Firstly, the OIRFs is shown with an impulse of Real Government spending and a response in real quarterly GDP using three orderings in Figure (4.15).

42

39 See Appendix II for results

Figure (4.15): Orthogonal impulse response function for the first difference in real government expenditure.

This provides an insight into the response of real quarterly GDP to a positive difference in real government expenditure using the Chow Lin interpolation method. It shows that using order16, there is a positive shock on output that decreases towards zero. In the long-term, there is a moderate benefit to a unit change in government spending. Yet, both order15 and order14 show only a moderate effect from a unit shock in the first difference of government spending. A shock that even goes below zero if the endogenous variables are ordered in the way of order15. However, in the short term thereʼs a very positive impact from Government Expenditure.

Finally, the response of quarterly GDP to a difference in the New York Federal Discount rate is considered under the same framework. Figure (4.16) shows the OIRFs for the New York Federal Discount rate.

0.2

.4.6

.81

0 2 4 6 8step

order14: oirf of D.RealGovExp -> D.rqgdpgororder15: oirf of D.RealGovExp -> D.rqgdpgororder16: oirf of D.RealGovExp -> D.rqgdpgor

Impulse(First differences in Real Government Spending) Response(First differences in real quarterly GDP)

43

Figure (4.16): OIRFs for a unit increase in the New York Federal Discount Rate.

Figure (4.16) presents a sign problem that counters economic rationale. It states that when there is a unit increase in the discount rate, it provides a positive shock for real quarterly GDP that decreases below zero after step 4 and slowly climbs above zero by step 8. This evidently contrasts with the monetarist interpretation of the recovery and evidently it is a result that is not anticipated given that a rise in either interest or discount rates leads to a fall in economic activity especially in the context of a ʻrecoveryʼ.

4.4 Denton Interpolation

Finally, the results from the Denton interpolation method for the above VAR model. Similar tests were applied to the VAR model using the Denton interpolation technique and it was found that the model was stable; the errors can be safely assumed to conform to a normal distribution; and the errors were not found to be autocorrelated. The results of these tests are presented in Appendix II. The model consists of real quarterly Government Expenditure; real quarterly GDP (1996 $); the Monetary Base; and the New York Federal Discount Rate.

-.10

.1.2

.3.4

0 2 4 6 8step

order14: oirf of D.NYFedDiscount -> D.rqgdpgororder15: oirf of D.NYFedDiscount -> D.rqgdpgororder16: oirf of D.NYFedDiscount -> D.rqgdpgor

Impulse (First Difference in NY Fed Discount Rate) Response(First difference in Real Quarterly GDP)

44

The impulse response functions were calculated for the new dataset. Firstly, real Government Expenditure was considered as an impulse in Figure (4.17)

Figure (4.17): Impulse response functions for Government Expenditure on Real Quarterly GDP.

The results presented in Figure (4.17) starkly contrast with other interpolation techniques. Firstly, the magnitudes in the impulse factors are far greater under the Denton interpolation method. For each ordering there is an initial positive impact from a unit shock to government expenditure. Then there is a gradual decline after eight steps for order17 and order18, that is below zero indicating a moderate negative impact from fiscal policy in the long-run. This conforms to economic rationale outlined above by Friedman (2006). According to order24, there is an extremely moderate effect from fiscal policy according to figure (4.18).

-20

24

68

0 2 4 6 8step

order17: oirf of rgovexpden -> qgdpdentonorder18: oirf of rgovexpden -> qgdpdentonorder24: oirf of rgovexpden -> qgdpdenton

Impulse (Real Gov Exp) Response(Quarterly GDP Denton)

45

Figure (4.18): The moderate effect of fiscal policy on the recovery.

The New York Federal Discount rate was then studied using impulse response functions. In Figure (4.19), the response from a unit shock in the New York Federal Funds rate is shown.

Figure (4.19) shows the effect from a unit shock in the New York Federal Discount Rate.

