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BWFF3033 FINANCIAL MARKET AND INSTITUTIONS
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INTRODUCTION ................................................................................................................................. 2
LITERATURE REVIEW .................................................................................................................... 3
LITERATURE REVIEW BASED ON INFLATION ........................................................................ 3
LITERATURE REVIEW BASED ON INTEREST RATE ............................................................... 4
LITERATURE REVIEW BASED ON MONETARY POLICY ........................................................ 4
LITERATURE REVIEW BASED ON STOCK AND INVESTMENT ............................................ 5
METHODOLOGY ............................................................................................................................... 6
Data Source ......................................................................................................................................... 6
Estimation Technique ......................................................................................................................... 6
RESULTS AND DISCUSSION ........................................................................................................... 7
RESULTS ........................................................................................................................................... 7
DISCUSSION ..................................................................................................................................... 9
CONCLUSION ......................................................................................................................... 11
REFERENCES ......................................................................................................................... 12
APPENDIX ............................................................................................................................... 14
TABLE OF CONTENTS
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A Time Series Analysis on Correlation between Inflation Rate, Monetary Policy and
Interest rate with Gross Domestic Product
Mohamad Helmi Surep, Norhidayah Mohd Idros, Zarina Mohd.Nor,
Nor Faezah Che Hamzah and Nur Amirah Mohamad
Colloege of Business (COB), School of Economic, Finance and Banking (SEFB)
Universiti Utara Malaysia (UUM) 06010, Changloon Kedah, Malaysia
Abstract: This paper examines the correlation between inflation rate, monetary policy and
interest rate with gross domestic product (GDP) in Malaysian over the period 1980 to 2010.
The paper reports a strong relation between inflation rate based on CPI, monetary policy in
money supply and interest rate for deposit account with gross domestic product (GDP) in
Malaysian.
Keyword: Inflation rate, monetary policy, interest rate, gross domestic product.
INTRODUCTION
INTRODUCTION: A time series model can provide a reasonable benchmark to evaluate the
value added in forecasting of economic theory relative to the pure explanatory power of the
past behaviour of the variable. In this paper we conduct a detailed analysis of the time series
analysis on correlation between inflation rate, monetary policy and interest rate with gross
domestic product (GDP) focusing on the Malaysia, for which 30 years time series analysis
from 1980 to 2010. Our main goal is to investigation whether this 3 variable have correlation
between long-run economic growth and GDP.
The gross domestic product (GDP) or gross domestic income (GDI) is the value of
total production of goods and services in a country over a specified period and it is one of the
measures of national income and output for a given country's economy. GDP can be defined
in three ways, all of which are conceptually identical. First, it is equal to the total
expenditures for all final goods and services produced within the country in a stipulated
period of time (usually a 365-day year). Secondly, it is equal to the sum of the value added at
every stage of production (the intermediate stages) by all the industries within a country, plus
taxes less subsidies on products, in the period. Third, it is equal to the sum of the income
generated by production in the country in the period—that is, compensation of employees,
taxes on production and imports less subsidies, and gross operating surplus (or profits). The
most common approach to measure GDP is using the expenditure method:
GDP = Private Consumption + Gross Investment – Government
Spending + (Export – Import)
According to the graft, it shows the Malaysia GDP per purchasing power parity (PPP)
from year 1980 till 2010. We can see that at year 1980 increase to 1985 and then stable at
year 1986 and 1987. Next, it rises till year 1998 but drop at year 1989. After that, the graft
also shows that the GDP per capita PPP increase slowly until 2009 which is a value is 14000
that the highest value than other years. Lastly, it decline to 13900 at year 2000.
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From this graph we can conclude that the graft has fluctuated among year and the
factor that influences this GDP became fluctuated is inflation based on CPI, interest in
deposit and monetary policy in money supply. In this paper also we had a additional variable
that might be influence the GDP, but our analysis is not focus on this additional variable we
just give a overview how does this stock market will influence GDP by giving a past research
by other researcher in the literature review section. Next, the basic assumptions for this
research are:
Null hypothesis (H0) : Inflation rate, interest rate and money supply do not have a
Significant influence on Gross Domestic Product in Malaysia
Alternative hypothesis (H1): Inflation rate, interest rate and money supply have a
Significant influence on Gross Domestic Product in Malaysia
Lastly, this paper consists of five sections. In section, the literature review that is
consists of four variables that have been chosen. In section three, the methodology that is will
discuss about how we get the data and the estimation technique. In section four, the result of
the analysis and the discussion based on our literature review. Section five is our conclusion
about this research.
LITERATURE REVIEW
LITERATURE REVIEW BASED ON INFLATION
Multivariate Time Series Analysis on Correlation between Inflation Rate and
Employment Rate with Gross Domestic Product
This journal is written by Fadli Fizari Abu Hassan Asari, Zuraida Mohamad, The Sofia Alias,
Norazidah Shamsudin, Nurul Syuhada Baharuddin and Kamaruzaman Jusoff on 2011 at
Faculty of Business Management, Universiti Teknologi MARA, Dungun and at Faculty of
Forestry, Universiti Putra Malaysia. This study examined the correlation between inflation
rate and total employment with gross domestic product. It uses time series data ranging from
1982 to 2006 and the scope is in Malaysia. The researcher applied STATA software to
process the data using log-log model in this study
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A Comparison of Time Series Models for Forecasting GDP Growth and Inflation
This journal is written by Massimiliano Marcellino on April 2007 at IEP-Universita Bocconi,
IGIER and CEPR. In this paper the researcher conduct a detailed analysis of the forecasting
performance of univariate time series models for GDP growth and inflation, the two key
variables for macroeconomic analysis, focusing on the US, for which very long time series
are available. The researcher has use Forecasting method that have linear method, time-
varying methods and non-linear methods. In summary, the gains from a more careful
specification of the benchmark model for GDP growth and inflation appear to be both
statistically and economically significant.
