Changing Monetary Policy Rules, Learning, and Real Exchange Rate Dynamics

When central banks set nominal interest rates according to an interest rate reaction function, such as the Taylor rule, and the exchange rate is priced by uncovered interest parity, the real exchange rate is determined by expected inflation differentials and output gap differentials. In this paper I examine the implications of these Taylor-rule fundamentals for real exchange rate determination in an environment where market participants are ignorant of the numerical values of the model's coefficients but attempt to acquire that information using least-squares learning rules. I find evidence that this simple learning environment provides a plausible framework for understanding real dollar--DM exchange rate dynamics from 1976 to 2003. The least-squares learning path for the real exchange rate implied by inflation and output gap data exhibits the real depreciation of the 70s, the great appreciation (1979.4-1985.1) and the subsequent great depreciation (1985.2-1991.1) observed in the data. An emphasis on Taylor-rule fundamentals may provide a resolution to the exchange rate disconnect puzzle.

Cointegration Vector Estimation by Panel DOLS and Long-Run Money Demand

We study the panel DOLS estimator of a homogeneous cointegration vector for a balanced panel of N individuals observed over T time periods. Allowable heterogeneity across individuals include individual-specific time trends, individual-specific fixed effects and time-specific effects. The estimator is fully parametric, computationally convenient, and more precise than the single equation estimator. For fixed N as T approaches infinity, the estimator converges to a function of Brownian motions and the Wald statistic for testing a set of linear constraints has a limiting chi-square distribution. The estimator also has a Gaussian sequential limit distribution that is obtained first by letting T go to infinity then letting N go to infinity. In a series of Monte Carlo experiments, we find that the asymptotic distribution theory provides a reasonably close approximation to the exact finite sample distribution. We use panel dynamic OLS to estimate coefficients of the long-run money demand function from a panel of 19 countries with annual observations that span from 1957 to 1996. The estimated income elasticity is 1.08 (asymptotic s.e.=0.26) and the estimated interest rate semi-elasticity is -0.02 (asymptotic s.e.=0.01).

The Use of Predictive Regressions at Alternative Horizons in Finance and Economics

When a k period future return is regressed on a current variable such as the log dividend yield, the marginal significance level of the t-test that the return is un- predictable typically increases over some range of future return horizons, k. Local asymptotic power analysis shows that the power of the long-horizon predictive regression test dominates that of the short-horizon test over a nontrivial region of the admissible parameter space. In practice, small sample OLS bias, which differs under the null and the alternative, can distort the size and reduce the power gains of long-horizon tests. To overcome these problems, we suggest a moving block recursive Jackknife estimator of the predictive regression slope coefficient and test statistics that is appropriate under both the null and the alternative. The methods are applied to testing whether future stock returns are predictable. Consistent evidence in favor of return predictability shows up at the 5 year horizon.Predictive regression, Long horizons, Stock returns, Small sample bias, Local asymptotic power

The Use of Predictive Regressions at Alternative Horizons in Finance and Economics

When a k period future return is regressed on a current variable such as the log dividend yield, the marginal significance level of the t-test that the return is unpredictable typically increases over some range of future return horizons, k. Local asymptotic power analysis shows that the power of the long-horizon predictive regression test dominates that of the short-horizon test over a nontrivial region of the admissible parameter space. In practice, small sample OLS bias, which differs under the null and the alternative, can distort the size and reduce the power gains of long-horizon tests. To overcome these problems, we suggest a moving block recursive Jackknife estimator of the predictive regression slope coefficient and test statistics that is appropriate under both the null and the alternative. The methods are applied to testing whether future stock returns are predictable. Consistent evidence in favor of return predictability shows up at the 5 year horizon.