-.004

-.002

0.0

02oi

rf

0 2 4 6 8step

Impulse (Real Government Spending) Response(Real Quarterly GDP)

-20

24

0 2 4 6 8step

order17: oirf of NYFedDiscount -> qgdpdentonorder18: oirf of NYFedDiscount -> qgdpdentonorder24: oirf of NYFedDiscount -> qgdpdenton

Impulse (NYFederal Discount) Response (Quarterly GDP)

46

The results show that there is an initial sign problem that contradicts economic rationale when analysing order17 and order18. However, after the fifth step there is a negative impact from a rise in the New York Federal Discount rate that is substantial and had the capacity to harm the economy significantly, thus, confirming the monetarist interpretation on the recovery from the Great Depression for order17. Whereas, order18 and order24 taper off to zero after eight steps.

To eradicate any semblance of a trend in the data, the first differences of each of the variables in the model are to be analysed. This is more suitable to the VAR model given that when this test is applied, the p values for autocorrelation become larger. Again, the model is stable and the normality in the errors can be safely assumed40.

The impulse response functions provide some confirmation of the results in previous methods. The first impulse under consideration is a rise in real quarterly government spending as depicted in Figure (4.20).

!

Figure (4.20): Impulse response function for the first difference in real government expenditure.

-4-2

02

4

0 2 4 6 8step

order20: oirf of D.rgovexpden -> D.qgdpdentonorder21: oirf of D.rgovexpden -> D.qgdpdentonorder25: oirf of D.rgovexpden -> D.qgdpdenton

Impulse (First Difference in Real Government Exp) Response(First difference in quarterly GDP)

47

40 See results in Appendix II

The IRFs in Figure (4.20) show some semblance of similarity in their results. In the long run, it states that there is a moderate net benefit from fiscal policy for all orderings (a greater benefit than in previous methods). In the medium term, each ordering shows that there is a negative effect to fiscal policy.

The IRFs in Figure (4.21) consider the impact on a unit shock in the New York Discount rate. In this case there are sharp contrasts in the results after the third step highlighting the inherent disadvantage in impulse response functions. Even so, it is clear that there seems to be a sign problem with this dataset. The only reasonable rationale for the results below is that individuals who save receive higher interest rates in the short term. Higher interest rates also mean an escalation in the costs to invest which naturally damage a recovery. However, in the long-run, the effects are negligible.

Figure (4.21): Effect on the first difference of quarterly GDP from a unit change in the New York Federal Discount Rate.

Therefore, using a Denton interpolation technique, the effects of both fiscal and monetary policy are ambiguous given the raw and filtered datasets.

-.50

.51

1.5

0 2 4 6 8step

order20: oirf of D.NYFedDiscount -> D.qgdpdentonorder21: oirf of D.NYFedDiscount -> D.qgdpdentonorder25: oirf of D.NYFedDiscount -> D.qgdpdenton

Impulse(First difference in NYFed Discount) Response(First difference in Quarterly GDP)

48

4.5 Hodrick Prescott Filter

For each dataset, the Hodrick Prescott filter was applied using the VAR model outlined in this dissertation that met all of the assumptions necessary for inferences with the other datasets presented in this dissertation. The result was colinearity between the exogenous variable and the dependent variables. Therefore, a VAR model could not be ascertained in this instance.

Accordingly, the VAR model was then revised in various ways41 to no avail. The resultant VAR models, using this method of filtering, violated the stability condition42.

49

41 Such as making the Monetary Base variable endogenous.

42 See Section 5.4 for comment.

Chapter 5: Conclusion

5.1 Evaluation

This dissertation has sought to establish the effectives of fiscal policy using a VAR model that has been rarely applied to the years 1933 to 193843. In Chapter 2, the existing body of literature either dismissed fiscal policy as an entity in the US economy during the aforementioned period or used questionable methods and assumptions that biased the results44. This dissertation has sought to fill this void.

In order to evaluate the effectiveness of fiscal policy, this dissertation has used quarterly data as a means to establish a VAR model that would provide an accurate answer to the research problem posed in section 1.2. Various interpolation methods were used in this article ranging from cubic splines to the Denton method, that have not been used before in this context, to ascertain a quarterly dataset that overcame the significant pitfalls from using annualised data.