LITERATURE REVIEW BASED ON INTEREST RATE
An Impact Analysis of Real Gross Domestic Product Inflation and Interest Rates on
Stock Prices of Quoted Companies in Nigeria
The journal case study from Department of Accounting, Faculty of Management Sciences
Olabisi Onabanjo University, Ago-Iwoye, Ogun State, Nigeria by Daferighe. Emmanuel E
and Aje. Samuel O. This journal was published in online on 2009 by © EuroJournals and this
journal aimed at examining the relationship between real GDP, inflation and interest rates,
and stock prices with a view to determining whether the fluctuations in the behaviour of stock
prices is influenced by those variables and analyse the impacts arising thereby. This research
intends to assess the impact of real GDP, inflation and interest rates on stock prices of quoted
companies in Nigeria and the method that use in this research are based on Stock Market
Value Index (SMVI).
The Impact of Foreign Interest Rates on the Economy: The Role of the Exchange Rate
Regime
The journal case study from Department, International Monetary Fund, Street, N.W.,
Washington on 27 June 2007 by Julian di Giovanniy and Jay C. Shambaugh. This journal
about the effect of interest rates in based on countries on other countries' annual real GDP
growth. This paper explores the connection between interest rates in major industrial
countries and annual real output growth in other countries and examines the potential
channels through which major-country interest rates affect other economies. We confirm the
results by moving beyond standard panel analysis, using a random coefficient model which
allows us to use a variety of controls and test why some countries experience more of an
impact from foreign interest rates.
LITERATURE REVIEW BASED ON MONETARY POLICY
Impact of Monetary Policy on Gross Domestic Product (GDP)
The journal case study from Iqra University, Business Administration Department, Karachi
and College of Management Sciences, Karachi on 27 June 2007. This literature was written
by Irfan Hameed and Ume-Amen it also can be obtained from the website ijcrb.webs.com.
This research article focuses on the impact of monetary policy on GDP and the country was
Pakistan. The study proved that the interest rate has minor relationship with GDP but the
growth in money supply greatly affects the GDP of an economy. This research use three
method is a data sources and nature of data in detail, the model to be applied to find out the
results and the hypothesis to be checked for this study.
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Fiscal and Monetary Policies and Economic Growth
The journal case study from Doctoral School of Finance and Banking, Academy of Economic
Bucharest, Romania and it was written by Moisa Altar on January 2003. This paper analyses
the way in which monetary and fiscal policy influences the performances of economic growth
and the methodology that this author use basis of a dynamic model with discrete variables of
the Sidrauski- Brock type, with infinite-lived households and money in the utility function.
This paper aim is to analyze the influence of several monetary and fiscal decisions on the
optimal trajectories and on the performance-function of the model
The Effects of Money Supply on Economic Growth in Iran
This literature was written by Fahimeh Movahedi Rad about The Effects of Money Supply on
Economic Growth in Iran. It was published in © EuroJournals on 2012 at Department of
Human Sciences, Islamic Azad University Mashhad Branch, and Mashhad, Iran. The journal
can be obtained from the website www.eurojournals.com/ajsr.htm. This literature concern
about symmetric impacts of monetary shocks on economic growth Applying econometric
technique by co-integration analysis and Error correction mechanism in Iran economy during
the period 1960-2010. This literature consists the theoretical and empirical literature of the
asymmetric effects of monetary shocks also Effects too Money Supply on Economic Growth
in Iran .In conclude, this happen at short run economic growth by increasing unexpected
money and must pay a higher cost in the long run to decrease inflation.
Linkage between Monetary Instruments and Economic Growth
This literature was written by Hameed Gul, Dr Khaid Mughal, and Dr Sabit Rahim on May
2012 at Islamabad about the Linkage between Monetary Instruments and Economic Growth.
In the literature it is showed that how the decisions of monetary authorities influence the
macro variables like GDP, money supply, interest rates, exchange rates and inflation. The
foremost objective of monetary policy is to enhance the level of welfare of the masses. The
method of least square OLS was used as strategy to explain the relationship between the
variables under study.
LITERATURE REVIEW BASED ON STOCK AND INVESTMENT
Stock Market and Economic Growth: An Empirical Analysis for Germany
The journal case study from Department of Applied Informatics, University of Macedonia,
Thessaloniki, Macedonia, Greece by Adamopoulus Antonios. It was published in online on
April 15, 2010. The main objective of this paper was to investigate the causal relationship
among economic growth, stock market development and bank lending at the Germany for the
period 1965-2007 using a Vector Error Correction Model (VECM).
Financial Stock Market and Economic Growth in Developing Countries: The Case of
Douala Stock Exchange in Cameroon
Rachelle Wouono Ognaligui was written this literature on May 2010 about Financial Stock
Market and Economic Growth in Developing at Cameroon, China. This paper examines
Sims’ causality test based on Granger definition of causality was used to examine causality
relationships between stock markets and economic growth in Cameroon based on the time
series data from 2006 to 2010. Our paper comes up with the opportunity given to the
Cameroonian government to understand that it is time to find financial policies, to encourage
companies and develop financial stock market culture, and enhance to push companies to
initiate IPO instead of bank loans when money is needed to increase their investment. It used
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Granger equation as a methodology in this literature to calculate whether stock market
development causes economic growth, or vice versa. In addition,
METHODOLOGY
Data Source
Data Source: In order to check the correlation between inflation rates, monetary policy and
interest rate with GDP we required to research inflation rate based on CPI, interest rate on
deposit in financial institution, monetary policy money supply (M2) and GDP at purchase
price. All this data is in term of currency Ringgit Malaysia and this data gather from the
period 1980-2010. The main data source in this regard has been the World Data Bank and the
additional sources was official websites of Bank Negara Malaysia has also been visited in this
regard.
