md in russia 2010

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Money Demand in Post-Crisis Russia:

Dedollarization and Remonetization

Iikka Korhonen and Aaron Mehrotra

AbsTrAcT: This paper assesses the monetary determinants o ination in Russia usingmoney demand unctions. We fnd a stable money demand relation or Russia ollowingthe 1998 crisis. Higher income boosts demand or real ruble balances and the incomeelasticity o money is larger than unity, reecting remonetization in the Russian economy.Ination aects the adjustment toward equilibrium, whereas broad money shocks leadto higher ination. We also show that exchange rate uctuations considerably inuenceRussian money demand. Our results or system stability and the predictive value o moneyjustiy using the money stock as an inormation variable. They also suggest that the stronginuence o exchange rate on money demand is likely to continue, despite the dedollariza-

tion o the Russian economy.KEy words: dollarization, money demand, Russia, vector error-correction models.

The Russian economy has undergone substantial changes since the August 1998 eco-

nomic and nancial crises. Economic growth resumed airly soon ater the ruble’s drastic

devaluation and a rebound in world energy prices. Rapid economic growth and soaring

commodity prices have helped push the Russian government’s nances into strong surplus

ater a prolonged period o public-sector decits. The changes in the spheres o exchange

rate and monetary policy, though less apparent, have also been proound. Beore the

August 1998 crisis, the ruble was pegged airly tightly to the U.S. dollar. Since the crisisand the ailure o the exchange rate peg—the linchpin o the Central Bank o Russia’s

(CBR’s) stabilization eorts—the CBR has avoided explicit exchange rate targets. It has,

nevertheless, infuenced changes in the ruble’s exchange rate quite heavily. At the same

time, the CBR has had a target band or infation. However, this target has usually been

overshot, as the CBR has given precedence to maintaining the nominal exchange rate

close to a desired level. In such an environment, searching or infation determinants has

obvious policy relevance. Our results should be interpreted in this light, as we assess how

shocks to the broad money stock, as well as exchange rate changes aecting the demand

or domestic money, are transmitted to infation. Our results are novel to the literature

on Russian monetary and exchange rate policy and, in addition, are relevant or other

emerging market countries where the exchange rate continues to be used as a policy tool,even i a xed exchange rate policy has been abandoned.

Iikka Korhonen (iikka.korhonen@bo.) is head o the Bank o Finland Institute or Economies

in Transition (BOFIT). Aaron Mehrotra (aaron.mehrotra@bo.) is a senior economist at BOFIT.

The authors thank Markus Lahtinen, Alexey Ponomarenko, and Jouko Rautava or their helpul

comments on several versions o this paper. They are also grateul to the participants at the Twenty-

Ninth Annual Meeting o the Finnish Society or Economic Research, Eighty-Second Annual

Conerence o the Western Economic Association, and internal seminars at the central banks o

Russia and Finland. All opinions expressed here are those o the authors and do not represent the

ocial position o the Bank o Finland.

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6 Emeging Maket Finane & Tae

In this paper, we estimate money demand unctions or Russia’s postcrisis period. The

evolution o the monetary policy regime over eight postcrisis years provides sucient

data to test econometrically or the presence o a stable money demand unction. Sev-

eral interesting research questions—all with clear policy implications—can be assessed

within this ramework. First, we can assess whether a stable money demand unction canbe said to exist. We know that Russian households and companies have shown increas-

ing trust in their currency and banks in recent years, which has led to a remonetization

o the economy. But can we nd a statistically signicant and stable relation among

money, prices, income, and other relevant variables in such an environment? I so, does,

or example, the income elasticity o money demand dier rom the values typically

ound in more mature economies? Second, we are interested in the role o the exchange

rate in Russian money demand. Even though the demand or ruble deposits has clearly

increased, oreign currency (especially U.S. dollar) holdings o Russian residents remain

large. We want to know whether the exchange rate aects ruble money demand in Rus-

sia. Presumably, the larger the absolute value o this infuence, the higher the remaining

degree o dollarization. This would justiy a large role or the exchange rate in conducting

monetary policy, which is likely the case in other emerging market economies, where the

exchange rate continues to be signicant in setting monetary policy.

To answer the above questions, we rst look or a cointegrating relation among the

real ruble money stock, an indicator or gross domestic product (GDP), and various

proxies or the opportunity cost o holding money. We nd that when these variables are

augmented by a trend, a stable long-run relation among them seems to exist. This rela-

tion can be interpreted as a money demand unction, whereas the trend variable captures

the monetary deepening we see in postcrisis Russia. We also nd that the data support

including the exchange rate variable. Next, we employ a vector error-correction model

to study the dynamics o our estimated system. A positive shock to real money stock increases infation and output in the Russian economy. An infation orecast based on the

estimated model captures actual price dynamics reasonably well. These results suggest

that money can useully indicate uture infation pressures and that the exchange rate

continues to infuence money demand in Russia.