5.2 Conclusions on the Research Aim

It was found that there was cautious agreement between the orderings of endogenous variables within interpolation techniques45. The results of the IRFs indicate their robustness when policy variables are at the start of any combination. However, there was no broad consensus across techniques.

When using the spline interpolation technique46, the effects of fiscal policy effects ambiguous but moderate. Contractionary monetary policy witnessed in this period was found to have a moderately negative impact on the US recovery.

50

43 See Section 1.2

44 See Section 2.4 and 3.2

45 See Section 4.2, 4.3 and 4.4

46 See Section 4.2

When using the Gordon and Krenn (2010) dataset47, it was found that government spending again had a net positive benefit to the recovery of the US economy. Whereas, the results for monetary policy were inconsistent with economic rationale and an obvious sign problem arises.

When using the Denton method of interpolation48, there are clear ambiguities in the results from the normal VAR model and the first differenced VAR model but it did highlight the capacity of fiscal policy to be harmful to the recovery of the US economy.

Across each measure, monetary policy was found to Granger Cause output indicating its greater importance to the recovery from the Great Depression over fiscal policy.

Therefore, the success of fiscal policy is inconclusive in the recovery of the US economy from the Great Depression and any effect it had would have been moderate at best. Thus, it would be hazardous to suggest any concrete multiplier for fiscal policy when part of this VAR model.

5.3 Limitations The explanation for these said findings may lie in the consequences of other New Deal policies on the variables used in this type of analysis. For instance, in Section 2.3, Cole and Ohanian (2006) referred to the damaging effects of the competition policies implemented by Roosevelt. While this may not be ostensible in the Government Spending variable, it has the capacity to affect the output variable. Thus, fiscal policyʼs positive impact may have been overshadowed by destructive regulation and this type of analysis49 has been unable to detect this force.

5.4 Suggestions for Future Research

To further this analysis, restrictions could be placed on the VAR model utilised in this research. For instance, the Structural VAR model specified in Blanchard and Perrotti

51

47 See Section 4.3

48 See Section 4.4

49 VARs as opposed to General Equilibrium analysis.

(2002) which imposes restrictions relating to economic theory. Also, an expansion of the VAR model needs to be considered to encapsulate more variables that affect output. To improve the Hodrick Prescott filter results, it may be appropriate to employ a similar technique advocated by Uhlig and Ravn (2002) to account for higher frequency observations.

Finally, the US is an extremely large and diverse country. Thus, the recovery from the Great Depression would have differed between areas depending upon the industrial structure; level of educational attainment and dependency upon export markets. Therefore, a rather interesting subject would be to analyse the effects of federal spending on local economies in various types of states to provide an in-depth investigation on the effects of fiscal policy at both a national and regional level.

52

AppendicesAppendix IExplanation of spline interpolation

If there are n data points, representing n years, then there are n-1 spaces. The cubic spline interpolation seeks to generate a piecewise cubic function that connects these two spaces. To generate a smooth function, the assumption that the first and second derivatives are both equal to zero at the last polynomial. This assumption has been made in order to exact greater efficiency from the spline method in estimating quarterly figures. According to Ueberhuber (1997) spline functions with a polynomial degree of 3 are an acceptable compromise with respect to differentiability, damping of perturbations and the computational effort required to calculate a degree higher than 3.

This results in a (4n-4)x(4n-4) linear system that can be solved for the coefficients of the n-1 cubic polynomials. Once the polynomial functions are attained, values representing quarters over the period can be matched with quarterly estimates for real output, government spending, and the average Federal Funds rate over the course of a quarter.

Example of Granger Causality

“Consider an economist who windsurfs. Windsurfing is a tiring activity, so he drinks a beer afterwards. With W = windsurfing and B = drink a beer. Here we have no difficulty determining that windsurfing causes beer consumption.

