P. A. V. B. Swamy

BRETTON-WOODS SYSTEMS, OLD AND NEW, AND THE ROTATION OF EXCHANGE-RATE REGIMES*

A recent contribution to the literature argues that the present international monetary system operates like the Bretton-Woods system. Asia is the new periphery of the system and pursues an export-led development strategy based on undervalued exchange rates and accumulated foreign reserves. The USA remains the centre country, pursuing a monetary-policy strategy that does not take external factors into account in conducting monetary policy. We test this hypothesis and also present a new method for decomposition of a time series using a time-varying business coefficient technique that allows us to test the relationship between the cycle and macroeconomic policies under both regimes.

Financial Deregulation, the Demand for Money, and Monetary Policy in Australia

Factors contributing to the deregulation of the Australian financial system are reviewed and the implications of deregulation are discussed for the transmission mechanism of monetary policy, the interest elasticity of money balances, and the stability of money demand. Several models of money demand, using three definitions of money, are estimated by both fixed- and random-coefficient techniques. Empirical results provide evidence that financial deregulation has led to a breakdown in the well-behaved money demand relationships that held in the regulated financial environment.

Is the Japanese economy in a liquidity trap

Abstract A random coefficient estimation procedure is used to estimate the time profile of the interest rate elasticity of Japanese money demand. Contrary to the prediction of the liquidity trap hypothesis, the absolute value of the elasticity is found to decline at lower levels of interest rates.

Further results on estimating linear regression models with partial prior information

Abstract This paper shows that Shillers smoothness restrictions on the lag coefficients in a distributed lag model imply a singular normal prior distribution. The posterior mean for this prior is different from Shillers estimator. Contradictions among specifying assumptions may arise if the moments of the singular normal prior are not properly determined. Sample data are used to guarantee that the estimated covariance matrix of an approximate generalized least squares estimator exceeds the estimatedmean square error matrix of the posterior mean by a positive semidefinite matrix. The posterior mean covers the generalized least squares and Almon estimators as special cases. We conclude with an empirical example which demonstrates the posterior means superiority in forecasting relative to both an approximate generalized least square estimator and Almon or ridge estimators.

The forward rate premium puzzle: a case of misspecification?

Abstract Empirical studies often report a negative relationship between the difference in the spot exchange rate and the forward premium, violating the forward-rate unbiasedness hypothesis. Using standard regression on a sample of ten exchange rates, we obtain both positive and negative coefficients. We argue that the negative coefficients could arise as a result of the non-linearities in the relationship and misspecification. As an alternative to the standard regression, we use a time-varying-coefficient technique that estimates bias-free coefficients and, thus, should provide better estimates of the link between spot and forward rates. Our findings strongly support the forward rate unbiasedness hypothesis. All the parameters are very close to unity and significant.

MICROPRODUCTION FUNCTIONS WITH UNIQUE COEFFICIENTS AND ERRORS: A RECONSIDERATION AND RESPECIFICATION

Estimated microproduction functions confront two major problems—those of (1) unknown functional forms and (2) the measurement of capital independent of the distribution of output among the factors of production. The latter problem has emerged unresolved from the earlier Cambridge capital controversy. In the presence of these two problems, all specifications of microproduction functions have involved nonunique coefficients and error terms. We provide a method of deriving time-varying coefficients that produces unique coefficients and error terms. Specifically, we respecify the microproduction function in such a way that its coefficients are the sums of (i) the appropriate partial derivatives and (ii) exact representations of excluded-variable biases. By decomposing the total coefficients, we obtain the unique coefficients and a unique error term. Our treatment of heterogeneous capital is not subject to the criticisms of that concept that emerged during the Cambridge controversy.

Small Area Estimation with Correctly Specified Linking Models

It is possible to improve the precision of a sample estimator for a small area based on sparse area-specific data by combining it with a model of its estimand, provided that this model is correctly specified. A proof of this result and the method of correctly specifying the models of the estimands of sample estimators are given in this paper. Widely used two-step estimation is shown to yield inconsistent estimators. The accuracies of different sample estimates of a population value can be improved by simultaneously estimating the population value and sums of the sampling and non-sampling errors of these sample estimates.

A note on generalizing the concept of cointegration

Building on the time-varying-coefficient (TVC) model, we propose a generalization of the concept of cointegration, allowing for the possibility that a set of variables measured with error entails a nonlinear relationship with unknown functional form. Both the dependent and explanatory variables of this relationship may be nonstationary (not necessarily of unit-root type), but there exists a nonlinear combination of all these explanatory variables that completely explains all the variation in the dependent variable. The TVC model allows us to test for the presence of this generalized cointegration in the absence of knowledge of the true nonlinear functional form and the full set of explanatory variables. We present the basic stages of the technique and discuss in detail how the issues of nonstationarity and cointegration affect each stage of the TVC estimation procedure.