Variables: Description for each variable under study is as under:
Monetary Policy: Monetary policy is the total amount of money available in an economy
at a particular point of time. The importance of an appropriate
monetary aggregate can hardly be over emphasized, particularly for
those countries that attach their monetary policy to monetary
aggregates. In this case we only focus in money supply M2
Interest Rate: Interest rate is the term interest rate usually means any bank lending
rate. However, the rates don’t always move rapidly because they are
driven by different forces. This rate is focus more on deposit in
financial institution. In this research we only look at bank institution.
Inflation: Inflation refers to the persistent rise in general price level. Inflation
affects the distribution of both income and wealth. In this case our is
more to inflation based on consumer price index CPI
Estimation Technique
Estimation Technique: To conduct this time series analysis we have applied EVIEW
software to process the data by using log-log model. The logarithm equation is as below: In
this EVIEW software we conduct two difference way analyses.
LOGGDP = - α + β LOGINFLATION - β LOGINTEREST + β LOGM2
Unit Roof Test: this Unit Roof Test use to integration orders of variables are examined by
Augmented Dickey – Fuller (ADF) unit root tests. Unit root tests are important to test
integration between the variables involved in the research conducted. This analysis involving
GDP, inflation, interest rate and money supply in two difference analyses that is trends and
trend intercept. This result of unit test we show whether the data have integrated between
GDP with inflation, interest rate and money supply.
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Co-Integration Test: This is followed by a multivariate co-integration analysis. The
multivariate co-integration techniques developed by Johansen and Juselious; Engle and
Granger using a maximum likelihood estimation procedure allows researchers to estimate
simultaneously models involving two or more variables to circumvent the problems
associated with the traditional regression methods used in previous studies on this issue.
Therefore, the Johansen method applies the maximum likelihood procedure to determine the
presence of cointegrated vectors in non-stationary time-series.
Vector error correction model: Since the variables included in the VAR model are found to
be co-integrated, the next step is to specify and estimate a Vector Error Correction Model
(VECM) including the error correction term to investigate dynamic behaviour of the model.
Once the equilibrium conditions are imposed, the VEC model describes how the examined
model is adjusting in each period towards its long-run equilibrium state.
RESULTS AND DISCUSSION
RESULTS
4.1 Descriptive Statistics
Table 1: Descriptive statistics on the data
Descriptive statistics: Table 1 shows the descriptive statistics including the minimum and
maximum values, mean, standard deviation and skewness of data. Firstly standard deviation
is widely used measure of the variability or dispersion, being algebraically more tractable
through practically less robust than the expected deviation or average absolute deviation. A
low standard deviation indicates that the data point to be very close to mean, vice verse.
Standard deviation for GDP at purchaser's prices is RM283427.8 and standard deviation for
inflation based on CPI is 2.077012. Next standard deviation for interest based on deposit is
2.606031and lastly standard deviation for money supply M2 is RM310000000000 or
3.10E+11. In addition skewness is a term describing the non-symmetry of a bell shaped
distribution. Skewed right is when too much data is on the left side of the curve, skewed left
is when too much data is on the right side of the curve. Skewness is important to know
because it can create some inaccurate estimates of means and standard deviations of data
expected to be symmetric. To get more analysis on these data we conduct a further analysis
that is unit root tests.
Stats GDP Inflation Interest M2
Min 54285.00 0.000000 2.000000 4.30E+10
Max 765965.0 10.00000 10.00000 1.06E+12
Mean 283427.8 3.225806 5.483871 3.59E+11
Sd 221375.6 2.077012 2.606031 3.10E+11
Sk 0.824469 1.060141 0.368642 0.844453
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4.2 Unit Roof Tests
Table 2: Results of Unit Roots Tests Based on Augmented Dickey-Fuller (ADF) Test
Trend Intercept and Trend
Level First Difference Level First Difference
LogGP -0.275231 -4.013205** -2.373107 -3.920186**
LogINFLATION -2.131528 -2.972549* -3.233263 -4.449267**
LogINTEREST -1.794903 -4.585405** -3.074261 -5.126128**
LogM2 -0.662644 -5.219095** -2.600832 -2.895983**
Notes: *, ** denote significance at 5% and 1% significant levels respectively.
Unit roof tests: In this analysis all variable have been tested at level and first difference.
From Augmented Dickey-Fuller (ADF) test we did trend analysis and trend intercept
analysis, firstly the result for trend analysis show that for t-statistics are statistically not
significant at level meaning that the null hypothesis is not rejected. This indicates that these
series are non-stationary at their level from. Consequently, the process is continued into the
first difference values of data and is show that this data have stationary at first difference.
After al variable finish with this analysis, our analysis continued to ADF trend intercept
analysis. The result from this analysis give us the same result from trend analysis that is
stationary at first difference but not at level. Based on table 2, it is show that all variable are
now stationary at first difference. Thus, all of the respective null hypotheses are rejected. This
analysis still continues with co-integration test.
4.3 Co-integration Test
Co-integration test: Cointegration rank (rank of matrix o) is estimated using Johansen
methodology. Johansen’s approach derives two likelihood estimators for the CI rank: a trace
test and a maximum Eigen value test.
Table 3: Results of Johansen-Juselius Test
Eigenvalue Statistics Trace statistic 5% Critical Value 1% Critical Value
r = 0 0.845452 72.08355** 47.21 54.46
r = 1 0.612749 31.00404* 29.68 35.65
r = 2 0.307547 10.13305 15.41 20.04
r = 3 0.088878 2.047721 3.76 6.65
Note: * indicates a significant level at 5%. The Trace and Max. Eigen value statistics have
been adjusted based on T-kn/T where T=number of effective observations, k=lag length,
n=number of independent variables
The CI rank (R) can be formally tested with the trace and the maximum Eigen value
statistics. The results are presented in Table 3. The trace statistic either rejects the null
hypothesis of no co-integration among the variables or does not reject the null hypothesis that
there is one co-integration relation between the variables. Start by testing H0: r = 0. If it
rejects, repeat for H0: r = 1. When a test is not rejected, stop testing there and that value of r is
the commonly-used estimate of the number of cointegrating relations. In this test, H0: r = 1 is
rejected at the 5% level (31.00 > 29.68). In other words, this trace test result rejects the null
hypothesis that these four variables are cointegrated at one co-integration. Since null
hypothesis is significant at r=1 so that we can conclude that at one co-integration exists
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among the variable. To know whether our variable moves closely to achieve that long run
equilibrium we will conduct further analysis that is Vector Error Correction.
