I. Targets and Instruments

1. Targets

Prior to the adoption of a floating exchange rate system in October 1984, the key intermediate target of monetary policy was generally the domestic component of base money—the net domestic assets (NDA) of the monetary authorities. Since then, monetary policy has been directed primarily toward maintaining the expansion of base money below a certain target path.

The choice of monetary targets and the conduct of exchange rate policy are closely connected. Under a regime of a freely floating exchange rate (defined here as one in which any net intervention by the Central Bank is only to achieve some predetermined target for international reserves that is independent of both the exchange rate and domestic monetary variables) there is, in practice, no difference between targeting base money and targeting NDA since the difference between the two—the net foreign assets (NFA) of the monetary authorities—is fixed. However, if the Central Bank’s net intervention in the exchange market does adjust in response to movements in the exchange rate or in monetary variables, then the choice of target does affect the conduct of monetary policy. The results of section IV will show that the Central Bank did continue significant net intervention even after the adoption of the floating rate regime. In this situation, the choice between base money and NDA as targets requires the Central Bank to choose between sterilizing or not sterilizing, respectively, such additional net intervention in the exchange market.

This choice will be influenced, to some extent, by the relative priority given to combating inflation and by the likely causes of the increased exchange market intervention. In the Philippines, the switch to the targeting of base money reflected the high priority attached to arresting quickly the inflationary spiral that began in late 1983 as well as the possibility of substantial, unanticipated capital inflows. If such inflows occurred and the Central Bank intervened in the exchange market to offset part of the consequent upward pressure on the exchange rate, then the impact on domestic liquidity and hence prices could be substantial unless the resulting increase in NFA were offset by increased sales of open market instruments.

However, the impact on prices would depend, inter alia, on the causes of the external capital inflow. If the inflow reflected an unanticipated increase in the demand for money, then the increase in the money supply resulting from such intervention would not put upward pressure on prices. To accommodate such an unanticipated shift in money demand, the Central Bank’s policy was to allow for a certain limited, upward adjustment in its targeted path for base money whenever its net international reserve position improved by more than a targeted amount. The maximum limits on such adjustments have been around 4 percent of base money.

In most countries, base money comprises the monetary authority’s liabilities in the form of (a) currency outside banks, plus (b) deposit money banks’ domestic currency holdings, plus (c) deposit money banks’ deposits with the monetary authority. This definition will be referred to here as reserve money. In the Philippines, however, there are good reasons for focusing upon a somewhat broader definition as the key measure of monetary expansion. First, the Central Bank has allowed certain of its own security issues, as well as some government securities, to count as bank reserves in fulfilling reserve requirements. At their peak in April 1984, bank holdings of these reserve eligible securities were equivalent to about one half of reserve money (Chart 2). However, the Central Bank’s policy has been to gradually retire these securities; by end-1988, they were equivalent to under 5 percent of reserve money. Second, there have been prolonged periods during which banks have had significant reserve deficiencies, reflecting the weak financial position of important segments of the banking system. The monetary impact of allowing prolonged deficiencies is similar to that of an increase in reserve money caused by increased Central Bank credit to the banks involved; these deficiencies could, therefore, be appropriately included in a broader definition of base money. At their peak in late 1983, these reserve deficiencies averaged over 10 percent of reserve money.

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Philippines: Trends in Base Money and its Components

(Billions of pesos)

Citation: IMF Working Papers 1989, 098; 10.5089/9781451949049.001.A001

Source: Data provided by the Philippine authorities.

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To take account of these factors, the principal aggregate used to measure the stance of monetary policy in the Philippines has been base money, defined as reserve money plus bank holdings of reserve eligible securities plus reserve deficiencies. As Chart 2 illustrates, use of this broader definition can lead to somewhat different judgments on the relative tightness of monetary policy.

II. Estimation of Money Demand Functions

The setting of target paths for monetary aggregates is predicated on the existence of reasonably stable relationships between the demand for money and the level of prices and real output. This section discusses the estimation of such relationships for various categories of broad and narrow money: M1 (currency plus demand deposits); M2 (currency plus demand, time and savings deposits); and M3 (M2 plus various deposit substitutes). ^3/ The exact definition of each variable and the data sources are discussed in the Appendix. Since shifts between different components of money, especially between currency and bank deposits, also affect the “derived” demand for base money, the estimated demand functions for these various components are also reported.