The novelty o this analysis is that we concentrate solely on postcrisis data, which

allow us to draw more relevant policy conclusions. The years beore the 1998 crisis

were marked by both a dierent monetary policy regime and the transition rom a so-

cialist system toward a more market-based system. The dynamics between the variables

may not be regime independent, and estimating a money demand relation covering

two dierent regimes could yield spurious results. Previous research has oten ound

evidence o an unstable demand unction or the ruble money stock (see, e.g., Bahmani-

Oskooee and Barry 2000; Oomes and Ohnsorge 2005). Our investigation or model

dynamics covers aspects that have not been investigated in money demand studies or

Russia during the current decade. These include using the money demand system to

orecast infation, measuring uncertainty in impulse response dynamics, and valuing

the inormation rom the real ruble money gap, that is, the dierence between actual

and equilibrium ruble money stock. All o these are important in considering the use

o money stock in the conduct o monetary policy. We also test or the importance o

the spot and one-month orward ruble/U.S. dollar exchange rates as opportunity cost

variables. This more clearly indicates the signicance o exchange rate movements

or ruble money demand.

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Mah–Apil 2010 7

Description and Selective Literature Survey o Russian Monetary Policy

During the Postcrisis Era

The August 1998 nancial crisis caused more than an abandonment o the xed peg or

the ruble; it orced the Russian government to reschedule its loan commitments, an act

tantamount to partial deault on its debts. Many commercial banks went under, some o

them owing substantial sums o money to oreign investors who had hedged their ruble

risks by selling ruble orward contracts. The underlying cause o the crisis was the Russian

government’s inability to balance its nances. A vicious circle o increased domestic and

international borrowing at ever-higher interest rates ended when the supply o external

nancing dried up. Russia was orced to abandon its xed exchange rate and reschedule

its loan commitments. Sutela (2000) discusses the crisis and the events leading up to it.

The collapse made it clear that an alternative regime was needed or monetary and

exchange rate policies. The adopted (and current) regime might be described as a hybrid

o infation targeting and managed foat. In its annual Guidelines for the Single State

Monetary Policy, the CBR species the infation target or the next year and includesa orecast range or growth o the ruble money stock (M2). Korhonen and Mehrotra

(2007) list these infation targets and money growth projections, as well as actual out-

comes, or the period 1999–2006. The conduct o monetary policy in that period was not

straightorward. The lack o monetary policy instruments and underdeveloped interbank

markets compelled the CBR to rely heavily on oreign exchange interventions. The CBR

announces annual ceilings or appreciation o the real ruble rate. During the postcrisis era,

the scal authorities have helped Russian monetary policy in ghting infation. Strong

budget surpluses and—especially rom 2004 onward—growth o the Stabilization Fund

have helped to regulate liquidity in the economy.

In an overview o nominal anchors in the Commonwealth o Independent States (CIS)

countries, Keller and Richardson (2003) nd that almost all CIS countries, even i they

claim to have foating exchange rates, are heavily involved in managing the external values

o their currencies. This reliance on exchange rate interventions as the main instrument

o monetary policy is partly explained by high degrees o dollarization. The authors

conclude that as infation expectations decline and dedollarization gathers pace, other

nominal anchors may become more optimal. Even so, direct infation targeting appears

to be out o reach or these central banks or a while.

Previous research on money demand and prices in Russia includes a traditional money

demand unction approach along with estimations o various monetary policy rules.

With a single exception, none o the studies reviewed here concentrate on the postcrisis

period. Nikolic ;

(2000) estimates several models o infation or Russia using data romthe precrisis period. As one would expect, he nds that money Granger-causes infation.

However, the ndings also suggest that infation may Granger-cause growth o the broad

money stock; to the extent that this refects eedback eects in money demand, the cen-

tral bank needs to account or such eects in its eorts to control the money stock. In

addition, velocity was highly volatile beore the crisis, compounding the diculties in

implementing stabilizing monetary policies.

Oomes and Ohnsorge (2005) augment traditional money demand unctions by in-

cluding estimates o oreign cash holdings in the monetary aggregates. When testing

or cointegration between the monetary aggregate, output variable, deposit interest rate,

and exchange rate change, they nd the most stable long-run relation or the specica-

tion in which both oreign currency deposits and cash holdings are included in the broad

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money aggregate. In the specications with only the ruble money stock, the exchange

rate change is always signicant and a aster rate o depreciation decreases the demand

or ruble balances. However, the model with the broad ruble money stock is reported to

be the most unstable o all tested specications.

Vymyatnina (2006) studies the monetary transmission mechanism in Russia betweenJuly 1995 and September 2004. The hypothesis is that demand or real ruble balances

depends on income (proxied by real total trade), opportunity cost o holding money

(proxied by an interbank interest rate) and the exchange rate against the U.S. dollar.