Table 4: Long-run Equilibrium Coefficients for VECM
Variable Coefficient Std. Error t-Statistic Prob.
LNINTEREST -0.031969 0.101699 -0.314346 0.7559
LNINFLATION 0.098254 0.054399 1.806186 0.0829
LNM2 0.853898 0.049306 17.31824 0.0000
C -10.17191 1.430300 -7.111731 0.0000
R-squared 0.975875 Mean dependent var 12.29226
Adjusted R-squared 0.972979 S.D. dependent var 0.844986
S.E. of regression 0.138898 Akaike info criterion -0.982710
Sum squared resid 0.482317 Schwarz criterion -0.794118
Log likelihood 18.24930 F-statistic 337.0831
Durbin-Watson stat 1.323069 Prob(F-statistic) 0.000000
Vector Error Correction Model: The presence of cointegration between variables show that
exists one co-integration among variable, to know whether GDP, inflation, interest and
money supply have long-run relationship in the period 1980 - 2010 the VEC model can be
applied.
LGGDP = - 10.1719 + 0.09825LGINFLATION - 0.03196LGINTEREST + 0.85389LGM2
The cointegrating equation: Which is given at above has been normalized for LGGDP just
to get meaning from the coefficients. As all variable are logarithmic, we may interpret
coefficients in terms of elasticity because we have one co-integration among variable. So we
may say that 1 percent increase in inflation is associated with 0.098 percent increase in
Malaysia’s GDP. The coefficient of money supply M2 rate is also significant, and its value is
0.854 showing that 1 percent increase (decrease) will increase (decrease) GDP by 0.854
percent. On the other hand, the coefficients of interest showing that 1 percent increase in
interest on deposit is associated with 0.0319 percent decrease in GDP. Thus, GDP elasticity
with respect to money supply M2 is more elastic as compared to GDP elasticity with respect
to inflation and interest. This result clearly shows that monetary policy is the main factor that
effect Malaysia’s GDP from 1980 to 2010.
DISCUSSION
DISCUSSION: This paper currently study about the correlation between inflation rate,
monetary policy and interest rate with gross domestic product (GDP) in Malaysian over the
period 1980 to 2010. From the finding it is show that the entire variable has a correlation with
GDP in economic growth respectively. But the relationship between GDP and entire variable
can divide into two, which is in short-run economic growth and long-run economic growth.
INFLATION AND GDP
From our finding we can conclude that inflation only have a short-run economic growth, a
very limited role in GDP and have negative relationship with GDP. According to Fadli Fizari
Abu Hassan Asari et al.(2011) in journal Multivariate time series analysis on correlation
between inflation rate and employment rate with gross domestic product has mention that
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“the effects of inflation on economic growth continues to be an important and complex topic
in economic. Based on statement, if inflation has real economic effects, then governments can
influence economic performance through monetary policy”. Other that he also mentions that
“The structuralisms argue that inflation is necessary for economic growth, whereas the
monetarists argue the opposite” and he stated that “Inflation does not affect real output in the
long run, but that in the short-run inflation negatively affects output”. Besides this journal,
from other journal also give a same result from their research for example a journal that is A
comparison of time series models for forecasting GDP growth and inflation by Massimiliano
Marcellino (2007) has mention that “We can anticipate that we find a very limited role for
nonlinearity for both GDP growth and inflation, while a careful specification of the linear
models is very important” and the conclusion that led them in research say that “Inflation
forecasts produced by the Phillips curve generally have been more accurate than forecasts
based on other macroeconomic variables, including interest rates, money and commodity
prices”
INTEREST AND GDP
From this analysis it shows that interest rate in deposit only has negative relationship with
GDP and it will effect in short-run economic growth only. From other finding by Julian di
Giovanniy and Jay C. Shambaugh (2007) in their journal The Impact of Foreign Interest
Rates on the Economy: The Role of the Exchange Rate Regime show that “The main finding
thus implies that there are real costs to the loss of monetary autonomy that comes with
pegging and provides further support for the hypothesis that interest rates can have
substantial effects on the real economy. Specifically, base-country interest rates that are 1
percentage point higher lead to a 0.20 percentage point decline in annual GDP growth in
pegged countries as opposed to no change in countries with floats” this clearly show that
interest only give negative relationship to GDP as Julian di Giovanniy and Jay C.
Shambaugh (2007) said.
MONETARY POLICY AND GDP
Based on this analysis money supply give a positive relationship to GDP and it is more to
long-run economic growth compare to inflation and interest rate. From the journal that is
impact of monetary policy on gross domestic product (GDP) by Irfan Hameed and Ume-
Amen stated that “Monetary policy rests on the relationship between the rates of interest in an
economy, that is the price at which money can be borrowed, and the total supply of money.
Monetary policy uses a variety of tools to control one or both of these, to influence outcomes
like economic growth, inflation, exchange rates with other currencies and unemployment”
from this statement it show that money supply or monetary policy will affect many part of
economic, not only GDP. In our case money supply has effect the most in GDP so that the
percentage increase in GDP is higher in money supply. Other than that, according Fahimeh
Movahedi Rad in his journal The Effects of Money Supply on Economic Growth in Iran stated
that “although policy makers can increase the economic growth to some extent by supplying
unexpected amount of money, but with reduction of money supply and inflation, they should
spend much more due to the reduction of economic growth. Thus economic policy makers
should always consider monetary disciplines and macroeconomic stability and they should
not sacrifice it for short-term meager economic growth interest.” Based this statement it give
us evidence about our analysis.