2. Estimation results

The estimation results for the period Q4 1973 to Q4 1988 are reported in Table 1. The reported equations include the one-year lagged rate of inflation, but results using the polynomial distributed lags on past inflation were similar. The results are broadly as would be expected on theoretical grounds. Demand for time and savings deposits is a positive function of T90, representing the own rate of return, and is a negative function of TR91, representing the return on substitute assets. In the equations for Ml and currency, T90 proved to be much more significant as an indicator of returns on substitute assets than TR91; the estimated coefficient of the latter was of the wrong sign and often statistically insignificant. The results indicate that increases in inflation have a strong negative impact on the demand for real money balances and that this impact tends to be stronger for the broader monetary aggregates. For instance, on the basis of the results reported in Table 1, a 1 percentage point increase in the rate of inflation leads to about a 0.6 percent decrease in the demand for real M2 and 0.3 percent for M3, but only a 0.06 percent decrease in the demand for real currency (Table 2). Like a number of earlier studies, the results also show low estimated income elasticities of demand for M2 and M3. This probably reflects, to a large extent, the strong growth in financial assets such as Treasury and Central Bank bills that are close alternatives to the interest-bearing liabilities included in these monetary aggregates; for instance the deposit substitutes included in M3 were larger than the entire Ml aggregate during the early 1980s, but by 1988 had declined to less than the equivalent of 5 percent of Ml. Such shifts may also have affected the stability of the demand functions for these broader aggregates.

Table 1

Estimates of Money Demand Functions

Note: The numbers in parentheses below the estimated coefficients indicate absolute t-values; R² = coefficient of determination; D.W. = Durbin-Watson statistic; rho = estimated first-order autocorrelation coefficient after Hatanaka estimation; H = Durbin’s h-statistic; SSR = sum of squared residuals; SEE = standard error of estimate; b and f refer to backward and forward recursive residuals, respectively; * = significant at the 0.05 level and ** = significant at the 0.01 level. Coefficients for seasonal dummy variables are not reported. Other symbols are defined in the text.

Table 1

Estimates of Money Demand Functions

Regression Equation	Dependent Variable	log(GNP)	INFL (π4)	TR91	T90	Lagged Dependent Variable	R2 rho	D.W.H	SSR SEE	Cusums (b) Cusums (f)	Cusums Squared (b) Cusums Squared (f)
1.	log (M3/P)	0.30**	-0.002*	-0.002	-0.0009	0.46**	0.66	1.25	0.068	0.694	0.175
		(4.15)	(2.45)	(1.03)	(0.25)	(6.25)	0.75	3.57	0.037	0.655	0.185
2.	log (M3/P)	0.33**	-0.002*	-0.002		0.41**	0.64	1.20	0.078	0.653	0.205
		(4.41)	(2.27)	(1.36)		(5.60)	(0.76)	3.81	0.039	0.725	0.174
3.	log (M2/P)	0.40**	-0.003**	-0.00006	0.0003	0.52**	0.68	1.50	0.086	0.673	0.310**
		(4.46)	(3.09)	(0.02)	(0.07)	(6.51)	0.78	2.49	0.041	1.109*	0.115
4.	log (M2/P)	0.40**	-0.003**		0.001	0.52**	0.68	1.50	0.087	0.610	0.312**
		(4.55)	(3.22)		(0.04)	(6.56)	0.78	2.47	0.041	1.165**	0.126
5.	log (M2/P)	0.41**	-0.003**	0.0001		0.51**	0.68	1.49	0.088	0.652	0.312**
		(4.72)	(3.08)	(0.05)		(6.48)	0.78	2.50	0.041	1.070*	0.097
6.	log (M1/P)	0.34**	-0.0006	0.002	-0.01*	0.58**	0.64	1.69	0.202	0.867	0.265**
		(3.26)	(0.53)	(0.50)	(2.34)	(5.11)	0.56	2.70	0.063	0.653	0.324**
7.	log (M1/P)	0.33**	-0.0005		-0.01**	0.56**	0.63	1.69	0.204	0.749	0.276**
		(3.21)	(0.46)		(2.80)	(5.18)	0.56	2.23	0.063	0.582	0.331**
8.	log (CUR/P)	0.53**	-0.0002	0.008*	-0.018**	0.40**	0.79	1.76	0.141	0.989*	0.185
		(5.19)	(0.15)	(2.20)	(3.57)	(3.50)	0.64	2.09	0.053	0.465	0.302**
9.	log (CUR/P)	0.49**	-0.0003		-0.008*	0.48**	0.77	1.81	0.153	1.188*	0.134
		(4.55)	(0.27)		(2.48)	(3.90)	0.62	2.75	0.054	0.588	0.323**
10.	log (TSD/P)	0.46**	-0.003*	-0.002	0.006	0.65**	0.77	1.80	0.198	0.908	0.330**
		(3.33)	(2.29)	(0.59)	(1.02)	(8.66)	0.73	0.96	0.062	0.879	0.143
11.	log (DPSUB/P)	0.03	0.002	0.002	-0.02	0.98**	0.84	1.76	2.125	1.064*	0.277**
		(0.07)	(0.56)	(0.15)	(0.97)	(14.68)	0.51	1.03	0.204	1.191**	0.596**

Note: The numbers in parentheses below the estimated coefficients indicate absolute t-values; R² = coefficient of determination; D.W. = Durbin-Watson statistic; rho = estimated first-order autocorrelation coefficient after Hatanaka estimation; H = Durbin’s h-statistic; SSR = sum of squared residuals; SEE = standard error of estimate; b and f refer to backward and forward recursive residuals, respectively; * = significant at the 0.05 level and ** = significant at the 0.01 level. Coefficients for seasonal dummy variables are not reported. Other symbols are defined in the text.