However, the estimated cointegration relation appears to be unstable, especially around

the time o the 1998 crisis and rom 2002 onward. Estimating a single model or the

entire data sample ails to pick up the deepening monetization o the Russian economy

o recent years (see Kim and Pirttilä 2004). Though the relatively long sample obviously

yields more observations, including the 1998 crisis complicates the interpretation o the

obtained coecients.

Granville and Mallick (2006) estimate long-run relations between real money, the

interest rate, and the exchange rate with monthly data rom February 1992 to January

2005. According to their results, the exchange rate aects interest rates in Russia but

does not directly aect infation. However, the authors detect a break around the 1998

crisis, which prompts them to estimate the model or infation and interest rate only or

the precrisis period as well. The resulting coecients are quite dierent rom those

obtained rom the ull sample, reinorcing our view that it is quite useul to concentrate

on the postcrisis period.

Esanov et al. (2006) estimate several monetary policy rules or Russia rom the last

quarter o 1993 to 2002. Although this is not exactly the same as estimating money

demand unctions, the exercise is relevant. The McCallum rule or monetary policy

determines the growth o the monetary base by the target and actual rates o growth o the nominal GDP as well as the change in velocity. In addition, the authors use the U.S.

dollar exchange rate in estimating both McCallum and Taylor monetary policy rules, as

we do when estimating money demand unctions or Russia. Esanov et al. (2006) also

estimate a hybrid Ball rule or Russian monetary policy. They conclude that the McCal-

lum rule, where monetary base is the policy instrument, best ts the data or the period.

On one hand, the CBR has always paid attention to monetary aggregates, as evidenced

by its public orecasts or M2 growth. This would make the result plausible. On the other

hand, the exchange rate has gured prominently in CBR policy, both beore and ater

the 1998 crisis. Furthermore, changes in velocity have meant that growth o the money

stock has consistently overshot its target band in the postcrisis era. Yet the authors do

not consider a policy rule under which the exchange rate is the policy variable. Interpret-

ing the results is all the more challenged by the presence o large shits in the levels o

many variables ollowing the 1998 nancial crisis, and despite the authors’ best eorts

to control or this with dummy variables.

Vdovichenko and Voronina (2006) also look at dierent monetary policy rules in Rus-

sia, but they concentrate on the period between 2000 and 2003. Their analysis reveals that

the CBR has continued to regard the stability o the exchange rate as very important. Also,

the monetary base seems to be more relevant as a domestic monetary policy instrument

than, say, the interest rate. The result concerning the exchange rate accords with our own

results and we also leave out the interest rate in our estimations.

In a CBR internal note, Ponomarenko (2007) estimates an error-correction moneydemand unction or Russia using real M2 as the dependent variable between March

1999 and September 2006 In the long run specication the income variable is domestic

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Mah–Apil 2010 9

absorption (household consumption, xed capital ormation, and government consump-

tion). The income elasticity o money seems to be quite high—over 2.5. However, the

estimations are done in a single-equation ramework, whereas we utilize a systems ap-

proach. Thereore, no impulse responses are reported either.

From the studies reviewed here, it appears that a money demand relation can also beound or Russia, even with the complications rom widespread dollarization and the

August 1998 nancial crisis. Nevertheless, it is apparent that these actors have intro-

duced a large degree o uncertainty into the empirical results. Thus, we aim to provide

more stable and policy-relevant results by restricting ourselves to data rom the postcrisis

period and a single monetary policy regime.

Data

The present study uses monthly data rom January 1999 to December 2006, omitting the

months immediately ollowing the August 1998 crisis. This eight-year period, though

short, provides enough observations or statistical inerence about the postcrisis behavior

o money demand in Russia. Admittedly, a cointegration analysis would benet rom a

longer sample (see, e.g., Hakkio and Rush 1991). Nevertheless, the very nature o transition

economies implies that a long consistent sample is not available.1 Moreover, economic

theory ocuses on long-run relations that are captured in the cointegration ramework.

In the main econometric model, we use monthly data on the nominal M2 ruble money

stock, denoted mt ; consumer price index, denoted pt ; GDP indicator, denoted yt ; and

nominal ruble exchange rate against the U.S. dollar, denoted et .2 Figures depicting vari-

ables appear in Korhonen and Mehrotra (2007). We speciy long-run real broad money

demand in the Russian economy as

m p y e u

t t t t t −( ) = + + +β β π β1 2 3 ,

(1)

where all variables are in logarithms. Demand or real money balances is explained by

real income, infation (πt, the month-to-month change in the consumer price index), and

the exchange rate.3 We include the latter two variables to account or the opportunity

cost o holding money. Interest rates are generally not considered appropriate indicators

o monetary policy stance in Russia, given the low liquidity in the interbank markets.

