Suchergebnisse

In this paper, using the Monte Carlo (MC) method we propose an estimation and (at the same time) a test procedure for the stability parameter of a-stable distributions. One powerful advantage of the MC method is that it provides an exact significance level for finite samples, whose distribution can be far different from that of asymptotic samples on which the level of confidence interval for estimates is usually based. Statistical theory for the MC method is given. A simulation study compares the efficiency of our estimate with the Hill estimate (Hill, 1975). Construction of significance level based on the MC method is exploited and the corresponding power function is also studied. An empirical application demonstrates an easy implementation of our estimation and test procedure. It turns out that our estimate can improve the efficiency of any estimator for a in terms of mean square error.

Abstract.  We discuss statistical inference problems associated with identification and testability in econometrics. We consider inference in non‐parametric models and weakly identified structural models (weak instruments). We point out that many ill‐defined statistical problems, such as non‐testable hypotheses, occur in these areas and are typically associated with asymptotic approximations. In non‐parametric models, such problems include testing moments and inference under heteroscedasticity or serial dependence of unknown form. For weakly identified structural models, difficulties are typically associated with improper pivots, and we review recent developments aimed at proposing more reliable procedures, including alternative proposed statistics, bounds, projection, split‐sampling, conditioning, Monte Carlo tests. JEL classification: C1, C12, C14, C15, C3, C5

This book contains eleven articles which provide empirical applications as well as theoretical extensions of some of the most exciting recent developments in time-series econometrics. The papers are grouped around three broad themes: (I) the modeling of multivariate times series; (II) the analysis of structural change; (III) seasonality and fractional integration. Since these themes are closely inter-related, several other topics covered are also worth stressing: vector autoregressive (VAR) models, cointegration and error-correction models, nonparametric methods in time series, and fractionally integrated models. Researchers and students interested in macroeconomic and empirical finance will find in this collection a remarkably representative sample of recent work in this area