Official Interventions and Occasional Violations of Uncovered Interest Parity in the Dollar-DM Market

This paper presents a model of exchange rate determination in which the forward premium anomaly emerges as the result of unanticipated central bank interventions in the foreign exchange market. Deviations from uncovered interest parity (UIP) therefore represent neither unexploited profit opportunities nor compensation for bearing risk. In simulations, the model generates a forward premium anomaly and matches several other notable features of US-German data. Additional empirical support is obtained from an analysis of Fed and Bundesbank interventions in the dollar—DM market where it is found that the forward premium anomaly intensifies during those times when a central bank intervenesForward premium anomaly, foreign exchange intervention

Dynamic Seemingly Unrelated Cointegrating Regression

Multiple cointegrating regressions are frequently encountered in empirical work as, for example, in the analysis of panel data. When the equilibrium errors are correlated across equations, the seemingly unrelated regression estimation strategy can be applied to cointegrating regressions to obtain asymptotically ecient estimators. While non-parametric methods for seemingly unrelated cointegrating regressions have been proposed in the literature, in practice, specification of the estimation problem is not always straightforward. We propose Dynamic Seemingly Unrelated Regression (DSUR) estimators which can be made fully parametric and are computationally straightforward to use. We study the asymptotic and small sample properties of the DSUR estimators both for heterogeneous and homogenous cointegrating vectors. The estimation techniques are then applied to analyze two long-standing problems in international economics. Our first application revisits the issue of whether the forward exchange rate is an unbiased predictor of the future spot rate. Our second application revisits the problem of estimating long-run correlations between national investment and national saving.

Exchange Rate Models Are Not as Bad as You Think

Standard models of exchange rates, based on macroeconomic variables such as prices, interest rates, output, etc., are thought by many researchers to have failed empirically. We present evidence to the contrary. First, we emphasize the point that "beating a random walk" in forecasting is too strong a criterion for accepting an exchange rate model. Typically models should have low forecasting power of this type. We then propose a number of alternative ways to evaluate models. We examine in-sample fit, but emphasize the importance of the monetary policy rule, and its effects on expectations, in determining exchange rates. Next we present evidence that exchange rates incorporate news about future macroeconomic fundamentals, as the models imply. We demonstrate that the models might well be able to account for observed exchange-rate volatility. We discuss studies that examine the response of exchange rates to announcements of economic data. Then we present estimates of exchange-rate models in which expected present values of fundamentals are calculated from survey forecasts. Finally, we show that out-of-sample forecasting power of models can be increased by focusing on panel estimation and long-horizon forecasts.

Bias Reduction by Recursive Mean Adjustment in Dynamic Panel Data Models

Accurate estimation of the dominant root of a stationary but persistent time series are required to determine the speed at which economic time series, such as real exchange rates or interest rates, adjust towards their mean values. In practice, accuracy is hampered by downward small- sample bias. Recursive mean adjustment has been found to be a useful bias reduction strategy in the regression context. In this paper, we study recursive mean adjustment in dynamic panel data models. When there exists cross-sectional heterogeneity in the dominant root, the recursive mean adjusted SUR estimator is appropriate. When homogeneity restrictions can be imposed, a pooled recursive mean adjusted GLS estimator with fixed e¤ects is the desired estimator. Application of these techniques to a small panel of five eurocurrency rates finds that these interest rates are unit root nonstationary as the bias-corrected autoregressive coefficient exceeds 1.Small sample bias, Recursive mean adjustment, Panel Data, Cross-sectional dependence, Interest rate dynamics

Mean Reversion in Equilibrium Asset Prices

Recent empirical studies have found that stock returns contain substantial negative serial correlation at long horizons. We examine this finding with a series of Monte Carlo simulations in order to demonstrate that it is consistent with an equilibrium model of asset pricing. When investors display only a moderate degree of risk aversion, commonly used measures of mean reversion in stock prices calculated from actual returns data nearly always lie within a 60 percent confidence interval of the median of the Monte Carlo distributions. From this evidence, we conclude that the degree of serial correlation in the data could plausibly have been generated by our model.