Detecting and modelling structural changes in GARCH processes have attracted increasing attention in time series econometrics. In this paper, we propose a new approach to testing structural changes in GARCH models. The idea is to compare the log likelihoods of a time-varying parameter GARCH...

This paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary. Since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting, we develop estimators for...

This paper proposes an extension of the Blinder-Oaxaca decomposition from two to a continuum of comparison groups. The proposed decomposition is then estimated for the case of racial wage differences in urban Peru, exploiting a novel data set that allows the capturing of mestizaje (racial mixtures).

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of...

We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain...

This paper focuses on the robust Effcient Method of Moments (EMM) estimation of a general parametric stationary process and proposes a broad framework for constructing robust EMM statistics in this context. This extends the application field of robust statistics to very general time series...

Inspired by findings of lowdimensional nonlinearities and the Theorem of Takens (1983) forecasting models of financial time series are often built upon nonparametric, i.e. universal nonlinear, univariate relationships. Empirical investigations, however, are seriously contaminated by the problem...

Conventional time series analysis, focusing exclusively on a time series at a given scale, lacks the ability to explain the nature of the data generating process. A process equation that successfully explains daily price changes, for example, is unable to characterize the nature of hourly price...

In this paper, we investigate the relative performance of Value-at-Risk (VaR) models with the daily stock market returns of nine di.erent emerging markets. In addition to well-known modeling approaches such as variance-covariance method and historical simulation, we study the extreme value...

This paper presents an empirical investigation of scaling and multifractal properties of U.S. Dollar-Deutschemark (USD-DEM) returns. The data set is ten years of 5-minute returns. The cumulative return distributions of positive and negative tails at di.erent time intervals are linear in the...