In this paper the authors study the problem of non parametric estimation of an unknown regression function from dependent data with sub-Gaussian errors. As a particular case, they handle the autoregressive framework.

In this poper we present a consistent spacification test of a parametric regression function against a general nonparametric alternative. The proposed test is based on wavelet estimation ant it is shown to have similar rates of convergence to the more commonly used kernel based tests.

We establish the relationships between certain Bayesian and classical approaches to instrumental variable regression. We determine the form of priors that lead to posteriors for structural parameters that have similar properties as classical 2SLS and LIML and in doing so provide some new insight...

This paper considers residuals for time series regression. Despite much literature on visual diagnostics for uncorrelated data, there is little on the autocorrelated case. In order to examine various aspects of the fitted time series regression model, three residuals are considered. The fitted...

One form of data reduction is cluster analysis which is used to separate detailed data into consistuent groups. In this paper we illustrate a useful application of cluster analysis to the data reduction of detailed discrete responses of a type that are often found in large surveys.

Based on Monte Carlo simulations using both stationary and nonstationary data, a model selection approach which uses the SIC to select a "best" group of forecasts in the context of forecast combination regressions dominates a number of other techniques, including the standard t-statistic...