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STOCK MARKET AND GDP
At the introduction section we have discuss about our additional variable that will affect
Malaysia’s that is stock market. It this section we will give a brief example and evidence how
does stock market effect GDP or economic growth. Firstly according to Adamopoulus
Antonios in his journal Stock Market and Economic Growth: An Empirical Analysis for
Germany. “Stock market development may influence economic growth is risk diversification.
Obstfeld suggests that international risk sharing through internationally integrated stock
markets improves the allocation of resources and accelerates the process of economic growth.
Evolution of stock market has impact on the operation of banking institutions and hence, on
economic promotion. This means that stock market is becoming more crucial, especially in a
number of emerging markets and their role should not be ignored. Information in stock
markets is contained in equity prices, while loan managers collect that in banks.” So this how
will stock market influence the growth of economic. In addition, journal Financial Stock
Market and Economic Growth in Developing Countries: the Case of Douala Stock Exchange
in Cameroon by Rachelle Wouono Ognaligui stated that
“The financial stock market facilitates higher investments and the allocation of capital, and
indirectly the economic growth. Sometimes investors avoid investing directly to the
companies because they cannot easily withdraw their money whenever they want. But
through the financial stock market, they can buy and sell stocks quickly with more
independence. An efficient stock market contributes to attract more investment by financing
productive projects that lead to economic growth, mobilize domestic savings, allocate capital
proficiency, reduce risk by diversifying, and facilitate exchange of goods and services.
Positive causal correlation between stock market development and economic activity. Many
other researchers argue that there is a positive correlation between financial development and
economic growth”. From both of this report it clearly show how does stock market affect the
GDP.
CONCLUSION
Conclusion: In this paper we have provided an extensive evaluation of the role of
sophisticated time series analysis on correlation between inflation rate, monetary policy and
interest rate with gross domestic product. We know that the factor that influences GDP
became fluctuated is inflation based on CPI, interest in deposit and monetary policy in money
supply. Our conclusion is inflation and interest rate influence the GDP in the short run only.
It means that inflation and interest rate only have a short-run economic growth, a very limited
role in GDP and have negative relationship with GDP. In addition, there is no relationship
between inflation and interest rate with GDP in the long run. However, from this analysis
money supply and our additional variable that is stock market give a positive relationship to
GDP and it is more to long-run economic growth compared to inflation and interest rate.
Besides, money supply or monetary policy will affect many part of economic, not only GDP.
Therefore, the result is called unidirectional since only money supply and stock market can
influence the GDP in the long run. This finding is particularly evident because we use real
time data or considering only the period starting from 1980 until 2010. It is recommended
that future researchers should improve the reliability and validity of the result by replacing
the other independent variables such as level of income, population and others.
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Mohamad, Norazidah Shamsudin and Kamaruzaman Jusoff (2011) 'A Vector Error
Correction Model (VECM) Approach in Explaining the Relationship Between Interest
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Nurul Syuhada Baharuddin and Kamaruzaman Jusoff (2011) 'Multivariate Time Series
Analysis on Correlation between Inflation Rate and Employment Rate with Gross
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2012).
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APPENDIX
Data 1: Original Data
Series Name
GDP (current
LCU) M2 (current LCU)
Deposit interest rate (%)
Inflation, consumer prices (annual
%)
1980 54,285 42,988,400,000 6 7 1981 58,669 51,521,800,000 10 10 1982 63,726 59,944,942,857 10 6 1983 71,223 69,845,700,000 8 4 1984 81,009 81,224,200,000 10 4 1985 78,890 89,196,000,000 9 0 1986 72,907 100,776,900,000 7 1 1987 81,085 102,999,600,000 3 0 1988 92,370 111,841,100,000 5 3 1989 105,233 136,257,600,000 6 3 1990 119,081 76,660,900,000 7 3 1991 135,123 89,599,100,000 8 4 1992 150,682 154,031,700,000 7 5 1993 172,193 194,638,300,000 5 4 1994 195,460 217,037,900,000 6 4 1995 222,472 257,245,200,000 7 3 1996 253,733 304,796,200,000 8 3 1997 281,795 353,672,088,820 9 3 1998 283,243 354,483,971,030 4 5 1999 300,764 397,372,516,000 3 3 2000 356,401 437,299,573,000 3 2 2001 352,579 488,183,360,263 3 1 2002 383,213 510,073,620,908 3 2 2003 418,769 554,078,590,009 3 1 2004 474,048 624,375,109,916 3 2 2005 522,445 679,277,474,460 3 3 2006 574,441 771,869,840,064 3 4 2007 642,049 833,021,504,227 3 2 2008 742,470 920,783,860,959 3 5 2009 679,938 992,051,869,993 2 1
2010 765,965 1,064,945,222,88
7 3 2
