Money Demand in a High Inflation Period: Yugoslavia - CiteSeerX

“Money Demand in a High Inflation Period: Yugoslavia”

Max Gillman1 and Miroljub Labus 2

ABSTRACT Recent work has tested money demand within the cash-in-advance economy for moderate inflation countries. This paper extends these results by testing the demand for money as derived from a cash-in-advance economy for a high inflation country, Yugoslavia, over the 1994-1998 period during which currency substitution was pervasive. Using monthly data, the estimated equation includes the costs of alternative means of exchange to capture such inflation tax avoidance, and finds support for the model. The paper compares the results to the partial equilibrium approaches of the Cagan model and other empirically tested general equilibrium specifications of money demand. The results relate the interest elasticity of money demand to the degree of financial development, and allow for a distinction in this regard between developing and developed countries.

JEL: E13, E41, C13, O42 Keywords: money demand, Yugoslavia, credit services, cash-in-advance

1

Max Gillman, Department of Economics, Central European University, Nador utca 9, 1051 Budapest, Hungary; [email protected]. 2 Miroljub Labus, Deputy Price Minister of Yugoslavia; and School of Law, University of Belgrade, Gospodar Jevremova 13, 11000 Belgrade, Yugoslavia.

1.1 Introduction This paper presents and tests a cash-in-advance endogenous growth economy that employs a special case of the McCallum and Goodfriend (1987) exchange technology. From the model the money demand is derived and tested, for a high but not hyper, inflation period in Yugoslavia, with monthly data from 1994 to 1998. This provides an explanation of money demand in a country experiencing a high degree of currency substitution, by extending Gillman and Otto’s (1998) results, in which a similar cash-in-advance framework is used to explain money demand in the moderate inflation developed countries. Given the finding of empirical support for the model, the paper shows how the results interlink the interest elasticity and the degree of financial development. Easterly et al (1995), using a general equilibrium model for which the semi-interest elasticity may be rising or falling with inflation depending on asset substitutability, find for high inflation countries that there is a rising semi-interest elasticity as inflation goes up. However for Yugoslavia, Petrovic and Mladenovic (2000) find a falling semi-interest elasticity in a Cagan-type model. Gillman and Kejak (2000) find in their calibration, of a model similar to Gillman and Otto (1998), a slightly falling semi-interest elasticity and a rising interest elasticity with the inflation rate. The model of this paper is a human capital –only version of the Gillman and Kejak model, and from this we derive our model for estimation, and thereby expect to find a rising interest elasticity with the inflation rate. The paper’s empirical results support this for Yugoslavia, find rejection of an alternative constant elasticity model, and are linked to the degree of financial development by the general equilibrium setting. The paper captures currency substitution by including the cost of alternative exchange means, a general approach used by others for both moderate and high inflation economies (Aiyagari,

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Braun, and Eckstein, 1998, Frenkel and Mehrez, 2000, Ireland 1994, 1995, and Gillman, 1993). Our thesis is that this approach may also be useful in explaining a high-inflation, transition country, such as Yugoslavia, that is also characterized by financial deregulation and innovation. The application of a model with alternative exchange means requires some adaptation for a transition economy in which currency substitution is rampant. As with developed countries, included in money demand is the wage rate because of the cost of time in alternative exchange means, as in Goodfriend (1997), Gillman (1993), Dowd (1990), and Karni (1974). Also included is a time series for the productivity of the banking sector, as in Gillman and Otto (1998), in order to capture the cost of the banking activity that substitutes for inflation-taxed cash. These substitutes may be either the “checkable” interest-bearing deposits of mature financial sector economies, or the foreign exchange conversions of domestic money that are the dominant alternative exchange means offered by banks in less mature financial sector economies. This leads us to make an ad hoc special addition for countries with a less developed financial sector, in particular of including the exchange rate in the money demand function, as is a focus of Petrovic and Mladenovic (2000). With the nominal interest rate already included in the money demand, the inclusion of an exchange rate captures any additional costs of alternative exchange means due to changes in the inflation rate in the foreign country that issues the currency (eg. Germany, and the Deutch Mark) most used as a substitute means of exchange. Note that Frenkel and Taylor (1993), who study Yugoslavia for a period from 1980 to 1989, try including a variable to capture the cost of alternative exchange means, although they find it insignificant. However, Petrovic and Vujosevic (1996a) argues that the period of study in Frenkel and Taylor also includes hyperinflation and so includes a regime break that brings into question their results. We find the cost of alternative means to be a key part of the explanation.

2

We next discuss the Yugoslavian setting (Section 1.2), set up the model, derive the balanced growth rate and the money demand, and approximate this demand equation to derive our equation for estimation (Section 2). Following the description of the data (Section 3), we test the model, present the results, and also test an alternative model (Section 4). After drawing out some of the implications of the results (Section 5), We qualify and conclude (Section 6).

1.2 Yugoslavian money and banking experience The smaller of two recent hyperinflations occurred in the last quarter of 1989 until June 1990, with an acceleration in the monthly price rise from 200 to 300 percent. The economy fell back into hyperinflation at the end of 1991, lasting 24 months and peaking with a monthly price increase of 313 million percent. During the second “transition period” hyperinflation from 1992 to January 1994, war occurred over the breakup of the six republics that comprised the former Yugoslavia. Afterwards, in the Federal Republic of Yugoslavia (FRY) consisting of the states of Serbia and Montenegro, the income velocity of money continued an upward trend from March 1994 to the end of 1998, with considerable variation. The high inflation of this period apparently is due in part to deficit finance through money printing during a time of extensive military operations. Petrovic et al. (1999) explain how money supply increases explain the high inflation of the 1980’s and Petrovic and Vujosevic (1996b) find the same explanation for the inflation rates during the December 1990 to October 1993 period. The high inflation was met by substitution mainly to the use of foreign currency, for most consumers and small firms. However foreign currency denominated, non-interest bearing, bank accounts were used to some extend by a portion of large firms, in what is called a type of “payments” system. This system avoids the inflation tax and would not be desirable for inclusion in the money aggregate, in that we are studying the demand for the non-interest bearing currency of Yugoslavia, the dinar, plus any Yugoslavian dinar-denominated