Pantyushin and Cherdantseva (2007) suggest that no interest rate aects infation or

investment dynamics. The use o a government bond (GKO) rate as a long-run interest

rate is mitigated by the collapse o this market during the 1998 crisis and its subsequent

lack o liquidity. Moreover, the CBR can control the exchange rate level to a considerabledegree. As the Russian economy remains highly dollarized, the exchange rate potentially

has a large eect on money demand. The consumer price index and the nominal exchange

rate against the U.S. dollar are taken rom the International Monetary Fund’s (IMF) In-

ternational Financial Statistics (IFS) database. The nominal ruble stock comes rom the

CBR. The monthly GDP indicator comes rom Russia’s statistical agency, Rosstat, and

is based on output in ve core sectors o the economy.4 Most previous studies on Russian

money demand use industrial production to proxy income. Given the growing importance

o services and construction in the Russian economy, we eel the GDP indicator better

captures the dynamics o the overall economy.

The order o integration o the series has important implications or the chosen estima-tion approach. We tested or unit roots with the Ng–Perron test. The maximum number

or lags was set to 11, but the actual number o lags was determined by the Schwarz

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inormation criterion (lags were usually between 0 and 5). For real money, oil price, the

exchange rate, infation, and GDP, a unit root cannot be rejected or the series in levels,

whether we include as deterministic variables a constant or a constant and trend.5 There-

ore, treating the variables as integrated o order one, that is, nonstationary, would seem

prudent, though in principle, stationary variables can also be accommodated in vectorerror-correction (VEC) systems.

As our unit root tests suggest the series can be modeled as integrated o order one, it is

possible that the series are driven by common stochastic trends. I so, they are said to be

cointegrated. Thus, we preer the VEC specication, as it allows us to distinguish between

short-run and long-run relations. Theoretical relations, such as a money demand relation,

are typically dened in terms o the levels o the series, whereas a lack o cointegration

could lead to spurious regressions when the variables are integrated o an order higher

than zero. We test or cointegration utilizing the test proposed by Saikkonen and Lütkepohl

(2000), including a constant, linear trend, and seasonal dummies in the testing procedure,

because some series trend over time. The oil price is not included, as it only appears in

our estimated system as an exogenous variable. Table 1 reports the results.

For our benchmark postcrisis sample, the Saikkonen–Lütkepohl test suggests the exis-

tence o one cointegration relation, as a cointegration rank o zero can be rejected at the 1

percent level, and a rank o one cannot be rejected at any conventional level. As a robust-

ness test, we considered truncated samples 1999M2–2005M12 and 2000M1–2006M12,

respectively. These alternative samples do not change our nding o a cointegrating rank

o one, even at the 10 percent signicance level. We continue with the assumption that

a single cointegration relation exists among the system variables.

Estimation Results and Their Interpretation

As mentioned above, and like most recent research on money demand, we adopt a VEC

model. Our cointegration tests justiy our choice; they ound that the variables share

common stochastic trends that could plausibly be written in the orm o a money demand

relation (though this can only be tested in the context o system estimation). When we

omit the deterministic terms and include exogenous variables, a reduced-orm VEC

model can be expressed as

∆ Π Γ ∆ Γ ∆ x x x x B z B z ut t t p t p t p t p t = + + + + + + +

− − − − + −1 1 1 1 1 0… … ,

(2)

where p denotes the order o the VEC model. K is the number o endogenous variables.

xt = ( x1t , ..., xKt )′ is a (K × 1) random vector, and Γ i are xed (K × K ) coecient matrices. zt are unmodeled stochastic variables and are thereby assumed to be exogenous. Bi are

parameter matrices. The ut = (u1t , ..., uKt )′ is a K -dimensional white noise process with

E(ut ) = 0. When the variables are cointegrated, Π has reduced rank r = rk (Π) < K . It can

be expressed as Π = αβ′, where α and β are (K × r ) matrices that contain the loading

coecients and the cointegration vectors, respectively.

We estimate the model with ve lags in both the endogenous and exogenous variables.

Our choice or the lag length is based on tests or misspecication and stability. As the

observations or 1999M2–1999M7 are used as presample values, the eective estimation

sample runs rom 1999M8 to 2006M12. We include a constant, seasonal dummies, and

a linear trend as the deterministic variables in the system. The trend is restricted in the

cointegration relation. As the ruble-to-U.S. dollar exchange rate is a policy variable or

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Mah–Apil 2010 11

the CBR, we only include it as unmodeled (and restricted) in the cointegration relation.

It thus appears as one o the long-run determinants o money demand, without entering

the short-run dynamics o the estimated system.6 Oil price in rst dierences is included

as an exogenous variable. We estimate the cointegration relation by the simple two-step

(S2S) procedure, which eectively utilizes easible generalized least squares (FGLS).

The S2S estimator displays substantially better small-sample properties than does the

maximum likelihood (ML) estimator, as Brüggemann and Lütkepohl (2005) show. Thecointegration relation is estimated as (standard errors in parentheses):

m p y e t t t t t

−( ) =

( )

−

( )

−

( )

+1 725

0 324

4 593

2 392

0 954

0 135

0 010.

.

.

.

.

.

.π

00 001.

.