View Table

Table 1

Estimates of Money Demand Functions

Regression Equation	Dependent Variable	log(GNP)	INFL (π4)	TR91	T90	Lagged Dependent Variable	R2 rho	D.W.H	SSR SEE	Cusums (b) Cusums (f)	Cusums Squared (b) Cusums Squared (f)
1.	log (M3/P)	0.30**	-0.002*	-0.002	-0.0009	0.46**	0.66	1.25	0.068	0.694	0.175
		(4.15)	(2.45)	(1.03)	(0.25)	(6.25)	0.75	3.57	0.037	0.655	0.185
2.	log (M3/P)	0.33**	-0.002*	-0.002		0.41**	0.64	1.20	0.078	0.653	0.205
		(4.41)	(2.27)	(1.36)		(5.60)	(0.76)	3.81	0.039	0.725	0.174
3.	log (M2/P)	0.40**	-0.003**	-0.00006	0.0003	0.52**	0.68	1.50	0.086	0.673	0.310**
		(4.46)	(3.09)	(0.02)	(0.07)	(6.51)	0.78	2.49	0.041	1.109*	0.115
4.	log (M2/P)	0.40**	-0.003**		0.001	0.52**	0.68	1.50	0.087	0.610	0.312**
		(4.55)	(3.22)		(0.04)	(6.56)	0.78	2.47	0.041	1.165**	0.126
5.	log (M2/P)	0.41**	-0.003**	0.0001		0.51**	0.68	1.49	0.088	0.652	0.312**
		(4.72)	(3.08)	(0.05)		(6.48)	0.78	2.50	0.041	1.070*	0.097
6.	log (M1/P)	0.34**	-0.0006	0.002	-0.01*	0.58**	0.64	1.69	0.202	0.867	0.265**
		(3.26)	(0.53)	(0.50)	(2.34)	(5.11)	0.56	2.70	0.063	0.653	0.324**
7.	log (M1/P)	0.33**	-0.0005		-0.01**	0.56**	0.63	1.69	0.204	0.749	0.276**
		(3.21)	(0.46)		(2.80)	(5.18)	0.56	2.23	0.063	0.582	0.331**
8.	log (CUR/P)	0.53**	-0.0002	0.008*	-0.018**	0.40**	0.79	1.76	0.141	0.989*	0.185
		(5.19)	(0.15)	(2.20)	(3.57)	(3.50)	0.64	2.09	0.053	0.465	0.302**
9.	log (CUR/P)	0.49**	-0.0003		-0.008*	0.48**	0.77	1.81	0.153	1.188*	0.134
		(4.55)	(0.27)		(2.48)	(3.90)	0.62	2.75	0.054	0.588	0.323**
10.	log (TSD/P)	0.46**	-0.003*	-0.002	0.006	0.65**	0.77	1.80	0.198	0.908	0.330**
		(3.33)	(2.29)	(0.59)	(1.02)	(8.66)	0.73	0.96	0.062	0.879	0.143
11.	log (DPSUB/P)	0.03	0.002	0.002	-0.02	0.98**	0.84	1.76	2.125	1.064*	0.277**
		(0.07)	(0.56)	(0.15)	(0.97)	(14.68)	0.51	1.03	0.204	1.191**	0.596**

Note: The numbers in parentheses below the estimated coefficients indicate absolute t-values; R² = coefficient of determination; D.W. = Durbin-Watson statistic; rho = estimated first-order autocorrelation coefficient after Hatanaka estimation; H = Durbin’s h-statistic; SSR = sum of squared residuals; SEE = standard error of estimate; b and f refer to backward and forward recursive residuals, respectively; * = significant at the 0.05 level and ** = significant at the 0.01 level. Coefficients for seasonal dummy variables are not reported. Other symbols are defined in the text.

Table 2

Philippines: Short-Run and Long-Run Elasticities or Semi-Elasticities of the Demand for Money, Q4 1973-Q4 1988 ^1/

^1/Derived from estimates reported in Table 1.

^2/Semi-elasticities. If the semi-elasticity of a variable is b, then a 1 percentage point change in that variable leads to approximately 100 b percent change in the demand for money. Thus, for M2, a 1 percentage point increase in the rate of inflation leads, in the long-run, to a 0.6 percent decline in the demand for real M2.