But now suppose that it takes 23 hours for our economist to recover enough to even open a beer, and furthermore let�ʼs suppose that he is lucky enough to live somewhere (unlike

Chicago) where he can windsurf every day. The �“cause precedes effects�” rule would

lead you to believe that drinking beer causes one to windsurf!How can one sort this out? The problem is that both B and W are regular events. If one could find an unexpected W, and see whether an unexpected B follows it, one could determine that W causes B.

So here is a possible definition: if an unexpected W forecasts B then we know that W “causes� B. This will turn out to be one of several equivalent definitions of Granger

causality.” - Cochrane (1997)

53

Appendix II

Consumption

Real Consumption (measured in 1996 US dollars) shows significant variation over the period. This is best depicted by a graph of the first differences in each quarter measured as shown in Figure (A1).

The quarterly estimates of real consumption indicate that after a significant decline in the period 1929-1933, consumption tends to rise over the years of 1933-1938. Analysing consumption also provides an insight into the expectations of Americans during this era. Deaton (1992) describes consumption as the decision to spend money in the present period rather than saving for future consumption or some unspecified contingency. Evidently, there is a fall in consumption growth after 1937. This can be attributed to the institutional changes that were taking place in the US at that time. Monetarists50 ascribe that the 1937-38 recession was a result of a tightening of the monetary stock by the Federal Reserve, whereas, Keynesians blame increasing taxation and a fall in Federal spending. Crafts and Fearon (2010) assert that the recession of 1937 to 1938 is attributable to deflationary monetary and fiscal policy.

Figure (A1): First differences of real consumption in 1996 dollars over the period

-10

010

20Fi

rst d

iffer

ence

of R

eal C

onsu

mpt

ion

in b

illion

s ($

1996

)

1933q1 1934q3 1936q1 1937q3 1939q1Time

54

50 Such as Milton Friedman.

Investment

During the era under consideration, there is a positive rate of growth in real investment over the period until the fourth quarter of 1937. In the second quarter of 1937, the growth in real investment reaches its peak. However, after this peak there is a dramatic decrease in the rate of growth in investment and after 1938, there is a contraction in real investment. This is depicted in figure (A2). It seems to be the case that the rate of growth in government spending matches that of real investment with a significant multiplier for fiscal policy. However, investment decisions in macro economic theory are made on the basis of marginal product of capital and the real interest rate. Therefore, the VAR model enables the identification of whether the increases in output can be attributable to fiscal or monetary policy. Therefore, the VAR model consists of variables that model monetary policy.

Figure (A2) shows the first differences of real investment in ($bn 1996).

-10

-50

510

Firs

t diff

eren

ces

of re

al in

vest

men

t ( 1

996)

1933q1 1934q3 1936q1 1937q3 1939q1Time

55

VAR Coefficients for the spline interpolation

Dependent Var Variable Coefficient Standard Error

Quarterly GDP

rQGDP

L1 0.89 0.28

L2 0.93 0.54

L3 -0.90 0.26

Federal Funds

L1 -162 45.53

L2 323.82 83.43

L3 -187.19 45.95

Government Exp

L1 -1.11 0.39

L2 1.8 0.7

L3 -1.15 0.54

Monetary Base -0.99 2.2

Constant 186.06 41.34

Federal Funds

rQGDP

L1 0.0039 0.0019

L2 -0.01 0.0037

L3 0.004 0.0018

Federal Funds

L1 2.49 0.31

L2 -2.42 0.57

L3 0.87 0.31

Government Exp

L1 0.01 0.0026

56

Dependent Var Variable Coefficient Standard Error

L2 -0.012 0.0048

L3 0.0072 0.0037

Monetary Base -0.029 0.016

Constant -0.28 0.28

Government Exp rQGDP

L1 0.33 0.23

L2 -0.51 0.43

L3 0.25 0.21

Federal Funds

L1 13.09 36.06

L2 -22.28 66.08

L3 10.46 36.39

Government Exp

L1 1.15 0.305

L2 -0.63 0.56

L3 -0.2 0.43

Monetary Base 2.36 1.81

Constant 21.5 32.74

Table (A1) shows the coefficients for the variables specified in the VAR model for spline interpolation.