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non-interest bearing accounts. Basically there appear to be no significant such non-interest bearing deposits, and for this reason we use the supply of dinars as our money aggregate. Modernization of the banking system in Yugoslavia has occurred very gradually. Reforms in 1962 and 1965 established a two-tier form. The central bank was dissolved of commercial bank functions and the commercial banks took over the government's portfolio of investment funds. Commercial banks intermediated “investment credits” for new enterprises, collected private savings, operated the deposits of enterprises and the government, and mediated in external payments. Banks became universal financial institutions with deposit, credit, and underwriting functions. However banks remained government owned and the central bank set interest rate ceilings and regulated credit rationing by the banks. Real interest rates often were negative as preferential credits were given to enterprises in the government's priority area of development. In 1989, banks were legally transformed into joint-stock companies and the government of a particular bank became its shareholder. At a time when trading in securities markets were not very developed, this reform was well short of open market privatization. Founders obtained large credits from their own banks often on preferential terms. New private banks started to emerge, segmenting the sector into "old", government-capitalized, banks founded before 1989 and “new” private banks founded after 1989 with mixtures of government capital and both domestic and foreign private capital. By the end of 1998, there were 46 “old” and 59 “new” banks. The old banks have significantly more non-performing loans, foreign debts, and frozen foreign exchange accounts as compared to the new banks. 1 A particular deregulation that occurred during the 1994-1998 period was the elimination of interest rate ceilings on deposit accounts, in December 1995. Still, the environment of high inflation

1

For details see Labus (1998).

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variability has left bank deposits little used. The puts the focus on whether the development of the banking sector, although still well below Western standards, enabled easier use of foreign exchange as opposed to currency use. If such banking development made the use of foreign currency easier in Yugoslavia, then this could well effect money demand. The 1994-1998 period is characterized by very high inflation, but not hyperinflation. In June 1999, one of the two states that comprise Yugoslavia, Montenegro, adopted a dual currency system under which the German Mark was introduced and allowed as a second official means of payment in addition to the Yugoslavian dinar. This change in monetary regimes creates a break in the data series for the Yugoslavian currency, and may indicate a regime shift, and so we choose the 19941998 period as the best period in terms of data for estimation. Over this period, the relevant variables exhibit stationarity (see Table 1B), and we find a money demand function well-explained by standard variables plus those for the finance sector that capture the cost of exchange substitutes, plus the exchange rate.

2. The Cash-in-advance Endogenous Growth Model 2.1 Representative Agent Problem Consider U to denote the discounted stream of log utility that depends on goods ct and leisure xt : (1)

∞

U = ∫ e− ρt [ln c (t ) + α ln x(t )]dt . 0

The agent buys a fraction, at ∈ (0,1] , of goods with fiat money supplied by the government and a fraction, 1 − a t , with credit that is produced the agent through time spent in credit services production. The agent's fiat use is thereby constrained through the Clower - Lucas (1988) type constraint:

5

M t = a tPtc t .

(2)

However in contrast to Lucas, in which at is exogenous, the fraction at is a choice variable of the agent. The government supplies fiat at a constant rate through lump sum transfers denoted by Vt ∈ R+ : (3)

Vt = σ M t . With ht indicating the stock of human capital index, with lGt indicating the time spent in

goods production, and with a linear production of goods given by ct = wlGt ht , then the time change in nominal fiat holdings equals the income from working, whPl , plus the lump sum fiat transfer t t Gt minus the expenditure on goods: (4)

M& t − whPl t t Gt −V t + Pc t t = 0. The other flow constraint is for human capital investment. With a Lucas (1988) -type

investment function, let the rate of growth of human capital be proportional to the raw, non-indexed, time spent in human capital accumulation. This time is the residual from the time endowment of 1 after accounting for leisure time, xt , time in goods production, lGt , and time in the production of credit services. Let lFt denote the time spent in credit service production per unit of goods production. The total time spent in financing exchange by credit equals lFt ct With AH ∈R++ , zero depreciation, and the human capital investment function is (5)

h&t = AH (1 − xt −lGt −l Ft ct ) ht . The technology of the credit services is one of a diminishing marginal product of labor in the

production of the share of goods that is bought with credit. With AFt ∈ R++ , and γ ∈ (0,1) , the credit services production function is (6)

1 − at = AFt ( lFt ht ) . γ

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Substituting in for at in the Clower constraint of equation (2), using the credit services technology of equation (6), the representative agent maximizes the following Hamiltonian with respect to ct , xt , lGt , lFt , and the stock variables M t and ht : Max ~ H = e −ρ t ( ln ct + α ln xt )

(

+ µt M t − 1 − AF (l Ft ht )γ  Pc t t

(7)

+ λt [ wPlt Gt ht − Pc t t + Vt ]

).

+φt  AH (1 − xt − l Ft ct − lGt ) ht  2.2 Equilibrium2 The first-order equilibrium conditions imply, dropping time subscripts, three key "nonneutralities" of stationary inflation. The marginal rate of substitution between goods and leisure is given by U c 1 + aR + wlF h = , Ux wh

(8)

so that it equals the ratio of the shadow price of goods to that of leisure. The shadow price of goods is 1 plus the average exchange costs, while that of leisure is the effective real wage. The comparative statics indicate that this rate rises as the inflation rate rises so that the agent substitutes from exchange to non-exchange, from goods to leisure. When the inflation rate increases there is also a loss of resources, as time is used up in credit services, that increases the marginal utility of real income and decreases both goods and leisure consumption. Leisure falls as long as the interest elasticity of money demand is less than the seignoirage-maximizing level of one (in absolute value), as Gillman et al (1999) show. The leisure increase causes a growth rate decrease. With g indicating the balanced-growth rate of consumption and of human capital, this growth rate can be expressed as the difference

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between the return on human capital, denoted by rH and equal in the model to AH (1 − x ) , and the rate of time preference ρ ≡ (1/ β ) − 1 : (9)

g = rH − ρ = AH (1 − x) − ρ .

