Band 9, Heft 2

The Representative Agent Growth Model is estimated econometrically using the Generalized Method of Moments for the U.S. economy for three separate Growth Eras and the results compared to those obtained using the Kydland--Prescott calibration approach. The estimated parameters differ substantially in the three cases, which imply changing social preferences for present versus future income and work--leisure tradeoffs. These in turn imply switching among alternative balanced growth paths and differences in the contributions of capital, labor, and labor augmenting productivity among the three Eras. Using the GMM method yields very high productivity and capital elasticity parameters and a very low time preference parameter for Eras I compared to Eras III and IV. While both GMM and the calibration method yield much smaller leisure parameters for Era IV than for Eras I and III.

This paper proposes a statistical test of the martingale hypothesis. It can be used to test whether a given time series is a martingale process against certain non-martingale alternatives. The class of alternative processes against which our test has power is very general and it encompasses many nonlinear non-martingale processes which may not be detected using traditional spectrum-based or variance-ratio tests. We look at the hypothesis of martingale, in contrast with other existing methods which test for the hypothesis of martingale difference. Two different types of test are considered: one is a generalized Kolmogorov-Smirnov test and the other is a Cramer-von Mises type test. For the processes that are first-order Markovian in mean, in particular, our approach yields the test statistics that neither depend upon any smoothing parameter nor require any resampling procedure to simulate the null distributions. Their null limiting distributions are nicely characterized as functionals of a continuous stochastic process so that the critical values are easily tabulated. We prove consistency of our tests and further investigate their finite sample properties via simulation. Our tests are found to be rather powerful in moderate size samples against a wide variety of non-martingales including exponential autoregressive, threshold autoregressive, markov switching, chaotic, and some of nonstationary processes.

This paper applies nonlinear projection methods to solve Ramsey problems in a stochastic monetary economy. The presence of nonlinear distortions in the Ramsey problem requires the use of a solution procedure which captures these effects. The nonlinear projection method, even with low-order Chebyshev polynomials as employed in this paper, is able to capture a significant portion of the Jensen's inequality effects. As an example of the usefulness of nonlinear projection methods, we examine Barro's (1987, 1979) conjecture that welfare gains are available from policy smoothing with debt. Increases in the volatility of distortionary monetary policy are more than offset by declines in the volatility of distortionary labor taxes so that introduction of debt is welfare enhancing.

We address a consistency problem in the commonly used nonparametric test for Granger causality developed by Hiemstra and Jones (1994). We show that the relationship tested is not implied by the null hypothesis of Granger non-causality. Monte Carlo simulations using processes satisfying the null hypothesis show that, for a given nominal size, the actual rejection rate may tend to one as the sample size increases. Our results imply that evidence for nonlinear Granger causality reported in the applied empirical literature should be re-interpreted.

We develop a new Markov Chain Monte Carlo procedure for a time series regression model truncated by upper and lower bounds. The regression error term is assumed to follow an ARMA--GARCH process. We use a convergence diagnostics with a simultaneous test of mean and covariance stationarity and discuss model selection criteria. Using MCMC procedure we test the purchasing power parity theory for the Japanese yen controlled to fluctuate in a narrow band and find that the theory is supported if double truncation is incorporated in estimation.

This paper explores the forecasting abilities of Markov-Switching models. Although MS models generally display a superior in-sample fit relative to linear models, the gain in prediction remains small. We confirm this result using simulated data for a wide range of specifications by applying several tests of forecast accuracy and encompassing robust to nested models. In order to explain this poor performance, we use a forecasting error decomposition. We identify four components and derive their analytical expressions in different MS specifications. The relative contribution of each source is assessed through Monte Carlo simulations. We find that the main source of error is due to the misclassification of future regimes.

A pervasive finding of unit roots in macroeconomic data often runs counter to intuition regarding the stochastic nature of the process under consideration. Two econometric techniques have been utilized in an attempt to resolve the finding of unit roots, namely long memory and models that depart from linearity. While the use of long memory and stochastic regime switching models have developed almost independently of each other, it is now clear that the two modeling techniques can be intimately linked. In particular, both modeling techniques have been used in isolation to study the dynamics of the real exchange rate. To determine the importance of each technique in this context, I employ a testing and estimation procedure that allows one to jointly test for long memory and non-linearity (regime switching behavior) of the STAR variety. I find that there is substantial evidence of non-linear behavior for the real exchange rate for many developing and European countries, with little evidence for ESTAR non-linearity for countries outside the European continent including Japan and Canada. In cases where non-linearity is found, I also find significant evidence of long memory for the majority of the countries in my sample. Thus, long memory and non-linearity can also be viewed as compliments rather than substitutes. The linear model in isolation appears to be inadequate for breaking down the paradox known as the PPP puzzle. On the other hand, a combination of long memory and non-linearity may be a promising research avenue for pursuing an answer to the paradox.