( )

+ ut

(3)

The estimated coecients have the signs that theory suggests. A higher level o out-

put increases the demand or real money, whereas a higher opportunity cost o holding

money leads to a all in money demand. The coecient on the infation rate is weakly

signicant, just above the 5 percent level. Its relatively high absolute value could refect

the limited investment opportunities in the Russian economy outside real assets, as Banerji

(2002) discusses. Our estimated income elasticity o money demand is higher than unity,

refecting increasing monetization o the Russian economy ater the 1998 crisis. In their

analysis o earlier surveys on money demand, Knell and Stix (2006) report that the aver-

age income elasticity o broad money or non–Organization or Economic Cooperation

and Development (OECD) countries is very close to unity, but the standard deviation o

elasticity observations tends to be quite high, ranging rom 0.36 to 0.53. Our estimate

or income elasticity is not ar o rom the results ound or other non-OECD countries.

Finally, including a short-run money market interest rate that could plausibly assume the

role o the own interest rate o broad money yields statistically insignicant estimates,

or its long-run and adjustment coecients.7 Thus, we omit it rom the nal model.

Our estimated cointegration relation remains relatively robust to changes in the chosenVEC order, as Table 2 shows. Testing with a one-month ruble-to-U.S. dollar orward

Table 1. Saikkonen–Lütkepohl cointegration test

Lagged Null TestSample levels hypothesis statistic

1999M2–2006M12 1 = 0 74.47*** = 1 24.59 = 2 5.43 = 3 0.041999M2–2005M12 1 = 0 61.84*** = 1 18.10 = 2 6.68 = 3 0.002000M1–2006M12 1 = 0 53.32*** = 1 22.02 = 2 11.20 = 3 0.09

Notes: Deterministic terms include a constant, a linear trend, and seasonal dummies or all systems.

*** signicance at the 1 percent level.

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T a b l e

2 .

E s t i m a t e d c o i n t e g r a t i o n v

e c t o r s w i t h a l t e r n a t i v e l a g l e n

g t h s , s p o t r a t e s , a n d f o r w a r d

c o n t r a c t s

M e t h o

d

E x c h a n g e r a t e

L a g s

β ( y )

S E ( y )

β ( π )

S E ( π )

β ( e )

S E ( e )

S 2 S

S p o t R U B / U S D

4

2 . 0 7 9

0 . 3 7 1

– 5 . 5 5 5

2 . 5 9 9

– 1 . 0 2 9

0 . 1 5 3

S 2 S


5

1 . 7 2 5

0 . 3 2 4

– 4 . 5 9 3

2 . 3 9 2

– 0 . 9 5 4

0 . 1 3 5

S 2 S


6

1 . 9 8 9

0 . 3 3 7

– 5 . 6 8 0

2 . 8 6 3

– 1 . 0 1 6

0 . 1 4 5

S 2 S


7

1 . 5 8 9

0 . 2 3 2

– 6 . 6 9 2

2 . 0 6 0

– 1 . 0 1 4

0 . 0 9 8

S 2 S

O n e - m o n t h f o r w a r d

6

1 . 2 6 8

0 . 2 7 6

– 1 7 . 2 7 5

3 . 2 1 5

– 1 . 0 9 0

0 . 0 8 9

N o t e : S E d

e n o t e s s t a n d a r d e r r o r s .

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Mah–Apil 2010 13

exchange rate oers additional insight, as the exchange rate sensitivity o the demand

or Russian domestic broad money may vary using dierent exchange rates. Moreover, a

orward rate measures exchange rate expectations, which should be important or the ruble

money-holding sector, especially during the postcrisis period, when increased condence

in the domestic currency has triggered dedollarization. To our knowledge, previous stud-ies have not used orward contracts to analyze Russian money demand.8 Table 2 reports

the results. A caveat here is that the estimation sample starts only in 2000M7, due to data

limitations (the orward data are available only rom 2000M1 onward) and signicant

outliers in the beginning o 2000. We nd that the orward exchange rate is statistically

signicant with the expected sign, and its coecient estimate alls very close to that o the

spot exchange rate in the benchmark estimation sample. However, our coecient estimate

or the infation rate now increases to –17.275. We continue with the benchmark specica-

tion o using the spot ruble-to-U.S. dollar exchange rate (level), as the sample is longer, but

note that the orward data bring about correctly signed and signicant coecient estimates

or the exchange rate in the Russian money demand equation.9 Furthermore, through the

covered interest rate parity, we are actually including the interest rate dierence between

Russia and the United States in the estimation, when we use the orward rate.

The short-run parameters o the VEC system are estimated by FGLS. Model reduction

was achieved by a procedure in which the parameter with the lowest t -value was checked

and possibly eliminated. The nal model only includes coecients with a higher t -value

than 1.67. This approach allows us to preserve degrees o reedom in our short sample

without removing any statistically signicant coecients rom the estimated system.

Table 3 reports the loading coecients, the entire short-run part o the system, and mis-

specication test results rom the model.