An inflation rate increase causes an increase in leisure and a decrease in growth. The inflationgrowth effect is stronger per unit of inflation at lower inflation rates, when the goods to leisure substitution is strongest. As the inflation rate rises, the substitution is increasingly from fiat to the use of credit, in the purchase of goods, and increasingly less from goods to leisure. Besides a decrease in the equilibrium goods per unit of human capital, the inefficiently higher use of leisure, and the lower growth rate, the other non-neutrality is the increase in output of the credit services sector. The first-order condition with respect to the time usage in credit production yields an equation analogous to the first order condition of Baumol (1952) in which the marginal cost of fiat is set equal to the marginal cost of exchange credit: (10)

(

)

γ  (1 − a )(1/ γ )−1 . R = w γ A1/ F 

The marginal cost of credit, on the righthand-side of (10), is the ratio of the marginal labor cost to the marginal labor product. This equation indicates that the time in credit service production will rise as the inflation rate rises, and this result, from a similar model, is what Aiyagari et al (1998) find evidence for internationally. See related models in English (1999) and de Abrou Pessoa (2001). Gillman and Kejak (2000) show that this model’s exchange technology is a special case of the McCallum and Goodfriend (1987) “shopping time” economy. Instead of shopping time we model banking time with a production technology. By combining the credit services production technology of equation (6) with the cash constraint of equation (2), the effective banking time

2

Gillman, Kejak, and Valentinyi (1999) prove the existence and uniqueness of the equilibrium for a more general utility function, in a economy that differs slightly in its definitions, but has the same equilibrium conditions.

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lF h can be solved for in terms of consumption goods and real money balances as in McCallum and Goodfriend’s shopping time constraint. The only difference is that this implies a specific functional form based on the microfoundations of the production of credit services, as opposed to the general form in McCallum and Goodfriend. Also human capital enters this banking time function as well because of our endogenous growth setting. The advantage of our approach is that we get specific banking sector coefficients that enter the money demand function and help in a critical way to provide our explanation of money demand. In contrast, applications of McCallum and Goodfriend, as in Lucas (2000) and Goodfriend (1997), have no theoretical guide on the specification of the shopping time function and simply specify it so as to fit certain facts. In particular both of these papers choose to fit a –0.5 interest elasticity for all inflation rates. In our model, changes in the interest elasticity are critical to the money demand explanation, while constancy of the banking sector coefficients underlie the way in which the interest elasticity rises in magnitude as inflation rises. 2.3 Money Demand The Baumol (1952) condition also implies the money demand function, just as Baumol derived a money demand function from his equation. Implicit in Baumol's equation is a very specific assumption about the provision of credit services (the cost is proportional to money velocity), and this led to the constant, negative square root, interest elasticity of money demand. With a more general Cobb-Douglas type production function for the credit services, the interest elasticity depends on the coefficients of this production function. But it is more like a Cagan (1956) money demand function than a constant elasticity money demand function, because the interest elasticity rises in absolute value as the inflation rate increases. The money demand equation which we estimate follows from the Baumol- type equation (10). Solving for a, the inverse income (equals consumption) velocity of money is equal to

9

m / c = 1 − AF 1/(1−γ ) ( R / w )

γ /(1−γ )

(11)

,

and this can be approximated by using that 1 − x ; − ln x for x not close to zero: m / y = a0 + a1 ln R + a 2 ln w + a3 ln AF ,

(12)

where the implied parameter restrictions are a1 = −γ / (1− γ ) , a2 = γ / (1− γ ) and

a3 = −1/ (1 − γ ) , noting that a1 = −a2 . Here we substitute income for consumption because in the model they are equal. As an extension, we add below the exchange rate to capture the use of foreign currency as a particular substitute. The last part of the model, which as added without the more desirable specification of a general equilibrium two country model, is to add the exchange rate, similar to Petrovic and Mladenovic (2000), so as to include changes in the foreign inflation tax of the foreign exchange currency that substitutes for the domestic currency: m / y = a0 + a1 ln R + a 2 ln w + a3 ln AF + a 4 E ,

(13)

where E is an index of foreign exchange rates relative to the Yugoslavian dinar. For Yugoslavia, the Deutch Mark is the main substitute currency. We expect that a4 < 0 since a higher dep

3. Description of Data Data are monthly from 1994:3 to 1998:12, not seasonally adjusted. Table 1A reports summary first and second moment statistics. All series display stationarity at a 1% level of confidence. Unit root tests are presented in the Table 1B. Non-interest bearing money is approximated by currency holdings, from the official statistics provided by the National Bank of Yugoslavia (The NBY Bulletin, various issues). The nominal interest rate data exists in the form of the Money Market Rate from the Bulletin for 1994-1998, and from the private corporation Money Market for 1994-1998. Data on nominal interest rates

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required a second source because over the period from 1994:02 to 1995:12 the National Bank of Yugoslavia imposed a 9% ceiling on annual nominal interest rates. The banks officially complied with this restriction while informally charging additional commission fees that in fact increased nominal interest rates. For this reason the officially released figures on nominal interest rates are not marketbased for 1994-1995. The industry money market interest rate data from Money Market reflects the shadow market nominal interest rates over the period of regulation. As these differ only slightly from the officially released figures on money market interest rates since January 1996, we use the industry data over the entire period for consistency. The exchange rate series is the “Black Exchange Rate” series, an index of foreign exchange rates published also 1in the Bulletin . Also found there is the index of industrial production that we use for our real output variable Y. Data on monthly wage rates in the economy as a whole (Average Net Wage, All Sectors) and the Banking sector particularly (Average Net Wage, Banking), as well as the Industrial Output Index and the Retail Price Index are regularly published by the Federal Statistical Office in the Index Monthly Review and The Statistical Index , various issues. Measures for real variables are obtained by deflating nominal values with the price level series based on consumers price index (P=CPI). Monthly data in Figure 1 show net real profits and real wages for the banking sector from January 1994 to December 19983 . There is a coefficient of correlation is 0.41 between the nominal monthly series, and one of 0.36 when deflated. Following Lowe (1995) and Gillman and Otto (1998), we use the real wage as a proxy for productivity in the banking sector in the money velocity

3

See Bulletin of the NBY, various issues, table 1A, column 12.

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estimation. We include this variable to capture the cost of providing credit services that allow for avoidance of the inflation tax.