57

Response Orderings for the Orhogonalised Impulse Response functions

Dataset Name Ordering

Spline Interpolation

Order5 rQGDP, G_Exp, FedFunds

Order6 G_Exp, rQGDP, FedFunds

Order7 G_Exp, FedFunds, rQGDP

Order8 d.rQGDP, d.FedFunds, d.G_Exp

Order9 d.G_Exp, d.FedFunds, d.rQGDP

Third D.rQGDP, D.G_Exp, D.FedFunds

Chow Lin (Gordon) Second rgdpgor NYFedDiscount RealGovExp

Order11 rgdpgor RealGovExp NYFedDiscount

Order12 RealGovExp NYFedDiscount rgdpgor

Order14 d.rgdpgor d.RealGovExp d.NYFedDiscount

Order15 d.rqgdpgor d.NYFedDiscount

d.RealGovExp

Order16 d.RealGovExp d.NYFedDiscount

d.rqgdpgor

Denton Order17 qgdpdenton rgovexpdenton NYFedDiscount

Order18 rgovexpden NYFedDiscount

qgdpdenton

Order24 rqgdpdenton NYFedDiscount rgovexpdenton

58

Dataset Name Ordering

Order20 D.qgdpdenton d.rgovexpdeton NYFedDiscount

Order21 D.rgovexpden D.NYFedDiscount

D.qgdpdenton

Order25 d.qgdpdenton d.NYFedDiscount

d.rgovdenton

Table (A2) Orderings for the IRFs.

Indicators for the Denton Interpolation

Indicator Interpolated Variable

Federal Budget Expenditures NBER Series

15005b to 15005f

Real Quarterly Government Spending ($1996)

US Composite Index of Leading Series m16002a

NBER

Real Quarterly GDP

Table (A3) Indicators used for Denton Interpolation

Test for first differences of spline interpolation

Autocorrelation

Lag chi2 P>Chi2

1 19.34 0.14

2 21.4 0.11

Table (A4) shows the results of the Lagrange Multiplier Test.

59

Normality

Jarque-Bera Test

Equation chi2 P>Chi2

D.rQGDP 2.03 0.36

D.G_Exp 0.74 0.69

D.Fed_Funds 3.21 0.20

All 5.97 0.43

Skewness Test

Equation Skewness chi2 P>Chi2

D.rQGDP 0.61 1.17 0.28

D.G_Exp 0.28 0.25 0.61

D.Fed_Funds -0.94 2.78 0.1

All 4.20 0.24

Kurtosis Test

Equation Kurtosis chi2 P>Chi2

D.rQGDP 4.04 0.86 0.35

D.G_Exp 3.78 0.48 0.49

D.Fed_Funds 3.73 0.43 0.51

All 1.77 0.62

Table (A5) shows the tests for normality.

60

Stability

Eigenvalue Modulus

0.93 + 0.32i 0.98

0.93 - 0.32i 0.98

-0.94 0.94

0.64 + 0.60i 0.88

0.64 - 0.60i 0.88

0.80 + 0.35i 0.87

0.8 - 0.35i 0.87

-0.41 0.41

0.03 0.03

Table (A6) shows the tests for stability.

Tests for Chow Lin/Gordon and Krenn dataset with the same VAR model

First Differences

Stability

Eigenvalue Modulus

0.78 + 0.56i 0.96

0.78 - 0.56i 0.96

-0.47 + 0.69i 0.84

-0.47 - 0.69i 0.84

-0.59 + 0.33i 0.68

-0.59 - 0.33i 0.68

0.41 + 0.43i 0.59

0.41 - 0.43i 0.59

-0.05 0.05

Table (A7) shows the stability test for the VAR Model (All endogenous variables first differenced)