The adjustment coecients on the cointegration relation ect–1 in Table 3 are o interest,

as they indicate the possible adjustment o our system toward long-run equilibrium. Wend that the coecient on the equation or real money (–0.142) is statistically signicant

and negative, indicating that excess liquidity is corrected within our system. The speed o

adjustment is relatively rapid. Excess liquidity in the Russian economy results in infa-

tion pressure and higher real GDP, as indicated by the statistically signicant adjustment

coecients in the infation and output equations.10 For the short-run coecients, there is

eedback in the system between the money and price variables as they enter both equa-

tions. Similarly, we detect eedback between real money and output.

Misspecication tests, also reported in Table 3, include the Lagrange multiplier (LM)

test or autocorrelation, the Jarque–Bera test or nonnormality, and the multivariate vec-

tor autoregressive conditional heteroskedasticity (VARCH)-LM test or autoregressive

conditional heteroskedasticity (ARCH) eects in model residuals. None o these tests

suggest misspecication.11 The investigation o model stability is o special interest in

money demand examinations. A stable money demand relation is a prerequisite or a

meaningul specication o money growth targets, and could also be useul i concepts o

excess liquidity, such as monetary overhang, are considered. We test or system stability

by recursive eigenvalue tests, proposed by Hansen and Johansen (1999). The short-run

parameters are concentrated out based on the ull sample, and only the long-run part is

estimated recursively. The standard errors to construct the 95 percent condence inter-

vals are estimated based on the ull sample. The recursive eigenvalue remains relatively

stable during the maximum period available or investigation. Additionally, the τ-statistic

remains much smaller than the 5 percent critical value. The Chow orecast test, utiliz-ing bootstrapped p-values obtained by 1,000 replications, also suggests model stability.

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We test or a possible break date or every observation, commencing in 2003M2, which

corresponds to the longest available sample in our case. For no observation can we reject

parameter stability, even at the 10 percent level.12 We continue with the assumption that

the estimated model displays stability over time. Our results o system stability or the

ruble broad money stock contrast with Oomes and Ohnsorge (2005), and likely stem

rom the use o postcrisis data only in our estimation sample.13

To examine the orecasting perormance o our money demand system in terms o the

infation rate, we estimated the model until 2006M5 and created out-o-sample orecasts

or infation seven months ahead. For the exogenous oil price variable, the actual out-comes during the period 2006M6–2006M12 were imposed. Figure 1 shows the orecast

Table 3. Loading coecients, short-run part, and misspecication tests or

vector error-correction model

Equation D (m – p ) D y D p

ect –1 –0.142 0.233 0.044

(0.025) (0.025) (0.013)

D(m–p )t–1 0.279 — —

(0.079)

D(m–p )t–2 — — —

D(m–p )t–3 0.120 — —

(0.074)

D(m–p )t–4 –0.241 — 0.049

(0.079) (0.017)

D(m–p )t–5 — –0.210 —

(0.078)

D t–1 –0.234 — —

(0.049)

D t–2 — — —

D t–3 –0.193 — 0.041

(0.043) (0.011)

Δ t–4 –0.129 — 0.032

(0.046) (0.007)

Δ t–5 — 0.125 0.016

(0.037) (0.008)

Δπ t–1 — –2.636 –0.300

(0.517) (0.066)

Δπ t–2 — –2.230 –0.212

(0.583) (0.067)

Δπ t–3 0.814 — –0.303

(0.290) (0.078)

Δπ t–4 — — —

Δπ t–5 — — —

LM(5)=37.74 [0.77] LM(4)=34.20 [0.55] LM(1)=8.82 [0.45]

JB1=0.35 [0.84] JB2=0.40 [0.82] JB3=0.08 [0.96]

VARCH–LM(5)=199.28 [0.15]

Notes: Standard errors are in parentheses. The coecients or the infation rate are multiplied by 100.

The LM-test or autocorrelation is conducted at one, our, and ve lags. JB is the Jarque–Bera test or

nonnormality or three individual system equations. VARCH-LM is the multivariate ARCH-LM test

at ve lags. The p-values or test statistics are in brackets. Constant, seasonal dummy, and exogenous

variables are not displayed.

8/3/2019 MD in Russia 2010


Mah–Apil 2010 15

(bold dashed line), together with the actual outcome (bold solid line) and the condence

intervals (two thin dashed lines). All actual values all inside the condence intervals,

supporting our estimated system. Shortening the estimation sample by one month and

orecasting eight periods ahead, we nd that all the actual outcomes similarly remain

inside the condence intervals. The orecast in the end o the period (2006M12) again

alls quite close to the actual outcome.