Table 2

Philippines: Short-Run and Long-Run Elasticities or Semi-Elasticities of the Demand for Money, Q4 1973-Q4 1988 ^1/

Monetary Aggregate and Variable		Short-Run Elasticity	Long-Run Elasticity	Adjustment Parameter
Currency				0.52
	GNP	0.49	0.94
	INFL (π4) 2/	-0.0003	-0.0006
	T90 2/	-0.008	-0.015
M1				0.44
	GNP	0.33	0.75
	INFL (π4) 2/	-0.0005	-0.001
	T90 2/	-0.01	-0.023
M2				0.49
	GNP	0.41	0.84
	INFL (π4) 2/	-0.003	-0.0061
	TR91 2/	0.0001	0.0002
M3				0.59
	GNP	0.33	0.56
	INFL (π4) 2/	-0.002	-0.003
	TR91 2/	-0.002	-0.003

^1/Derived from estimates reported in Table 1.

^2/Semi-elasticities. If the semi-elasticity of a variable is b, then a 1 percentage point change in that variable leads to approximately 100 b percent change in the demand for money. Thus, for M2, a 1 percentage point increase in the rate of inflation leads, in the long-run, to a 0.6 percent decline in the demand for real M2.

View Table

Table 2

Philippines: Short-Run and Long-Run Elasticities or Semi-Elasticities of the Demand for Money, Q4 1973-Q4 1988 ^1/

Monetary Aggregate and Variable		Short-Run Elasticity	Long-Run Elasticity	Adjustment Parameter
Currency				0.52
	GNP	0.49	0.94
	INFL (π4) 2/	-0.0003	-0.0006
	T90 2/	-0.008	-0.015
M1				0.44
	GNP	0.33	0.75
	INFL (π4) 2/	-0.0005	-0.001
	T90 2/	-0.01	-0.023
M2				0.49
	GNP	0.41	0.84
	INFL (π4) 2/	-0.003	-0.0061
	TR91 2/	0.0001	0.0002
M3				0.59
	GNP	0.33	0.56
	INFL (π4) 2/	-0.002	-0.003
	TR91 2/	-0.002	-0.003

^1/Derived from estimates reported in Table 1.

^2/Semi-elasticities. If the semi-elasticity of a variable is b, then a 1 percentage point change in that variable leads to approximately 100 b percent change in the demand for money. Thus, for M2, a 1 percentage point increase in the rate of inflation leads, in the long-run, to a 0.6 percent decline in the demand for real M2.

III. Tests of the Stability of the Money Demand Functions

Any instability in the estimated demand for money functions could manifest itself in several different ways. There could be a sudden shift in the underlying parameters for a particular subperiod; or a more gradual shift in the parameters over time; or the short-term adjustment path of the money stock could vary according to the nature of the initial shock, even though the final equilibrium position was as indicated by the basic model. In this section, we report a number of tests of whether parameters of the money demand functions are invariant over time.

The term stability is defined here in the statistical sense of the null hypothesis, H₀, that: B₁ = B₂ = …B_T and ${\mathrm{S}}_{\mathrm{1}}^{\mathrm{2}}={\mathrm{S}}_{\mathrm{2}}^{\mathrm{2}}=\mathrm{\dots }{\mathrm{S}}_{\mathrm{T}}^{\mathrm{2}}$, where B denotes the vector of parameters (β₀, β₁, β₂, β₃, β₄, δ) and ${\mathrm{S}}_{\mathrm{t}}^{\mathrm{2}}$ the variance of u_t at time t. It is useful to conduct a number of different stability tests, because they differ in terms of their power and alternative hypotheses. For example, the alternative hypothesis in the Quandt likelihood ratio test and the Chow test, discussed below, supposes coefficients are constant within a subperiod but change by discrete amounts over subperiods. If this is not the case, then it would be appropriate to re-estimate coefficients with a methodology that allows continuous but gradual time variation in the coefficients.

A fairly general approach aimed at investigating the stability of the regression coefficients consists of estimating a series of “recursive” regressions, starting with the first k+1 observations and adding a new observation with each regression until the final iteration contains all T observations in the sample. The predictive performance of the model can then be simulated, each observation in the sample being predicted with the parameter estimates based on the preceding observations. Brown, Durbin, and Evans (1975) suggest that the sequence of standardized one-step ahead prediction errors or “recursive residuals” be computed, and the cumulated sum of the residuals (or squared residuals) is then evaluated against a test statistic to determine whether the residuals are accumulating at a reasonably steady rate as the sample lengthens. If not, then the hypothesis that the parameter estimates are stable throughout the sample period is rejected at the specified confidence level. ^11/

There is a complication in the application of recursive residuals technique in the present context in that the errors of the money demand equations are serially correlated. If the estimated coefficient of serial correlation, $\hat{\rho }$, is sufficiently close to the true ρ one may expect the corresponding recursive residuals to have under the null hypothesis properties quite close to those of residuals based on the true value of ρ. However, there is no general guarantee that various test statistics computed from both sets of recursive residuals (based on ρ and $\hat{\rho }$ respectively) will have the same asymptotic distributions. Dufour (1982) suggested considering a grid of values of ρ inside some neighborhood of $\hat{\rho }$ and checking the robustness of the main conclusions. If the main conclusions on stability/instability are the same independently of ρ, these can be viewed as reliable. Hence, the cusums and cusums squared tests were all repeated for a range of values from ρ = -0.9 to 0.9 by intervals of 0.1.