Figure 1. Productivity Proxies in the Banking Sector

1000

1984 Dinars

500 600

0

500

-500

400

-1000

1984 Millions of Dinars

1500

300 200 100 1995

1996 Real Wage Rate

1997

1998 Real Profit

4. Empirical Results Here in a single equation model we estimate the inverse velocity, defined as the current dinar cash supply divided by the price index, denoted as CP, divided by the index of industrial production, Y, or (CP/Y)t . The inverse velocity is hypothesized to depend on the real wage in Finance, defined as the nominal wage rate in Finance divided by the price index, (Wf/P), as a proxy for the productivity shift parameter in the credit services production function. This representation of the Cash-Credit model implies that inverse velocity also depends on the nominal interest rate, R, and the real wage, defined as the nominal economy-wide wage rate divided by the price index, W/P. With time subscripts, the estimation equation is (14)

(CP/Y)t = a0 + a1*log(Rt) + a2*log(W/P)t + a3*log(Wf/P)t + ut As noted above, all variables (CP/Y, log (R), log(W/P) and log(Wf/P) ) are integrated of

order zero. Table 2 reports the OLS results, where S1 to S12 are seasonal dummy variables. All estimated coefficients are significant and have the signs predicted by the theory of Section 2, equation (12). These are that the effect of the nominal interest rate is negative, a10, as an increase in the cost of time makes credit production more expensive and induces substitution towards money; that the effect of the real wage in finance is negative, a3 >0, as an increase in the productivity of credit services causes substitution from money to exchange credit. However there is a problem of serial autocorrelation. Table 3 reports the results with an AR(1). This eliminates the serial correlation problem and the coefficient estimates again have the signs as expected. The coefficient estimates appear robust to this adjustment although it somewhat corrupts statistical significance of a1 coefficient. Testing the parameter restriction a1 = - a2 of the theory in equation (12), with a Wald test, but find that we cannot accept the restriction. In an investigation of an alternative error process, we find stronger results. Table 4 reports the results if the serial correlation error is modeled by a moving average process, MA(1), instead of the AR(1) process. The results support the model more strongly with the theoretically expected signs, high t-statistics, and with the magnitudes of the coefficients changing little across all three specifications, although the test of the equivalence of the magnitudes of the a1 and a2 coefficients is still not accepted. As an extension to the basic model, we consider the Petrovic and Mladenovic (2000) concept of adding the rate of change of the shadow exchange rate and test the model of equation (13). Table 5 reports these results. Again the signs as expected, with robustness of the coefficient estimates, and with an increased significance in terms of higher t-statistics for every variable. This appears to be the strongest model, implying not only the importance of the productivity of the banking sector, for handling foreign currency as a substitute to domestic currency, but also the price of using foreign currency in terms of its own foreign inflation tax.

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4.1 Tests of Alternative Functional Forms From a functional form point of view the Cash-Credit model is a special case of semi-log demand for money function. Its uniqueness is due to a distinct property of the dependent variable (CP/P) which is a level variable, while all explanatory variables are logarithms. The more standard functional form is the constant interest elasticity, log-log format. Following the set-up of this alternative model as in Gillman and Otto (1998), we test the following equation: (15)

log(CP/Y) t= b0 + b1*log(Rt)+ b2 *log(Yt) + vt

Note that we expect b1 to be negative as it estimates the interest elasticity of money demand. The magnitude and sign of b2 is ambiguous. A unitary income elasticity, as theory in Section 2 suggests, implies that b2 is zero. An income elasticity less than one implies that b2 is negative. It is assumed that the Cash-Credit model is correctly specified, which means that it is considered as the nullhypothesis (H0) in the test. The alternative model H1 is defined alongside the functional form of the above written standard demand for money model. The J-test proposed by Davidson and MacKinnon provides one method of choosing between two non-nested models. The idea is that if one model is the correct model, then the fitted values from the other model should not have explanatory power when estimating that model. The standard demand for money model is estimated over the sub-period 1994:3 to 1998:12. The estimated parameters given in Table 6 are statistically significant. However the DW statistic is very low. It is not possible to improve it by adding autoregressive error terms. That indicates mispecification of the model’s dynamics and that the model does not reflect the underlying data generation process. The fitted values from model H1 is then added to the Cash-Credit model and the augmented model is estimated again. Estimates are reported in Table 7. The t-statistics on the fitted values

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(referred as the STANDARD_MODEL variable bellow) is asymptotically distributed as N(0,1) under the null-hypothesis H0. The fitted values are not statistically significant which leads to rejection of the alternative model H1. That means that the Cash-Credit model better suits the data generating process than the standard demand for money model. The conclusion can be supported by the reverse test. In that case the standard demand for money model is treated as the null-hypothesis, while the Cash-Credit specification of demand for money serves as the alternative. Results of the J-test are reported in Table 8. The fitted values obtained from the Cash-Credit model are statistically significant. It means that the null-hypothesis is rejected in favor of the alternative.

5. Interest Elasticity, Seigniorage, and Financial Development An interesting feature of the model of Section 2 and the empirical results is that they form a type of general equilibrium version of the constant semi-interest elasticity money demand function. The interest elasticity of the model is − γ (1 − γ )  (1 − a ) ( a )  and the semi-interest elasticity is − γ (1 − γ )  (1 − a ) ( aR )  . The approximation of the model makes the interest elasticity of the estimated equation similarly equal to γ (1 − γ ) / a and the semi-elasticity equal to − γ (1 − γ )  / ( aR ) . The interest elasticity is not constant, as in the constant-elasticity partialequilibrium “log-log” model often used for developed countries with mixed success, (see for example, Hoffman, Rasche, and Tieslau, 1995, for a success, and Kuttner and Friedman, 1992, for a finding of no cointegration). Rather, as in the Cagan model, the interest elasticity increases with the inflation rate through the term, (1 − a ) / a in the Section 2 model, or the term 1/ a in the empirically estimated approximation of the model, since ∂a /∂ R < 0 as can be seen from equation (10).

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The constant part of this general equilibrium type of semi-elasticity is the term γ / (1 − γ ) . Unlike the free, semi-elasticity, parameter "α " in the original Cagan model, the γ parameter is a production coefficient that indicates the degree of diminishing returns to labor in the credit services sector. Perhaps a more useful interpretation of γ is that it is also the share of the real value labor income out of the real value of credit services output. To see this requires a small modeling extension that Gillman, Kejak, and Valentinyi (1999) detail, and that we show in the Appendix. The derivation of our empirical model directly from the theoretical model makes it so that the estimation of parameters of the model is in effect an estimation of γ , given in Table 9 below. TABLE 9: Estimated Coefficients of Equations (12) and (13); the Impliedγ Theoretical Value (Section2) Estimated (12) without Exch Rt Estimated (13) with Exch Rt Range

a1 ; γˆ −γ / (1 − γ ) ; γˆ

a2 ; γˆ γ / (1− γ ) ; γˆ

a3 ; γˆ

−1/ (1 − γ ) ; γˆ

-0.625; 0.86

15.45; 0.94

-3.25; 0.69

-0.445; 0.82

21.50; 0.96

-3.18; 0.69

(0.69, 0.96)

