This paper deals with a family of parametric, single-equation cointegration estimators that arise in the context of the autoregressive distributed lag (ADL) models. We particularly focus on a subclass of the ADL models, those that do not involve lagged values of the dependent variable, referred to as augmented static (AS) models. The general ADL and the restricted AS models give rise to the ADL and dynamic OLS (DOLS) estimators, respectively. The relative performance of these estimators is assessed by means of Monte Carlo simulations in the context of a triangular data generation process (DGP) where the cointegration error and the error that drives the regressor follow a VAR(1) process. The results suggest that ADL fares consistently better than DOLS, both in terms of estimation precision and reliability of statistical inferences. This is due to the fact that DOLS, as opposed to ADL, does not fully correct for the second-order asymptotic bias effects of cointegration, since a 'truncation bias' always remains. As a result, the performance of DOLS approaches that of ADL, as the number of lagged values of the first difference of the regressor in the AS model increases. Another set of Monte Carlo simulations suggests that the commonly used information criteria select the correct order of the ADL model quite frequently, thus making the employment of ADL over DOLS quite appealing and feasible. Additional results suggest that ADL re-emerges as the optimal estimator within a wider class of asymptotically efficient estimators including, apart from DOLS, the semiparametric fully modified least squares (FMLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99-125), the non-linear parametric estimator (PL) of Phillips and Loretan (1991, Review of Economic Studies 58, 407-36) and the system-based maximum likelihood estimator (JOH) of Johansen ( , Econometrica 59, 1551. All the aforementioned results are robust to alternative models for the error term, such as vector autoregressions of higher order, or vector moving average processes.
In this paper we provide some empirical evidence on the casual relationship between stock prices and exchange rates volatility in four East Asian countries. In order to test for causality-in-variance, we use a GARCH model for which a BEKK representation is adopted, and then test for the relevant zero restrictions on the conditional variance parameters. We find that in the pre-crisis sample stock prices lead exchange rates negatively in Japan and South Korea (consistently with the portfolio approach) and positively in Indonesia and Thailand. In the latter two countries after the onset of the 1997 East Asian crisis the spillover effects are found to be bidirectional.
This paper argues that Fisher's paradox can be explained away in terms of estimator choice. We analyse by means of Monte Carlo experiments the small sample properties of a large set of estimators (including virtually all available single-equation estimators), and compute the critical values based on the empirical distributions of the t-statistics, for a variety of Data Generation Processes (DGPs), allowing for structural breaks, ARCH effects etc. We show that precisely the estimators most commonly used in the literature, namely OLS, Dynamic OLS (DOLS) and non-prewhitened FMLS, have the worst performance in small samples, and produce rejections of the Fisher hypothesis. If one employs the estimators with the most desirable properties (i.e., the smallest downward bias and the minimum shift in the distribution of the associated t-statistics), or if one uses the empirical critical values, the evidence based on US data is strongly supportive of the Fisher relation, consistently with many theoretical models.Fisher's paradox, Cointegration, Single-equation estimators, Monte Carlo analysis,
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