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.

In this paper we propose a Bayesian method for estimating hyperbolic diffusion models. The approach is based on the Markov Chain Monte Carlo (MCMC) method after discretization via the Milstein scheme. Our simulation study shows that the hyperbolic diffusion exhibits many of the stylized facts...

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...

In this paper two new estimators are offred (one each for the fixed random effects specifications), and small sample performance compared with that of all the existing estimators.

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long term context, planners must adopt a probabilistic view of potential peak demand levels, therefore density forecasts (providing estimates of the...

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

This paper develops a new non-linear model to analyse the business cycle by exploiting the relationship between the asymmetrical behaviour of the cycle and leading indicators. The model proposed is an innovations form of the structural model underlying simple exponential smoothing that is...

In this paper the EM algorithm, which has been used successfully with censored and incomplete data sets, is adapted to the problem of scrambled data. The performance of the method is assayed using an artificially constructed data set. The relevance of the results for a real world labour market...

This paper presnets a method for simultaneously estimating a system of nonparametric multiple regressions which may seem unrelated, but where the errors are potentially correlated between equations. We show that the prime advantage of estimating such a 'seemingly unrelated' system of...