The results show that calibrating γ from the empirical estimates, and using all three parameters that indicate a value for γ according to the model, gives a range between 0.69 and 0.96. This is well above the value of γ estimated by Gillman and Otto (1998) for Australia, of 0.21, and of 0.26 that they estimate for the US in a revision/extension of the paper. Viewing γ as the share of labor in credit services, we would expect a higher share of labor in a less developed, or transition, country as compared to a developed country. Therefore the results appear consistent with the characterization of Yugoslavia as a developing country. The implied seigniorage in the model is similar to the results in Eckstein and Leiderman (1992). They find for a high inflation period for Israel that seigniorage as a percent of income was

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stable at different inflation rates, rather than showing a Laffer curve pattern as the Cagan model implies. They explain such a pattern for seigniorage with a general equilibrium, money-in-the-utility function model. In the model, they find that seigniorage initially rises and continues rising but at a very slight rate, once the inflation rate is above around 10%. This stability of the seigniorage they propose well-explains the Israeli high inflation experience. Our model of this paper has similar features in that seigniorage rises initially at a fast rate, and then at ever decreasing rate. Consider that seigniorage as a percent of output is defined in the Section 2 model as Rm / c . Since y = c , and m / c = a , the percentage seigniorage is simply expressed as Ra . The change in this with respect to inflation is found using equation (11) to be simply ∂ ( aR ) / ∂R = ( a − γ ) / (1 − γ ) , and a falls at a decreasing rate with the inflation rate in the model. This is another dimension, or consequence, of the rising interest elasticity with inflation. While the seigniorage data is not available to us for Yugoslavia, because there is not available a monthly GDP series, the type of rising and then relatively stable seigniorage that Eckstein and Leiderman find is also implicit in our model, and in our results of a rising interest elasticity for Yugoslavia. In terms of the level of the interest elasticity, Gillman and Kejak (2000) show through calibration that a lower γ implies a larger magnitude of the interest elasticity, − γ (1 − γ )  (1 − a ) ( a )  . The high value found for Yugoslavia of γ in our estimation implies that the money demand has a relatively small magnitude of the interest elasticity, and so is relatively inelastic, and that the static welfare cost of inflation (such as banking sector resources used up in avoiding the inflation tax) is not high relatively . But Gillman and Kejak (2000) also show that a less interest elastic money demand also means a higher decrease in the balanced growth rate when

17

inflation goes up.4 This suggests that there is a larger magnitude of the marginal negative effect of inflation on growth in Yugoslavia than in similar high-inflation countries with more developed financial sectors.

6. Qualification and Conclusion The model here specifies the agent’s utility as being defined over goods and leisure and constrained by a nominal income constraint, by a transaction constraint, and by a human capital investment constraint. In the income constraint money loses interest as it is held from period to period, thereby giving rise to the foregone interest cost of holding money. This constraint is ubiquitous to general equilibrium money models, in particular the money-in-the-utility function (MIUF) class, the overlapping generations class, the variants of cash-in-advance models, and the shopping-time/transaction-cost models of the McCallum and Goodfriend (1987) variety or Bansil and Coleman (1996) class. The second constraint is an exchange technology constraint, typically called the Clower-type constraint, whereby money is set equal to (or allowed to exceed in its inequality form) the purchases made with fiat. The exchange constraint is the primary distinguishing feature of the model relative to related classes of monetary models. The MIUF models lack any such second constraint and as a result their money demand functions end up being functions of income and the interest rate because of foregone interest incurred in the income constraint, giving a version of the standard empirical specification. However calibration of such a money demand requires setting a taste parameter for money so as to give the empirically desired interest elasticity of money demand. The money demand of the shopping-time/transaction-cost models requires specifying the parameters of the unknown transaction cost function again so as to give the desired

4

Khan and Senhadji (2000) show data panel OECD evidence of a non-linear negative effect of inflation on growth, whereby the marginal negative effect is higher for low inflation countries than for high inflation countries.

18

interest elasticity (Goodfriend, 1997, and Lucas, 2000). 5 Equilibrium money demand in the overlapping generations model is tenuous when capital is introduced and not usually the basis of empirical investigations. The exchange constraint of this economy is a special case of the shopping-time model, where shopping time is replaced by time allocated to credit service production. By explicitly specifying the production technology for the credit services, the money demand depends on the underlying parameters of this production function instead of on taste and general transaction cost parameters. And the productivity of the credit services sector explicitly enters the money demand function as well. In contrast to the shopping time models, this enables the money demand function to capture time series shifts in financial sector technology due to deregulation and innovation. The model also includes human capital. This allows the real wage to vary across sectors in accordance with the effective human capital in each sector, even with a constant real wage rate for a unit of homogenous labor. It also means that high inflation in the economy causes lower growth, the more so the higher is the credit sector coefficient γ , which was found to be relatively high for Yugoslavia. The monthly data is restricted to the 1994:3-1998:12 period because of hyperinflation before the period and because of the introduction of the German mark as a dual currency in 1999, which may have created a regime shift. This short time period is the main qualification of the paper’s results. The dual currency system would be worth further study in itself and to see whether the data period can be extended. Another qualification is that the index of industrial output which we use for output; it is not perfectly correlated with GDP, which is not available on a monthly basis as is typically the case for the transition countries. Some preliminary work has been done that tries to get a closer measure of GDP from the index and this may also be a useful avenue. With these

5

See Walsh (1998) for a description of MIUF, shopping-time, and standard cash-in-advance economies.

19

qualifications, the application of the model to a transition country, the new Yugoslavia, shows significant success, and might encourage application to other transition countries. The results, of a low level of the interest elasticity and of a rising interest elasticity with the rate of inflation, together imply that the high inflation of Yugoslavia causes a very significant negative effect on growth. This is because of both the cumulative impact of the high level of the inflation rate, and because of the less developed financial sector. Each marginal increase in inflation is more harmful to growth, in such a transition economy as Yugoslavia, because the inflation tax cannot be avoided as easily as in a country with a highly developed financial sector.