The evidence regarding stability from the two recursive-regression tests is mixed; some of the test statistics favor the rejection of the stability hypothesis, while some do not. With a few exceptions, the estimated equations appear to be stable when judged by the cusums test. However, most of the equations show some temporal instability when judged by the cusums-of-squares test. ^12/ Two points need to be made in this context. First, the two tests are designed to uncover different types of instability. The cusums test will only indicate systematic shifts in one direction that cause the errors to accumulate, whereas the cusums of squares test will also pick up haphazard instability that causes the sum of squared errors to deviate from the hypothesized path. It therefore appears from these tests that the equations are subject to haphazard instability, but with some important exceptions, probably not to systematic shifts. This would be consistent with the earlier description of a financial system that has been subject to frequent shocks. Second, high values of cusum of squares test together with low values of cusum tests can be interpreted as suggesting that instability is due to a shift in the residual variances rather than to shifts in the values of regression coefficients.

Whereas the cusums and cusums squared tests serve as guides to patterns in regression relationships, these tests fail to capture exact points of discontinuity because time must pass for forecast errors to accumulate. A movement of the sum of the residuals outside the confidence limits does not necessarily imply that a structural shift occurred precisely at that point in time; leads and lags may occur between a disruptive event and the period in which the instability becomes significant. Consequently, further tests are required to identify accurately points of discontinuity. The most important of these are the Quandt likelihood ratio test and the Chow test.

The Quandt likelihood ratio test may be useful if the cusums and cusums squared tests tend to reject the hypothesis of stability and it is believed that any change over time may be concentrated at a single unknown point, which it is desired to locate. ^13/ A time series of the Quandt likelihood ratio was calculated for each equation on the basis of the recursive regressions. On the hypothesis that the equation undergoes a single shift at a specific point, this shift is likely to occur at the observation where the time series reaches its minimum. Even if the time series has a lower value elsewhere but the ratio jumps sharply from one observation to the next, that point may also indicate instability in the function. For the present exercise, both procedures were used judgmentally to determine the most likely date for the functional shift. The results suggest that the underlying parameters may have shifted in 1983 or 1984, which coincides with the period beginning with the financial crisis of late 1983. The lowest values for the time series of the Quandt likelihood ratios for the various monetary aggregates were as follows:

Monetary aggregate	Date
Currency	Q2 1983
M1	Q4 1981 and Q1 1983
M2	Q1 1985
M3	Q1 1984 and Q1 1985

View Table

Monetary aggregate	Date
Currency	Q2 1983
M1	Q4 1981 and Q1 1983
M2	Q1 1985
M3	Q1 1984 and Q1 1985

For the narrower monetary aggregates, currency and M1, there were also sharp jumps in the likelihood ratio series in Q4 1983. Consequently, the Chow test was done for the hypothesis that the coefficients of the estimated equations are equal for the period Q4 1973-Q3 1983 and Q4 1983-Q4 1988. The results indicate that there was a significant shift in the parameters for the currency, M1 and M3 equations, but not for the M2 equation. ^14/

IV. Application of the Model of Exchange Market Pressure to the Philippines

As was mentioned in the introduction, the adoption of a floating exchange rate system in October 1984 resulted in a much closer linkage between exchange rate and domestic credit policies and led to a number of changes in the intermediate targets of monetary policy. This section applies the Girton-Roper model of exchange market pressure to the Philippine monetary experience. ^15/ The basic hypothesis of the model, which builds upon the monetary approach to the balance of payments and exchange, rate determination, is that an excess domestic supply of money will cause some combination of currency depreciation and an outflow of foreign reserves. Thus, exchange market pressure is defined as the sum of the rates of change in the exchange rate and international reserves, suitably defined. An imbalance between the rates of growth in the supply and demand for money creates pressure for the exchange rate to adjust, but by intervening in the foreign exchange market, the authorities can convert some or all of the exchange market pressure into reserve changes (i.e., balance of payments surpluses or deficits). The hypothesis that the magnitude of exchange market pressure is independent of whether the monetary authorities absorb the market pressure in the exchange rate or in foreign reserves is also tested.

The Philippines provide a good example for testing the model because the economy can be regarded as small and open, in that world monetary conditions and world prices for goods, services, and capital are exogenously determined, which simplifies the estimation of the model. In these circumstances, the model consists of the following equations: (1) a money demand function based on those estimated in the previous section; (2) the money supply process; (3) purchasing power parity; and (4) the equilibrium condition for the monetary sector.