As our estimated money demand system displays stability over time, deviations o the

money stock rom the long-run equilibrium could, in principle, provide inormation about

infation pressures in the economy. Svensson (2000) points out that this real money gap,

written as (mt – pt ) – (mt * – pt

*), is equivalent to the negative o the price gap in P* models

(see, e.g., Hallman et al. 1991; Tödter and Reimers 1994). Here, (mt * – pt

*) denotes the

equilibrium money stock obtained by substituting the actual outcomes or real money,output, the infation rate, and the exchange rate to the estimated long-run money demand

equation. We examine the orecasting perormance o the real money gap in terms o the

infation rate by testing or Granger causality, where causality is determined by a variable’s

ability to improve the orecasts o another variable. We include the infation rate (in rst

dierences to ensure stationarity) together with the real money gap and seasonal dummies

in the estimated system or the Granger causality test. This bivariate system, using one lag

as suggested by the Schwarz inormation criterion, passes tests or autocorrelation and

nonnormality, similarly to the estimated VEC model above.14 We nd that the null hypoth-

esis o Granger noncausality rom the real money gap to prices can be weakly rejected;

the test statistic is 3.505 ( p = 0.06). Thereore, the real money gap estimated within our

money demand system has some predictive power or uture infation.

For our system with a cointegrating relation, we implement the standard impulse

response analysis in the context o a structural VEC model, where contemporaneous

restrictions are used to identiy the system. The structural VEC model is expressed as

A x x x x Bt t t p t p t ∆ Π Γ ∆ Γ ∆= + + + +

− − − − +

* * *.

1 1 1 1 1… ε

(4)

The structural shocks εt are mutually uncorrelated. We assume that εt ∼ (0, I K ). In

Equation (4), Π* and Γ j* ( j = 1, ..., p – 1) are structural-orm parameter matrices. A is an

invertible matrix and allows or modeling o the instantaneous relations. The structural

shocks are related to the reduced-orm residuals by linear relations, specically

u A Bt t =

−1ε .

(5)

Figure 1. Out-o-sample infation orecast

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We identiy the model by considering restrictions on the parameter matrix B, with our

ordering o the variables specied as (m – p), y, π. In particular, we impose

B

=

* * *

** *

,0 00

(6)

where asterisks denote the unrestricted elements. Such an identication scheme eec-

tively amounts to a Cholesky decomposition o the variance–covariance matrix, with

the ordering o the variables specied as output, prices, and real money. Output is the

most rigid variable in our system, as it does not react within the same month to shocks in

prices or real money. In the model by Sims and Zha (2006), there are signicant adjust-

ment costs and inertia associated with real economic activity. Prices adjust slowly due to

menu costs, or because they are changed at exogenously determined random intervals,

as in Calvo (1983). Considering real money an indicator o monetary policy stance, the

noncontemporaneous eect o real money on output and prices is consistent with most

work on reduced-orm modeling o monetary policy. This real money stock is allowed

to react rapidly to any news about the economy, or indeed, any macroeconomic shocks

that aect real output and prices in our system.

Figure 2 illustrates impulse responses orty-eight months ahead. Hall 95 percent–

bootstrapped percentile condence intervals with 1,000 replications were used. In our

system, a permanent shock to real broad money leads to a statistically signicant and

permanent increase in the infation rate. This can be justied by considering the permanent

shock to real money as a permanently looser stance o monetary policy. This result is in

line with the nding that the system variables are integrated o order one; some shocks

should thus have permanent eects. The same real money shock leads to a permanentincrease in the level o GDP, as expected. As theory predicts, an infation shock leads to

a decline in the real money stock, with a statistically signicant eect. A shock to real

GDP leads to an increase in the money stock, which is in line with higher transaction

demand or broad money. Finally, the eects o permanent shocks stabilize in about

1.5 years in our system, supporting the idea that the sample is adequate or meaningul

inerence about long-run relations between the system variables.

Conclusion and Policy Implications

We have estimated dierent specications or money demand in Russia. To our knowledge,

such analysis in a VEC ramework has not been applied solely to data rom the period

ollowing the August 1998 nancial crisis. While this approach limited the available

data, it permitted us to concentrate on a period with a relatively homogenous monetary

policy regime. The CBR has maintained airly tight control o the nominal exchange rate,

whereas infation has decelerated. Nevertheless, the CBR has ailed repeatedly to meet its

infation targets. Infation seemed to plateau at around 10 percent in 2005, though quite

recently (spring 2007), urther declines have been registered.

We nd a stable money demand unction or Russia between the years 1999 and

2006. Demand or real ruble balances depends—as theory predicts—on income (prox-

ied by GDP) and the opportunity cost o holding money (proxied by infation and the

exchange rate). The estimated income elasticity o real money is somewhat higher thanthe average ound or other non-OECD countries (Knell and Stix 2006). This result is

8/3/2019 MD in Russia 2010


Mah–Apil 2010 17

likely explained by remonetization o the Russian economy during our sample period.

From a policy point o view, the nding o a stable money demand unction or domestic

currency is important, as it suggests that the domestic money stock can be used as an

inormation variable about developments in the real economy. This result contrasts toprevious literature covering the postcrisis era, such as Oomes and Ohnsorge (2005). We

nd that broad money shocks lead to higher infation and that deviations rom long-run

equilibrium ruble money stock provide inormation about uture infation. Another insight

o clear policy relevance is that exchange rate movements infuence the demand or real

money balances. Managing the ruble exchange rate can directly aect the remonetization

o the Russian economy. These results are probably relevant or other emerging market

countries where dollarization prevails.