61

Normality

Jarque-Bera Test

Equation chi2 P>chi2

d.rqgdpgor 0.48 0.78

d.NYFedDiscount 0.22 0.90

d.RealGovExp 0.32 0.85

All 1.03 0.98

Skewness Test

Equation Skewness Chi2 P>Chi2

d.rqgdpgor 0.25 0.21 0.65

d.NYFedDiscount -0.22 0.15 0.70

d.RealGovExp -0.31 0.31 0.58

All 0.66 0.88

Kurtosis Test

Equation Kurtosis chi2 P>Chi2

d.rgdpgor 2.41 0.28 0.60

d.NYFedDiscount 2.70 0.07 0.79

d.RealGovExp 3.13 0.01 0.91

All 0.37 0.94

Table (A8) shows the results for normality in the residuals.

62

Test for Autocorrelation

Lag chi2 P>Chi2

1 10.21 0.33

2 14.42 0.11

Table (A9) shows the validity of the assumption that there is no autocorrelation.

Tests for Denton Interpolation

Stability

Eigenvalue Modulus

0.82+0.4i 0.92

0.82 - 0.4i 0.92

0.51 + 0.63i 0.81

0.51 - 0.63i 0.81

-0.45 + 0.63i 0.77

-0.45 - 0.63i 0.77

0.64 0.64

-0.34 + 0.10i 0.35

-0.34 - 0.10i 0.35

Table (A10) shows the stability of the VAR model.

Autocorrelation

Lag Chi2 P>Chi2

1 15.11 0.09

2 9.96 0.35

Table (A11) shows that it is reasonable to assume no autocorrelation in the errors.

63

Normality

Jarque-Bera Test

Equation chi2 P>Chi2

qgdpdenton 1.48 0.48

rgovexpden 1.02 0.6

NYFedDiscount 0.5 0.28

All 3.00 0.81

Skewness Test

Equation Skewness chi2 P>Chi2

qgdpdenton -0.13 0.06 0.81

rgovexpden -0.54 0.87 0.32

NYFedDiscount 0.15 0.07 0.79

All 1.103 0.78

Kurtosis Test

Equation Kurtosis Chi2 P>Chi2

qgdpdenton 1.69 1.42 0.23

rgovexpden 3.23 0.04 0.84

NYDiscount 2.29 0.43 0.51

All 1.89 0.59

Table (A12) shows the validity of the assumption that the errors are normal.

64

First differences of the Denton method

Stability

Eigenvalue Modulus

0.78 + 0.56i 0.96

0.78 - 0.56i 0.96

-0.47 + 0.69i 0.84

-0.47 - 0.69i 0.84

-0.59 + 0.33i 0.68

-0.59 - 0.33i 0.68

0.41 + 0.43i 0.59

0.41 - 0.43i 0.59

-0.05 0.05

Table (A13) shows that all eigenvalues lie inside the unit circle.

65

Normality

Jarque-Bera Test

Equation Chi2 P>Chi2

d.rqgdpgor 0.48 0.78

d.NYFedDiscount 0.22 0.90

d.RealGovExp 0.32 0.85

All 1.03 0.98

Skewness Test

Equation Skewness Chi2 P>Chi2

d.rqgdpgor 0.25 0.21 0.65

d.NYFedDiscount -0.22 0.15 0.70

d.RealGovExp -0.31 0.31 0.58

All 0.66 0.88

Kurtosis Test

Equation Kurtosis chi2 P>Chi2

d.rqgdpgor 2.41 0.28 0.60

d.NYFedDiscount 2.70 0.07 0.79

d.RealGovExp 3.13 0.01 0.91

All 0.37 0.94

Table (A14) shows the assumption of normality holds for the differenced VAR model.

66

Autocorrelation

Lag Chi2 P>Chi2

1 10.21 0.33

2 14.42 0.11

Table (A15) shows the assumption of no autocorrelation holds.

Granger Causality

Equation Excluded Chi2 P>Chi2

QGDP Real Gov 7.91 0.048

NYFed 18.55 0

All 47.64 0

Table (A16) shows that NY Federal Discount rate Granger causes real quarterly GDP. Whereas, real Government Expenditure's effects are debatable.

67

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