M^d is the demand for nominal money balances; M^s is the supply of nominal money balances; P is the price level; Y is the real GNP; rf is the average yield on 91-day Treasury bills; e is the exchange rate, expressed in terms of units of foreign currency per unit of domestic currency; P* is the foreign price level; m is the money multiplier; R is the stock of international reserves; and D is the domestic credit component of base money. ^16/ By replacing P with P*/e in equation (1) and transforming equations (1) and (2) into terms of rates of growth gives:

where $\mathrm{k}=\frac{{\mathrm{R}}_{-1}}{{\mathrm{R}}_{-1}+{\mathrm{D}}_{-1}};$ the percentage rate of change for each variable X is denoted by $\hat{\mathrm{X}}$ and the parameters α₁, α₂, and α₃ are, respectively, the real income, interest rate, and inflation elasticities of real money balances. Substituting equations (1a) and (2a) in (4), the following equation, which measures exchange market pressure is obtained:

where $\hat{\mathrm{r}}$ is the change in international reserves as a proportion of the monetary base and $\hat{\mathrm{d}}$ is the change in domestic credit as a proportion of the monetary base. Since α₂, α₃ < 0 and α₁ > 0, equation (5) states that an increase in the rate of growth in domestic credit, the money multiplier, the yield on treasury bills, and the inflation rate will, ceteris paribus, result in a depreciation of the exchange rate and/or an outflow of international reserves; whereas an increase in real income and world prices will result in an appreciation of the exchange rate and/or an inflow of international reserves.

In contrast to the simpler monetary approach models that focus on the balance of payments of a small economy under a system of fixed exchange rates, the above model takes exchange market pressure rather than the balance of payments as the appropriate focal point for economies under managed floating. If there is an excess supply of money, domestic residents will attempt to restore equilibrium in their portfolios by increasing their expenditures on goods and services and nonmonetary assets. Part of the adjustment process involves residents exchanging their local money for foreign currency, which results in a depreciation of the exchange rate and a reduction in the supply of money. The depreciation of the exchange rate leads in turn to an increase in the domestic price level, which increases the demand for money. Therefore, equilibrium is restored by a reduction in the supply of money and/or an increase in the demand for money. By replacing the dependent variable $(\hat{\mathrm{r}}+\hat{\mathrm{e}})\text{by}\left(\hat{\mathrm{r}}\right)\text{or}\left(\hat{\mathrm{e}}\right)$ as the dependent variable, the empirical analysis tests whether the exchange market pressure model provides better results than the monetary model for the balance of payments or the monetary model for determining the exchange rate, used independently.

The model was estimated for quarterly data from the fourth quarter 1973 to the fourth quarter 1988 using ordinary least squares. The recursive residual approach and the Chow test were used to test for parameter stability. Estimates of the parameters of equation (5) are reported in Table 3. M1 was used for calculating the money multiplier variable in regressions (1) to (4), whereas M2 was used in regressions (5) to (8).

Table 3

Estimated Coefficients for the Model of Exchange Market Pressure

Note: Regressions (1)-(4) use M1 for calculating the money multiplier variable and regressions (5)-(8) use M2. The numbers in parentheses below the estimated coefficients indicate absolute t-values; R² = coefficient of determination; D.W. = Durbin-Watson statistic; SSR = sum of squared residuals; SEE = standard error of estimate; b and f refer to backward and forward recursive residuals, respectively; * = significant at the 0.05 level and ** = significant at the 0.01 level. Other symbols are defined in the text.