APPENDIX: Interpretation of credit services technology coefficient The credit services sector can be made explicit, as Gillman and Kejak (2000) show. In this approach, the consumer pays an explicit price for the credit services, say PF in nominal terms, and receives an explicit nominal profit, say Π , or π ≡ Π / P in real terms, that is returned to the consumer who also acts as the banking agent. This gives all of the same equilibrium conditions as in the Section 2 model, but now we also can derive the prices and profit. In particular, the profit can be expressed as (16)

π = ( PF / P) (1− a ) − wlF h ,

and it maximized by the bank firm with respect to labor hours lF h and subject to the production technology found in equation (6). This gives the first-order condition that PF / P = R . With the payment for credit services and the return to the consumer of bank profit, the consumer problem is slightly changed so that the equilibrium condition (10) becomes instead

(

)

( 1/γ ) −1 . PF / P = w γ A1/F γ  (1 − a )

20

The last two equations combine to give back equation (10), which together with equations (6) and (16) imply that γ = wlF h ( PF / P )(1 − a ) ; 1 − γ = π / ( PF / P )(1 − a )  ; so that γ and 1 − γ are the constant shares of labor wages and profit in output, similar to CobbDouglas type fixed shares of labor and capital income.

References Aiyagari, S. Rao, R. Anton Braun, and Zvi Eckstein, 1998, “Transaction Services, Inflation, and Welfare,” Journal of Political Economy, vol. 106, no. 6, 1274-1301. Bansil, Ravi, and Wilbur John Coleman, 1996, “A Monetary Explanation of the Equity Premium, Term Premium, and Risk-Free Rate Puzzles”, Journal of Political Economy, 104:1135-1171. Baumol , William J., 1952, “The Transactions Demand for Cash: An Inventory -Theoretic Approach”, Quarterly Journal of Economics, 66: 545-66. Cagan, Phillip, 1956, “The Monetary Dynamics of Hyperinflation”, In: M. Friedman, ed., Studies in the Quantity Theory of Money, University of Chicago Press, Chicago. De Adreu Pessoa, Samuel, 2001, “Welfare Characterization of Monetary-Applied Models and Three Implications”, University of Pennsylvania CARESS Working Paper #01-12. Dowd, Kevin, 1990, “The Value of Time and the Transactions Demand for Money,’ Journal of Money, Credit and Banking, 22: 51-64. Eckstein, Zvi, and Leonardo Leiderman, 1992, “Seigniorage and the welfare cost of inflation,” Journal of Monetary Economics, 29 (3): 389-410. English, William B., 1999, “Inflation and finance sector size”, Journal of Monetary Economics, December, 44(3): 379-400. Friedman, B.M., and K.N. Kuttner, 1992, “Money, Income, Prices, and Interest Rates”, American Economic Review, 82, No.3 (June): 472-492. Frenkel, Michael, and Gil Mehrez, 2000, “Inflation and Resource Misallocation”, Economic Inquiry, October, 38(4): 616-628.

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Frenkel Jacob A., and Taylor Mark P., 1993, “ Money Demand and Inflation in Yugoslavia, 19801989”, Journal of Macroeconomics, Vol. 15, pp.455-481. Gillman, Max, 1993, “The Welfare Cost of Inflation in a Cash-in-Advance Economy with Costly Credit”, Journal of Monetary Economics, February, 31(1): 97-115. Gillman, Max and Glenn Otto, 1998, “The Effect of Financial Innovation on the Demand for Cash: A Test of the Cash-in-Advance Model with Costly Credit”, CASE-CEU Working Papers Series No. 17, Warsaw. Gillman, Max, and Michal Kejak, 2000, “Modeling the Inflation-Growth Effect”, Central European University Department of Economics Working Paper WP7/2000. Gillman, Max, Michal Kejak, and Akos Valentinyi, 1999, “Inflation, Growth, and Credit Services”, Institute of Advanced Studies Transition Series Working Paper No.13, Vienna. Goodfriend, Marvin, 1997, “A Framework for the Analysis of Moderate Inflations,” Journal of Monetary Economics, 39: 45-65. Hoffman, D.L., R.H. Rasche, and M.A. Tieslau, 1995, “The Stability of Long-Run Money Demand in Five Industrial Countries”, Journal of Monetary Economics, 35, no.2 (April): 317-339. Ireland, Peter N., 1994, “Economic Growth, Financial Evolution, and the Long Run Behavior of Velocity”, Journal of Economic Dynamics and Control, 18(3-4) May-July, pp. 815-848. Ireland, Peter N, 1995, “Endogenous Financial Innovation and the Demand for Money”, Journal of Money, Credit and Banking, 27(1) February, pp. 107-123. Karni, Ed, 1974, “The Value of Time and Demand for Money,” Journal of Money, Credit and Banking, 6: 45-64. Khan, Mohsin S., and Abdelhak S. Senhadji, 2000, “Threshold Effects in the Relationship Between Inflation and Growth”, IMF Working Paper, June. Labus Miroljub, 1998, “Financial Sector in Yugoslavia” in Pitic, G. ed. Challenges and Opportunities for the Economic Transition in Yugoslavia, International Conference Proceedings, Belgrade, 1998, USAID, Economics Institute & Chesapeake Associates, pp.32-48. Lowe, Phillip, 1995, “Labour -Productivity Growth and Relative Wages: 1978-1994”, Reserve Bank of Australia Discussion Paper No. 9505. Lucas, Robert E., Jr., 1988, “Money Demand in the United States: A Quantitative Review”, Carnegie-Rochester Conference Series on Public Policy, 29: 169-172. Lucas, Robert E., Jr., 2000, “Inflation and Welfare”, Econometrica, Vol. 64, No. 2, pp. 247-274.

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McCallum, Bennett T., and Marvin S. Goodfriend, 1987, “Demand for Money: Theoretical Studies”, in New Palgrave Money, J. Eatwell, M.Millgate and P.Newman, eds., Macmillan Press, New York. Petrovic, Pavle, and Zorica Mladenovic, 2000, “Money Demand and Exchange Rate Determination under Hyperinflation: Conceptual Issues and Evidence from Yugoslavia”, Journal of Money, Credit, and Banking, 32(4): 785-806. Petrovic, Pavle, and Zorica Vujosevic, 1996a, “Comment on Frenkel and Taylor’s “Money Demand and Inflation in Yugoslavia, 1980-1989”, Journal of Macroeconomics, Vol. 18, No. 1, pp. 177-185. Petrovic, Pavle, and Zorica Vujosevic, 1996b, "The Monetary Dynamics in the Yugoslav Hyperinflation of 1991-1993: The Cagan Money Demand," European Journal of Political Economy; 12(3), November 1996, pages 467-83. Petrovic, Pavle, Zeljko Bogetic, and Zorica Vujosevic, 1999, "The Yugoslav Hyperinflation of 1992-1994: Causes, Dynamics, and Money Supply Process", Journal of Comparative Economics, 27: 335-353. Walsh, Carl E., 1998, Monetary Theory and Policy, The MIT Press, Cambridge.

