The problem considered in this paper is how to find reliable prediction intervals with simple exponential smoothing and trend corrected exponential smoothing. Methods for constructing prediction intervals based on linear approximation and bootstrapping are proposed.

It is well known that the usual techniques for estimating random and fixed effects panel data models are inconsistent in the dynamic setting. As a consequence, numerous consistent estimators have been proposed in the literature. However, all such estimators rely on certain well defined...

This paper is concerned with model selection based on penalized maximized log likelihood function. Its main emphasis is on how these penalities might be chosen in small samples to give good statistical properties.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not.

A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors.

In this paper, I develop a population-based Markov chain Monte Carlo (MCMC) algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium (DSGE) models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered...

Agent based models take into account limited rational behaviour of individuals acting on financial markets. Explicit simulation of this behaviour and the resulting interaction of individuals provide a description of aggregate financial market time series. Al-though the outcomes of such...

In the current paper, a one-sided version of a small sample correction to AIC is derived. This criterion will be based upon a two-sided model selection cretirion called AICs developed by Sugiura (1978) and studied in detail by Hurvich and Tsai (1989, 1991).

The purpose of this study is to investigate the effect of time-aggregation in discrete and contunous-time hazard models. A Monte Carlo study is conducted in which data are generated according to underlying contunous and discrete-time processes, which data are aggregated into daily, weekly and...

The main objective of this study is to investigate the rebustness of the popular Durbin-Watson (DW), Langrage multiplier (LM), Box-Pierce (BP) and Ljung-Box (LB) tests and their corrected versions against autoregressive distrurbances in the presence of dynamic heteroscedastic disturbances with...