Table 3

Estimated Coefficients for the Model of Exchange Market Pressure

		Estimated Coeffient of the Independent Variable									Cusums Squared (b)
Regression Equation	Dependent Variable	$\hat{\mathrm{d}}$	$\hat{\mathrm{P}}*$	Ŷ	$\hat{\mathrm{m}}$	$\hat{\mathrm{r}}\mathrm{f}$	$\hat{\mathrm{\pi }}$	Q	R2 D.W.	Cusums (b) Cusums (f)	Cusums Squared (f)	SSR SEE
1.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.86**	1.29*	0.45**	-0.49**	-0.14*	-0.01		0.78	0.343	0.234*	0.27
		(13.6)	(2.6)	(3.6)	(3.0)	(2.2)	(0.5)		1.90	0.606	0.108	0.07
2.	$\hat{\mathrm{r}}$	-0.80**	1.81	0.41**	-0.38*	-0.06	0.00		0.75	0.492	0.158	0.31
		(11.7)	(3.4)	(3.0)	(2.2)	(0.9)	(0.14)		2.01	0.337	0.195	0.08
3.	ê	-0.06	0.52	0.04	-0.10	-0.08*	-0.01		0.16	1.215**	0.357**	0.09
		(1.6)	(1.8)	(0.6)	(1.1)	(2.1)	(1.1)		1.19	1.066*	0.508**	0.04
4.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.86**	0.24	0.42**	-0.47**	-0.13*	-0.004	0.02	0.77	0.539	0.216*	0.26
		(13.6)	(0.26)	(3.3)	(3.0)	(2.1)	(0.4)	(1.3)	1.82	0.737	0.214*	0.07
5.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.95**	1.56**	0.28**	-0.94**	-0.14**	0.01		0.88	0.805	0.142	0.16
		(18.5)	(4.2)	(3.19)	(7.39)	(2.89)	(0.80)		1.73	0.463	0.133	0.05
6.	$\hat{\mathrm{r}}$	-0.93**	1.99**	0.31**	-1.05**	-0.006	0.005		0.92	0.532	0.191	0.15
		(18.7)	(5.5)	(3.56)	(8.63)	(0.13)	(0.67)		1.79	0.476	0.151	0.05
7.	ê	-0.03	-0.43	-0.02	0.11	-0.14**	0.001		0.30	1.235**	0.377**	0.08
		(0.8)	(1.65)	(0.3)	(1.31)	(4.00)	(0.22)		1.33	0.986	0.499	0.04
8.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.94**	0.42	0.26**	-0.92**	-0.15**	0.008	0.02	0.87	0.880	0.117	0.15
		(18.6)	(0.59)	(2.88)	(7.36)	(3.13)	(1.03)	(1.87)	1.70	0.678	0.205*	0.05

Note: Regressions (1)-(4) use M1 for calculating the money multiplier variable and regressions (5)-(8) use M2. The numbers in parentheses below the estimated coefficients indicate absolute t-values; R² = coefficient of determination; D.W. = Durbin-Watson statistic; SSR = sum of squared residuals; SEE = standard error of estimate; b and f refer to backward and forward recursive residuals, respectively; * = significant at the 0.05 level and ** = significant at the 0.01 level. Other symbols are defined in the text.

View Table

Table 3

Estimated Coefficients for the Model of Exchange Market Pressure

		Estimated Coeffient of the Independent Variable									Cusums Squared (b)
Regression Equation	Dependent Variable	$\hat{\mathrm{d}}$	$\hat{\mathrm{P}}*$	Ŷ	$\hat{\mathrm{m}}$	$\hat{\mathrm{r}}\mathrm{f}$	$\hat{\mathrm{\pi }}$	Q	R2 D.W.	Cusums (b) Cusums (f)	Cusums Squared (f)	SSR SEE
1.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.86**	1.29*	0.45**	-0.49**	-0.14*	-0.01		0.78	0.343	0.234*	0.27
		(13.6)	(2.6)	(3.6)	(3.0)	(2.2)	(0.5)		1.90	0.606	0.108	0.07
2.	$\hat{\mathrm{r}}$	-0.80**	1.81	0.41**	-0.38*	-0.06	0.00		0.75	0.492	0.158	0.31
		(11.7)	(3.4)	(3.0)	(2.2)	(0.9)	(0.14)		2.01	0.337	0.195	0.08
3.	ê	-0.06	0.52	0.04	-0.10	-0.08*	-0.01		0.16	1.215**	0.357**	0.09
		(1.6)	(1.8)	(0.6)	(1.1)	(2.1)	(1.1)		1.19	1.066*	0.508**	0.04
4.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.86**	0.24	0.42**	-0.47**	-0.13*	-0.004	0.02	0.77	0.539	0.216*	0.26
		(13.6)	(0.26)	(3.3)	(3.0)	(2.1)	(0.4)	(1.3)	1.82	0.737	0.214*	0.07
5.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.95**	1.56**	0.28**	-0.94**	-0.14**	0.01		0.88	0.805	0.142	0.16
		(18.5)	(4.2)	(3.19)	(7.39)	(2.89)	(0.80)		1.73	0.463	0.133	0.05
6.	$\hat{\mathrm{r}}$	-0.93**	1.99**	0.31**	-1.05**	-0.006	0.005		0.92	0.532	0.191	0.15
		(18.7)	(5.5)	(3.56)	(8.63)	(0.13)	(0.67)		1.79	0.476	0.151	0.05
7.	ê	-0.03	-0.43	-0.02	0.11	-0.14**	0.001		0.30	1.235**	0.377**	0.08
		(0.8)	(1.65)	(0.3)	(1.31)	(4.00)	(0.22)		1.33	0.986	0.499	0.04
8.	$\hat{\mathrm{e}}+\hat{\mathrm{r}}$	-0.94**	0.42	0.26**	-0.92**	-0.15**	0.008	0.02	0.87	0.880	0.117	0.15
		(18.6)	(0.59)	(2.88)	(7.36)	(3.13)	(1.03)	(1.87)	1.70	0.678	0.205*	0.05

Note: Regressions (1)-(4) use M1 for calculating the money multiplier variable and regressions (5)-(8) use M2. The numbers in parentheses below the estimated coefficients indicate absolute t-values; R² = coefficient of determination; D.W. = Durbin-Watson statistic; SSR = sum of squared residuals; SEE = standard error of estimate; b and f refer to backward and forward recursive residuals, respectively; * = significant at the 0.05 level and ** = significant at the 0.01 level. Other symbols are defined in the text.