23

Table 1: 1994:2-1998:12 Data A. Means and Standard Deviations of Variables Variable Mean Standard deviation Levels C/P: Real Cash* 747,749 222,299 Pi: Inflation** 2.99 3.42 Y: Industrial output*** 64.767 8.805 R: Interest rate**** 6.935 5.622 W/P: Real wage 184.773 36.022 rate***** Wf/P: Real wage rate 351.831 76.245 in the financial sector******

Mean

Standard deviation

Logarithms 6.563 -3.779 4.161 1.403 5.193

0.359 1.234 0.137 1.177 0.254

5.834

0.259

* C is measured in thousands of current 1994 dinars, , the price series P is normalized such that the average price level in 1994 is set to unity; ** percentage; ***indices of physical output set to 100 in 1992; ****R is a series of monthly money market nominal interest rate; ***** the economy-wide average real wage rate in constant 1994 prices; ******the financial sector average wage rate in constant 1994 prices.

B. Order of Integration in Data Series Augmented Dickey-Fuller Unit Root Test (with a constant, no trend) Sample 1994:2 to 1998:12 ADF statistics Lags Order of integration Real cash balances over real income -3.7670** 1 I(0) -3.7061** 2 I(0) -3.8413** 3 I(0) Logarithms of nominal interest rate -9.4391** 1 I(0) -8.4461** 2 I(0) -8.4225** 3 I(0) Log of real wage rate in the whole economy -15.0271** 1 I(0) -17.4176** 2 I(0) -19.7430** 3 I(0) Log of real wage rate in the banking sector -16.3932** 1 I(0) -17.4107** 2 I(0) -18.4313** 3 I(0) *MacKinnon critical values for rejecting hypothesized unit root

1% critical value** 5% critical value* 10% critical value

-3.5437 -2.9109 -2.5928

24

Table 2: Cash-Credit Model in High Inflation Dependent Variable: CP/Y Method: Least Squares Date: 06/29/99 Time: 08:25 Sample: 1994:03 1998:12 Included observations: 58 White Heteroskedasticity-Consistent Standard Errors & Covariance Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(R) LOG(W/P) LOG(WF/P) C S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 @TREND(94:2)

-0.601948 17.28746 -3.278411 -54.24214 -1.811404 -2.178335 -2.454667 -2.629421 -2.477651 -0.545384 -2.054788 -1.967188 -3.301598 -2.450535 -2.971356 -0.071463

0.238829 2.961224 1.359003 13.47437 1.109258 1.224115 0.796114 0.934114 0.840333 0.899402 0.828684 1.136132 1.015301 0.948951 1.132023 0.020932

-2.520409 5.837943 -2.412364 -4.025579 -1.632987 -1.779518 -3.083312 -2.814881 -2.948416 -0.606385 -2.479580 -1.731478 -3.251841 -2.582361 -2.624820 -3.413974

0.0156 0.0000 0.0203 0.0002 0.1099 0.0824 0.0036 0.0074 0.0052 0.5475 0.0172 0.0907 0.0023 0.0134 0.0120 0.0014

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

0.827869 0.766394 1.396750 81.93827 -92.31861 1.136783

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

11.63097 2.889859 3.735124 4.303522 13.46669 0.000000

25

Table 3: Cas h-Credit Model in High Inflation With Serial Correlation Correction Dependent Variable: CP/Y Method: Least Squares Date: 06/29/99 Time: 08:30 Sample: 1994:03 1998:12 Included observations: 58 Convergence achieved after 11 iterations White Heteroskedasticity-Consistent Standard Errors & Covariance Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(R) LOG(W/P) LOG(WF/P) C S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 @TREND(94:2) AR(1)

-0.502994 16.89357 -2.683092 -55.87559 -1.875461 -2.868689 -2.776957 -2.781662 -2.548966 -0.646595 -2.046104 -2.003577 -3.319098 -2.473958 -3.067815 -0.068569 0.479588

0.344635 3.103933 1.383233 12.53423 0.912510 0.863459 0.897565 0.873909 0.848166 0.926606 0.955478 1.139528 0.994299 0.884379 0.888872 0.029481 0.148468

-1.459497 5.442635 -1.939725 -4.457841 -2.055277 -3.322324 -3.093878 -3.183010 -3.005269 -0.697810 -2.141447 -1.758251 -3.338128 -2.797397 -3.451357 -2.325918 3.230244

0.1520 0.0000 0.0593 0.0001 0.0463 0.0019 0.0036 0.0028 0.0045 0.4892 0.0382 0.0862 0.0018 0.0078 0.0013 0.0250 0.0024


0.881723 0.835567 1.171849 56.30241 -81.43697 1.985263

Inverted AR Roots


11.63097 2.889859 3.394378 3.998301 19.10281 0.000000

.48

26

Table 4: Cash-Credit Model Serial correlation error is modeled by MA(1) process Dependent Variable: CP/Y Method: Least Squares Date: 05/29/00 Time: 17:22 Sample: 1994:03 1998:12 Included observations: 58 Convergence achieved after 9 iterations White Heteroskedasticity-Consistent Standard Errors & Covariance Backcast: 1994:02 Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(R) LOG(W/P) LOG(WF/P) C S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 @TREND(94:2) MA(1)

-0.625304 15.44904 -3.248787 -45.12913 -1.539333 -2.733703 -2.605682 -2.791844 -2.538985 -0.623840 -1.991803 -1.962926 -3.225994 -2.429919 -2.845166 -0.058501 0.609823

0.312888 2.765650 1.338487 11.27335 0.915237 1.086694 1.137631 1.053872 1.005487 1.037890 1.046200 1.302316 1.177097 1.069354 0.946709 0.026918 0.109999

-1.998494 5.586044 -2.427207 -4.003169 -1.681896 -2.515614 -2.290445 -2.649132 -2.525130 -0.601066 -1.903845 -1.507258 -2.740635 -2.272324 -3.005324 -2.173309 5.543889

0.0523 0.0000 0.0197 0.0003 0.1002 0.0159 0.0272 0.0114 0.0155 0.5511 0.0640 0.1394 0.0090 0.0284 0.0045 0.0356 0.0000

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Inverted MA Roots

0.871791 0.821758 1.220061 61.03049 -83.77542 2.035711


11.63097 2.889859 3.475015 4.078938 17.42438 0.000000

-.61

27

Table 5: Cash-Credit Model in High Inflation with the price of exchange substitute ( The rate of change of shadow exchange rate DLOG(EXCH(-1)) ) Dependent Variable: CP/Y Method: Least Squares Date: 05/30/00 Time: 21:39

Sample: 1994:03 1998:12 Included observations: 58 White Heteroskedasticity-Consistent Standard Errors & Covariance Variable

Coefficient

t-Statistic

Prob.