Data 2: Data In Terms of Logarithm Year LNGDP LNDEPOSIT LNINFLATION LNM2
1980 10.9020032247 1.79175946923 1.94591014906 24.4841961488
1981 10.9796667573 2.30258509299 2.30258509299 24.665270856
1982 11.0623479215 2.30258509299 1.79175946923 24.8166923588
1983 11.1735710789 2.07944154168 1.38629436112 24.9695543603
1984 11.3023155386 2.30258509299 1.38629436112 25.1204790693
1985 11.2758097561 2.19722457734 N/A 25.2141020325
1986 11.1969399353 1.94591014906 0 25.3361749997
1987 11.3032532662 1.09861228867 N/A 25.3579909417
1988 11.4335575296 1.60943791243 1.09861228867 25.4403449509
1989 11.5639322183 1.79175946923 1.09861228867 25.6378130488
1990 11.6875592128 1.94591014906 1.09861228867 25.062657637
1991 11.8139407537 2.07944154168 1.38629436112 25.2186111122
1992 11.9229269349 1.94591014906 1.60943791243 25.7604242623
1993 12.0563712197 1.60943791243 1.38629436112 25.9944088013
1994 12.1831110339 1.79175946923 1.38629436112 26.1033378296
1995 12.31255653 1.94591014906 1.09861228867 26.2732955526
1996 12.444037812 2.07944154168 1.09861228867 26.4429091935
1997 12.5489351352 2.19722457734 1.09861228867 26.591636018
1998 12.5540604654 1.38629436112 1.60943791243 26.5939289662
1999 12.614081183 1.09861228867 1.09861228867 26.7081400051
2000 12.7838117804 1.09861228867 0.69314718056 26.8038843189
2001 12.7730299896 1.09861228867 0 26.9139569105
2002 12.8563462493 1.09861228867 0.69314718056 26.957820907
2003 12.9450747342 1.09861228867 0 27.0405723729
2004 13.0690638614 1.09861228867 0.69314718056 27.1600171624
2005 13.1662749941 1.09861228867 1.09861228867 27.2442955327
2006 13.261152673 1.09861228867 1.38629436112 27.3720817718
2007 13.3724199037 1.09861228867 0.69314718056 27.4483252942
2008 13.5177377448 1.09861228867 1.60943791243 27.548491167
2009 13.4297568965 0.69314718056 0 27.6230412312
2010 13.5488917558 1.09861228867 0.69314718056 27.6939444799
Data 3: co-integration Test
Date: 10/28/12 Time: 13:07 Sample: 1980 2010
Included observations: 22 Test assumption: Linear deterministic trend in the data
Series: LNGDP LNDEPOSIT LNINFLATION LNM2 Lags interval: 1 to 2
Likelihood 5 Percent 1 Percent Hypothesized Eigenvalue Ratio Critical Value Critical Value No. of CE(s)
0.845452 72.08355 47.21 54.46 None ** 0.612749 31.00404 29.68 35.65 At most 1 * 0.307547 10.13305 15.41 20.04 At most 2 0.088878 2.047721 3.76 6.65 At most 3
*(**) denotes rejection of the hypothesis at 5%(1%) significance level L.R. test indicates 2 cointegrating equation(s) at 5% significance level
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Data 4: Unit Roof Test – Trend
Interest
Level First Difference ADF Test Statistic -1.794903 1% Critical Value* -3.6752
5% Critical Value -2.9665
10% Critical Value -2.6220
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNDEPOSIT)
Method: Least Squares
Date: 10/11/12 Time: 17:29
Sample(adjusted): 1982 2010
Included observations: 29 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNDEPOSIT(-1) -0.208054 0.115914 -1.794903 0.0843
D(LNDEPOSIT(-1)) 0.058167 0.185480 0.313603 0.7563
C 0.291976 0.194189 1.503566 0.1447
R-squared 0.113194 Mean dependent var -0.041516
Adjusted R-squared 0.044978 S.D. dependent var 0.296797
S.E. of regression 0.290045 Akaike info criterion 0.460139
Sum squared resid 2.187285 Schwarz criterion 0.601583
Log likelihood -3.672016 F-statistic 1.659352
Durbin-Watson stat 2.060351 Prob(F-statistic) 0.209784
ADF Test Statistic -4.585405 1% Critical Value* -3.6852
5% Critical Value -2.9705
10% Critical Value -2.6242
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNDEPOSIT,2)
Method: Least Squares
Date: 10/11/12 Time: 17:32
Sample(adjusted): 1983 2010
Included observations: 28 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNDEPOSIT(-1)) -1.294712 0.282355 -4.585405 0.0001
D(LNDEPOSIT(-1),2) 0.210105 0.191258 1.098545 0.2824
C -0.054881 0.058971 -0.930643 0.3609
R-squared 0.556526 Mean dependent var 1.98E-17
Adjusted R-squared 0.521048 S.D. dependent var 0.442032
S.E. of regression 0.305915 Akaike info criterion 0.569936
Sum squared resid 2.339595 Schwarz criterion 0.712672
Log likelihood -4.979106 F-statistic 15.68652
Durbin-Watson stat 2.084868 Prob(F-statistic) 0.000039
GDP
Level First Difference
ADF Test Statistic -0.275231 1% Critical Value* -3.6752
5% Critical Value -2.9665
10% Critical Value -2.6220
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNGDP)
Method: Least Squares
Date: 10/10/12 Time: 17:48
Sample(adjusted): 1982 2010
Included observations: 29 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNGDP(-1) -0.004338 0.015761 -0.275231 0.7853
D(LNGDP(-1)) 0.023755 0.196507 0.120886 0.9047
C 0.139565 0.193731 0.720404 0.4777
R-squared 0.003441 Mean dependent var 0.088594
Adjusted R-squared -0.073217 S.D. dependent var 0.065203
S.E. of regression 0.067548 Akaike info criterion -2.454250
Sum squared resid 0.118632 Schwarz criterion -2.312805
Log likelihood 38.58662 F-statistic 0.044894
Durbin-Watson stat 1.981862 Prob(F-statistic) 0.956173
ADF Test Statistic -4.013205 1% Critical Value* -3.6852
5% Critical Value -2.9705
10% Critical Value -2.6242
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNGDP,2)
Method: Least Squares
Date: 10/10/12 Time: 17:52
Sample(adjusted): 1983 2010
Included observations: 28 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNGDP(-1)) -1.172826 0.292242 -4.013205 0.0005