Regressions (1) and (5) provide strong evidence in support of the exchange market pressure model. All the estimated coefficients have the correct sign and significantly differ from zero; the exception is the inflation variable in equation (1), which is statistically insignificant. Recursive regressions were run, both forward and backward, over the sample period, and except for the cusums squared test for the backward recursion, the null hypothesis of parameter stability cannot be rejected. Since the floating exchange rate system was adopted in October 1984, the Chow test was done for the hypothesis that the coefficients of the estimated equations are equal for the period Q4 1973-Q3 1984 and Q4 1984-Q4 1988. The results support the hypothesis that the parameters of the estimated model are constant for the two sub-periods.

As a further test of the model, regression equations using either $\hat{\mathrm{r}}$ or ê as the sole dependent variable rather than the composite variable $\hat{\mathrm{r}}+\hat{\mathrm{e}}$ were estimated. Regression equations (2) and (6) report the results for $\hat{\mathrm{r}}$ as the sole dependent variable, and they are similar to those obtained for the composite variable. However, when ê is used as the dependent variable (equations (3) and (7)), the results are markedly poorer, with some parameter estimates having the wrong signs and the cusums and cusums squared tests indicating parameter instability.

As a final test, the hypothesis that the measure of exchange market pressure is not sensitive to its distribution between exchange rate and foreign reserves changes was tested. To do this, the variable, $\mathrm{Q}=(\hat{\mathrm{e}}-1)/(\hat{\mathrm{r}}-1)$, was added to the regression equations (1) and (5). ^17/ The results, reported in equations (4) and (8), are basically unchanged, and the Q variable is statistically insignificant. Thus, the measure of exchange market pressure is not sensitive to its composition.

Having demonstrated that the exchange market pressure model fits well for Philippine data, it is interesting to examine how at different periods the authorities’ have allowed excess domestic money supply pressures to be reflected primarily in movements in the exchange rate or in net foreign assets. Chart 3 illustrates that, even after the formal adoption of a floating exchange rate system in October 1984, changes in net foreign assets continued to bear a large part of the impact of excess domestic money supplies. Indeed, there was, in practice, remarkably little change in the degree of the authorities’ reliance on exchange rate movements compared with reserve management as a result of the announced change in exchange rate regime; for instance, the standard deviations in $\hat{\mathrm{r}}$ and ê were almost identical before and after the announced change in regime. ^18/ To some extent, this reflected the substantial discrete devaluations that occurred in 1983-84, just prior to the adoption of the floating rate (Chart 4).

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Philippines: Trends in Exchange Market Pressure and the Composition of the Authorities’ Response

Citation: IMF Working Papers 1989, 098; 10.5089/9781451949049.001.A001

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Trends in Exchange Rate

(pesos per US dollars; end period)

Citation: IMF Working Papers 1989, 098; 10.5089/9781451949049.001.A001

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V. Concluding Remarks

This paper has examined some of the key issues in the conduct of Philippine monetary policy since 1984, including the factors underlying the monetary authorities’ choice of intermediate policy targets and instruments used to achieve those targets. It was noted that the setting of target paths for monetary aggregates is predicated on the existence of reasonably stable relationships between the demand for money and the level of prices and real output. Accordingly, the paper reported on the estimation of demand functions for various categories of monetary aggregates, and discussed various tests of stability of these demand functions in light of the various shocks to the economy and the financial system.

The results suggest that the demand for money functions (especially for M2) have been more stable than might have been expected given that the financial system has encountered many shocks and has been significantly transformed over time. However, the results support the conclusions that (1) even if the demand for money and its components is reasonably stable in the longer term, there can be periods of temporary instability (especially for currency demand) when the variances around any predicted values may be large. This would argue for some caution in rigidly adhering to precise base money targets in the very short term, especially during periods of financial shocks; and (2) there has been a significant, long-term shift in most money demand functions following the 1983-84 period.

The analysis in this paper has emphasized that the choice of monetary targets and the conduct of exchange rate policy are closely connected. A monetary model of exchange market pressure was applied to the Philippine experience. The basic proposition of the model is that any excess domestic supply of money will be dissipated through some combination of adjustments in the balance of payments and exchange rates. The regression analysis shows that there is strong evidence of a negative relationship between the rate of domestic credit creation and the rates of change in exchange market pressure. The results also show that the measure of exchange market pressure does not depend on its composition between foreign exchange and foreign reserves. Finally, even after the adoption of a floating exchange rate system in October 1984, the authorities allowed changes in foreign reserves to continue to absorb most of the exchange market pressure.