A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...

We establish that under mild conditions, testing for the individual significance of an impulse indicator in the conditional model, selected on the basis of prior testing of its significance in the impulse saturated marginal model does not require bootstrapping critical values. Extensive Monte...

This document reviews and applies recently developed techniques for Bayesian estimation and model selection in the context of Time Series modelingfor Stochastic volatility. After the literature review on Generalized Conditional Autoregressive models, Stochastic Volatility models, and the...

An exploratory estimation of ARFIMA(p,d,q) models showed that the estimated d is sensitive to the short-term dynamics included. To address this issue, I run a series of Monte Carlo experiments and test the performance (i) of the AIC and the SIC in selecting p and q and (ii) of the AIC, the SIC...

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...

We use data generated by a macroeconomic DSGE model to study the relative benefits of forecast combinations based on forecast-encompassing tests relative to simple uniformly weighted forecast averages across rival models. Assumed rival models are four linear autoregressive specifications, one of...

The present paper aims to test a new model comparison methodology by calibrating and comparing three agent-based models of financial markets on the daily returns of 18 indices. The models chosen for this empirical application are the herding model of Gilli & Winker, its asymmetric version by...

We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002) does not necessarily lead to consistent estimation of common parameters...

In the context of an autoregressive panel data model with fixed effect, we examine the relationship between consistent parameter estimation and consistent model selection. Consistency in parameter estimation is achieved by using the tansformation of the fixed effect proposed by Lancaster (2002)....

Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....