D(LNGDP(-1),2) 0.210406 0.231670 0.908215 0.3724
C 0.105173 0.029529 3.561663 0.0015
R-squared 0.503034 Mean dependent var 0.001302
Adjusted R-squared 0.463276 S.D. dependent var 0.092635
S.E. of regression 0.067866 Akaike info criterion -2.441604
Sum squared resid 0.115145 Schwarz criterion -2.298868
Log likelihood 37.18245 F-statistic 12.65260
Durbin-Watson stat 1.968340 Prob(F-statistic) 0.000160
Inflation
Level First Difference ADF Test Statistic -2.131528 1% Critical Value* -3.7343
5% Critical Value -2.9907
10% Critical Value -2.6348
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNCPIINFLASI)
Method: Least Squares
Date: 10/11/12 Time: 17:46
Sample(adjusted): 1982 2010
Included observations: 24
Excluded observations: 5 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNCPIINFLASI(-1) -0.430333 0.201890 -2.131528 0.0450
D(LNCPIINFLASI(-1)) -0.340092 0.201027 -1.691774 0.1055
C 0.393308 0.247736 1.587610 0.1273
R-squared 0.435884 Mean dependent var -0.055073
Adjusted R-squared 0.382159 S.D. dependent var 0.584722
S.E. of regression 0.459609 Akaike info criterion 1.399586
Sum squared resid 4.436043 Schwarz criterion 1.546843
Log likelihood -13.79503 F-statistic 8.113195
ADF Test Statistic -2.972549 1% Critical Value* -3.7667
5% Critical Value -3.0038
10% Critical Value -2.6417
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNCPIINFLASI,2)
Method: Least Squares
Date: 10/11/12 Time: 17:47
Sample(adjusted): 1983 2010
Included observations: 22
Excluded observations: 6 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNCPIINFLASI(-1)) -1.311281 0.441130 -2.972549 0.0078
D(LNCPIINFLASI(-
1),2)
-0.177287 0.278820 -0.635850 0.5325
C -0.084481 0.113430 -0.744783 0.4655
R-squared 0.773671 Mean dependent var 0.054726
Adjusted R-squared 0.749847 S.D. dependent var 1.048927
S.E. of regression 0.524624 Akaike info criterion 1.673854
Sum squared resid 5.229379 Schwarz criterion 1.822633
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Durbin-Watson stat 1.976687 Prob(F-statistic) 0.002451
Log likelihood -15.41240 F-statistic 32.47426
Durbin-Watson stat 1.990799 Prob(F-statistic) 0.000001
Money Supply M2
Level First Difference ADF Test Statistic -0.662644 1% Critical Value* -3.6752
5% Critical Value -2.9665
10% Critical Value -2.6220
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNM2)
Method: Least Squares
Date: 10/11/12 Time: 17:12
Sample(adjusted): 1982 2010
Included observations: 29 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNM2(-1) -0.022248 0.033575 -0.662644 0.5134
D(LNM2(-1)) -0.010786 0.193956 -0.055611 0.9561
C 0.688211 0.879425 0.782570 0.4409
R-squared 0.016811 Mean dependent var 0.104437
Adjusted R-squared -0.058819 S.D. dependent var 0.160912
S.E. of regression 0.165577 Akaike info criterion -0.661065
Sum squared resid 0.712809 Schwarz criterion -0.519620
Log likelihood 12.58544 F-statistic 0.222280
Durbin-Watson stat 2.009048 Prob(F-statistic) 0.802197
ADF Test Statistic -5.219095 1% Critical Value* -3.6852
5% Critical Value -2.9705
10% Critical Value -2.6242
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNM2,2)
Method: Least Squares
Date: 10/11/12 Time: 17:14
Sample(adjusted): 1983 2010
Included observations: 28 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNM2(-1)) -1.384520 0.265280 -5.219095 0.0000
D(LNM2(-1),2) 0.359414 0.185820 1.934200 0.0645
C 0.144745 0.041373 3.498574 0.0018
R-squared 0.573830 Mean dependent var -0.002876
Adjusted R-
squared
0.539736 S.D. dependent var 0.233693
S.E. of regression 0.158544 Akaike info criterion -0.744617
Sum squared resid 0.628402 Schwarz criterion -0.601880
Log likelihood 13.42463 F-statistic 16.83100
Durbin-Watson stat 1.966512 Prob(F-statistic) 0.000023
Data 5: Unit Roof Test – Trend and intercept
Interest
Level First Difference ADF Test Statistic -3.233263 1% Critical Value* -4.3082
5% Critical Value -3.5731
10% Critical Value -3.2203
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNDEPOSIT)
Method: Least Squares
Date: 10/21/12 Time: 14:29
Sample(adjusted): 1982 2010
Included observations: 29 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNDEPOSIT(-1) -0.634077 0.196111 -3.233263 0.0034
D(LNDEPOSIT(-1)) 0.237344 0.189445 1.252838 0.2219
C 1.420157 0.472038 3.008564 0.0059
@TREND(1980) -0.027534 0.010739 -2.563823 0.0167
R-squared 0.299194 Mean dependent var -0.041516
Adjusted R-squared 0.215097 S.D. dependent var 0.296797
S.E. of regression 0.262947 Akaike info criterion 0.293710
Sum squared resid 1.728522 Schwarz criterion 0.482302
Log likelihood -0.258794 F-statistic 3.557733
Durbin-Watson stat 1.976644 Prob(F-statistic) 0.028533
ADF Test Statistic -4.449267 1% Critical Value* -4.3226
5% Critical Value -3.5796
10% Critical Value -3.2239
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNDEPOSIT,2)
Method: Least Squares
Date: 10/21/12 Time: 14:26
Sample(adjusted): 1983 2010
Included observations: 28 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNDEPOSIT(-1)) -1.287435 0.289359 -4.449267 0.0002
D(LNDEPOSIT(-1),2) 0.205383 0.200845 1.022596 0.3167
C -0.078730 0.134402 -0.585785 0.5635
@TREND(1980) 0.001572 0.007334 0.214279 0.8321
R-squared 0.542043 Mean dependent var 0.014481
Adjusted R-squared 0.484798 S.D. dependent var 0.434841
S.E. of regression 0.312118 Akaike info criterion 0.640692
Sum squared resid 2.338021 Schwarz criterion 0.831007