LOG(R) -0.444822 LOG(W/P) 21.49938 LOG(WF/P) -3.176214 DLOG(EXCH(-1)) -0.126757 @TREND(94:02) -0.094412 C -76.25609 S2 -1.899041 S3 -3.418104 S4 -2.285567 S5 -2.421650 S6 -2.516105 S7 -0.567025 S8 -2.394294 S9 -2.193751 S10 -3.696635 S11 -2.714591 S12 -3.515603

0.178915 -2.486219 1.868656 11.50527 1.256841 -2.527142 0.017906 -7.078998 0.016150 -5.845940 7.985978 -9.548748 1.020890 -1.860181 0.740007 -4.619018 0.798417 -2.862622 0.743593 -3.256687 0.708918 -3.549221 0.798619 -0.710007 0.709917 -3.372640 0.930597 -2.357358 0.787995 -4.691191 0.743814 -3.649555 1.038309 -3.385892

0.0171 0.0000 0.0155 0.0000 0.0000 0.0000 0.0700 0.0000 0.0066 0.0023 0.0010 0.4817 0.0016 0.0233 0.0000 0.0007 0.0016



0.903505 0.865848 1.058462 45.93398 -75.53453 1.791582

Std. Error

11.63097 2.889859 3.190846 3.794769 23.99321 0.000000

28

Table 6: Standard Model of Demand for Cash Balances Dependent Variable: LOG(CP/Y) Method: Least Squares Date: 06/29/99 Time: 18:25 Sample: 1994:03 1998:12 Included observations: 58 White Heteroskedasticity-Consistent Standard Errors & Covariance Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(R) LOG(Y) @TREND(94:02) C S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

-0.147809 1.948767 -0.002638 -4.980825 -0.293480 -0.677890 -0.507326 -0.518692 -0.445698 -0.141386 -0.182710 -0.448742 -0.697935 -0.552962 -0.589140

0.032140 0.541571 0.003833 2.099171 0.158401 0.196260 0.193049 0.193654 0.168036 0.165398 0.149090 0.174993 0.204083 0.173915 0.190284

-4.598984 3.598358 -0.688322 -2.372758 -1.852762 -3.454050 -2.627957 -2.678449 -2.652393 -0.854823 -1.225503 -2.564349 -3.419857 -3.179503 -3.096115

0.0000 0.0008 0.4949 0.0222 0.0708 0.0013 0.0119 0.0104 0.0111 0.3974 0.2271 0.0139 0.0014 0.0027 0.0034


0.465054 0.290886 0.218990 2.062141 14.46582 0.555674

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterio n F-statistic Prob(F-statistic)

2.421693 0.260056 0.018420 0.551293 2.670141 0.006814

29

Table 7: J-Test for Non-Nested Alternatives Dependent Variable: CP/Y Method: Least Squares Date: 06/29/99 Time: 18:32 Sample(adjusted): 1994:04 1998:12 Included observations: 57 after adjusting endpoints Convergence achieved after 9 iterations White Heteroskedasticity-Consistent Standard Errors & Covariance Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(R) LOG(W/P) LOG(WF/P) C S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 @TREND(94:2) STANDARD_MODEL AR(1)

-0.494740 21.78556 -3.095088 -76.79332 -2.002396 -3.644710 -2.761421 -2.637661 -2.637665 -0.625729 -2.461030 -2.300706 -3.849864 -2.847899 -3.675928 -0.090247 -0.566477 0.190433

0.303386 2.345269 1.218301 8.376420 1.070260 1.016686 0.900550 0.912942 0.767739 0.764448 0.726519 1.079087 0.914452 0.792906 0.974028 0.021502 2.033657 0.104842

-1.630727 9.289155 -2.540496 -9.167796 -1.870942 -3.584892 -3.066373 -2.889186 -3.435625 -0.818537 -3.387428 -2.132086 -4.210022 -3.591721 -3.773944 -4.197166 -0.278551 1.816382

0.1110 0.0000 0.0152 0.0000 0.0689 0.0009 0.0039 0.0063 0.0014 0.4180 0.0016 0.0394 0.0001 0.0009 0.0005 0.0002 0.7821 0.0770


0.902945 0.860639 1.044610 42.55722 -72.55174 2.072483


Inverted AR Roots

11.73753 2.798232 3.177254 3.822428 21.34318 0.000000

.19

30

Table 8: J-Test for Non-Nested Alternatives Dependent Variable: LOG(CP/Y) Method: Least Squares Date: 06/29/99 Time: 18:23 Sample: 1994:03 1998:12 Included observations: 58 White Heteroskedasticity-Consistent Standard Errors & Covariance Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(R) LOG(Y) @TREND(94:02) C S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 CASH_CREDIT_MODEL

0.018296 -0.323555 0.002330 2.414557 0.061684 0.096247 0.093339 0.106346 0.117413 0.055042 0.052701 0.075417 0.122162 0.107917 0.124554 0.100987

0.022292 0.820722 0.332794 -0.972240 0.001861 1.251904 1.202975 2.007155 0.081263 0.759063 0.088927 1.082304 0.068639 1.359859 0.076114 1.397191 0.079378 1.479158 0.055284 0.995620 0.051030 1.032760 0.090033 0.837661 0.108721 1.123620 0.090661 1.190344 0.117859 1.056809 0.009323 10.83198

0.4164 0.3365 0.2175 0.0512 0.4521 0.2853 0.1811 0.1697 0.1466 0.3251 0.3076 0.4070 0.2676 0.2406 0.2966 0.0000


0.899020 0.862955 0.096272 0.389265 62.81577 2.156228


2.421693 0.260056 -1.614337 -1.045939 24.92818 0.000000

31