The existence o a stable money demand unction at least partly vindicates the CBR’s

policy o linking money growth (in the orm o money growth orecasts) and infation

targets. O course, the practical implementation o such policy has been made more di-cult by the registered decline in velocity. I the growth in ruble money stock is driven

solely by remonetization, inspired by increased condence in domestic currency, it is

unlikely to lead to infationary pressures. In our model, we capture this development

with a linear trend, but we have the luxury o perorming our analysis ex post. Given that

our results also show that the ruble’s exchange rate remains an important determinant

o money demand, the close attention paid by Russia’s monetary authorities to the ruble

rate seems well justied.

Notes

1. Cointegration techniques have been previously used or Russia or similar or shorter estima-tion samples (e.g., Choudhry 1998; Oomes and Ohnsorge 2005; Rautava 2004).

Figure 2. Impulse responses

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2. When checking or the robustness o our results, we also use the one-month orwardruble/U.S. dollar exchange rate.

3. An alternative approach would be to replace the demand or real money balances with theratio o domestic currency to oreign currency deposits. This would lead to examining currencysubstitution in the Russian economy, which is an important phenomenon in itsel (or a recent

study, see Harrison and Vymyatnina 2007 and the reerences therein). At the end o 2007, the ratioo ruble time deposits to oreign currency deposits was approximately 4:1. We ocus instead onthe link between the ruble money stock, infation, and output, which are important to the conducto domestic monetary policy.

4. The ve core sectors are industrial production, retail trade, construction, transport, andagriculture.

5. For the rst dierences o our series, the null hypothesis o a unit root is always rejectedat the 1 percent level, with the exception o infation. The rapid disinfation may be behind thiscounterintuitive result. I we use the augmented Dickey–Fuller test, which has lower power insmall samples, the null hypothesis o Δπ having a unit root is rejected at the 1 percent level. (Thetest statistic is –13.079 when a constant and a trend are included.) Detailed results rom unit roottests are available rom the authors upon request.

6. Our approach is identical to that o Kalra (1999), who includes the state-owned banks’deposit interest rate as an unmodeled variable in the money demand cointegration relation or thetransition economy o Albania.

7. Similarly, in their long-run demand relation or the ruble broad money stock, Oomes andOhnsorge (2005) nd that the coecient on the deposit interest rate is insignicant.

8. Bonger (2001) uses data on the exchange rates o our major currencies against the U.S.dollar to investigate the t o a regression where uture spot rates are explained by data on currentorward contracts. He shows that when the exchange rate data are in dierences, the t o the modelis quite poor compared to estimations where the data are in the orm o nonstationary levels. Frootand Thaler (1990) provide an overview o the literature on the topic. For our purposes, the issue iswhether orward data provide similar estimates in the money demand system as the spot rate withthe perect oresight assumption.

9. We also tested or the inclusion o the nominal eective exchange rate, but our estimates

or the long-run money demand did not remain robust when this variable was used. The U.S. dol-lar remains the most widely used oreign currency in Russia in terms o economic transactions,savings accounts, and even in pricing.

10. The t -values o the adjustment coecients in the infation and output equations are 3.440and 9.244, respectively.

11. In contrast, including either the money market, renancing, or deposit interest rate leads tononnormality in the estimated system.

12. The results rom stability tests are available on request.13. Using an estimation period rom December 1996–December 2006, thereby including the

Russian crisis in the estimation sample, we cannot nd a single model that passes both the tests ormisspecication and stability. This holds or any tested lag length between 1 and 12.

14. In the LM test, there is very weak evidence o autocorrelation when our lags are used( p-value o 0.099).

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Bonger, P. 2001. Monetary Policy: Goals, Institutions, Strategies, and Instruments. New York:Oxord University Press.

Brüggemann, R., and H. Lütkepohl. 2005. “Practical Problems with Reduced-Rank ML Estima-tors or Cointegration Parameters and a Simple Alternative.” Oxford Bulletin of Economicsand Statistics 67, no. 5: 673–690.

Calvo, G. 1983. “Staggered Prices in a Utility-Maximizing Framework.” Journal of Monetary Economics 12, no. 3: 383–398.

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Choudhry, T. 1998. “Another Visit to the Cagan Model o Money Demand: The Latest RussianExperience.” Journal of International Money and Finance 17, no. 2: 355–376.

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Harrison, B., and Y. Vymyatnina. 2007. “Currency Substitution in a Dedollarizing Economy:The Case o Russia.” Discussion Paper 3/2007, Bank o Finland, Institute or Economies inTransition, Helsinki.

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To order reprints, call 1-800-352-2210; outside the United States, call 717-632-3535.

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