Log likelihood -4.969686 F-statistic 9.468889
Durbin-Watson stat 2.012424 Prob(F-statistic) 0.000260
GDP
Level First Difference ADF Test Statistic -2.373107 1% Critical Value* -4.3082
5% Critical Value -3.5731
10% Critical Value -3.2203
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNGDP)
Method: Least Squares
Date: 10/21/12 Time: 14:03
Sample(adjusted): 1982 2010
Included observations: 29 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNGDP(-1) -0.366898 0.154607 -2.373107 0.0256
D(LNGDP(-1)) 0.218741 0.199294 1.097579 0.2828
ADF Test Statistic -3.920186 1% Critical Value* -4.3226
5% Critical Value -3.5796
10% Critical Value -3.2239
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNGDP,2)
Method: Least Squares
Date: 10/21/12 Time: 14:17
Sample(adjusted): 1983 2010
Included observations: 28 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNGDP(-1)) -1.174122 0.299507 -3.920186 0.0006
D(LNGDP(-1),2) 0.212273 0.239700 0.885579 0.3846
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C 4.001250 1.649154 2.426244 0.0228
@TREND(1980) 0.034662 0.014716 2.355490 0.0267
R-squared 0.184441 Mean dependent var 0.088594
Adjusted R-squared 0.086574 S.D. dependent var 0.065203
S.E. of regression 0.062317 Akaike info criterion -2.585718
Sum squared resid 0.097086 Schwarz criterion -2.397126
Log likelihood 41.49292 F-statistic 1.884609
Durbin-Watson stat 1.991292 Prob(F-statistic) 0.158023
C 0.104012 0.038843 2.677757 0.0132
@TREND(1980) 7.79E-05 0.001644 0.047362 0.9626
R-squared 0.503080 Mean dependent var 0.001302
Adjusted R-squared 0.440965 S.D. dependent var 0.092635
S.E. of regression 0.069262 Akaike info criterion -2.370269
Sum squared resid 0.115134 Schwarz criterion -2.179954
Log likelihood 37.18376 F-statistic 8.099170
Durbin-Watson stat 1.969126 Prob(F-statistic) 0.000672
Inflation
Level First Difference ADF Test Statistic -2.600832 1% Critical Value* -4.3942
5% Critical Value -3.6118
10% Critical Value -3.2418
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNINFLATION)
Method: Least Squares
Date: 10/21/12 Time: 14:36
Sample(adjusted): 1982 2010
Included observations: 24
Excluded observations: 5 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNINFLATION(-1) -0.723571 0.278208 -2.600832 0.0171
D(LNINFLATION(-1)) -0.195051 0.218435 -0.892947 0.3825
C 1.155703 0.566334 2.040674 0.0547
@TREND(1980) -0.024102 0.016204 -1.487445 0.1525
R-squared 0.492073 Mean dependent var -0.055073
Adjusted R-squared 0.415884 S.D. dependent var 0.584722
S.E. of regression 0.446889 Akaike info criterion 1.377997
Sum squared resid 3.994187 Schwarz criterion 1.574339
Log likelihood -12.53596 F-statistic 6.458586
Durbin-Watson stat 1.967914 Prob(F-statistic) 0.003100
ADF Test Statistic -2.895983 1% Critical Value* -4.4415
5% Critical Value -3.6330
10% Critical Value -3.2535
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNINFLATION,2)
Method: Least Squares
Date: 10/21/12 Time: 14:38
Sample(adjusted): 1983 2010
Included observations: 22
Excluded observations: 6 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNINFLATION(-1)) -1.328117 0.458607 -2.895983 0.0096
D(LNINFLATION(-
1),2)
-0.166548 0.289935 -0.574433 0.5728
C -0.152930 0.323185 -0.473197 0.6418
@TREND(1980) 0.003591 0.015818 0.227025 0.8230
R-squared 0.774317 Mean dependent var 0.054726
Adjusted R-squared 0.736703 S.D. dependent var 1.048927
S.E. of regression 0.538230 Akaike info criterion 1.761904
Sum squared resid 5.214448 Schwarz criterion 1.960276
Log likelihood -15.38095 F-statistic 20.58597
Durbin-Watson stat 1.988329 Prob(F-statistic) 0.000005
Money Supply M2
Level First Difference ADF Test Statistic -3.074261 1% Critical Value* -4.3082
5% Critical Value -3.5731
10% Critical Value -3.2203
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNM2)
Method: Least Squares
Date: 10/21/12 Time: 14:16
Sample(adjusted): 1982 2010
Included observations: 29 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
LNM2(-1) -0.565813 0.184048 -3.074261 0.0050
D(LNM2(-1)) 0.265856 0.193287 1.375448 0.1812
C 13.92651 4.491364 3.100730 0.0047
@TREND(1980) 0.060377 0.020181 2.991748 0.0062
R-squared 0.276014 Mean dependent var 0.104437
Adjusted R-squared 0.189136 S.D. dependent var 0.160912
S.E. of regression 0.144898 Akaike info criterion -0.898128
Sum squared resid 0.524887 Schwarz criterion -0.709536
Log likelihood 17.02286 F-statistic 3.177019
Durbin-Watson stat 1.931825 Prob(F-statistic) 0.041507
ADF Test Statistic -5.126128 1% Critical Value* -4.3226
5% Critical Value -3.5796
10% Critical Value -3.2239
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LNM2,2)
Method: Least Squares
Date: 10/21/12 Time: 14:21
Sample(adjusted): 1983 2010
Included observations: 28 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
D(LNM2(-1)) -1.388561 0.270879 -5.126128 0.0000
D(LNM2(-1),2) 0.361793 0.189644 1.907753 0.0685
C 0.160821 0.076844 2.092819 0.0471
@TREND(1980) -0.000948 0.003788 -0.250255 0.8045
R-squared 0.574939 Mean dependent var -0.002876
Adjusted R-squared 0.521806 S.D. dependent var 0.233693
S.E. of regression 0.161602 Akaike info criterion -0.675794
Sum squared resid 0.626767 Schwarz criterion -0.485479
Log likelihood 13.46112 F-statistic 10.82082
Durbin-Watson stat 1.967771 Prob(F-statistic) 0.000109
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