The question of variable selection in a regression model is a major open research topic in econometrics. Traditionally two broad classes of methods have been used. One is sequential testing and the other is information criteria. The advent of large datasets used by institutions such as central...

Interest in the interface of nonstationarity and nonlinearity has been increasing in the econometric literature. This paper provides a formal method of testing for nonstationary long memory against the alternative of particular forms of nonlinerarity. The nonlinear models we consider are ESTAR...

We consider an alternative use of simulation in the context of using the Likelihood-Ratio statistic to test non-nested models. To date simulation has been used to estimate the Kullback-Leibler measure of closeness between two densities, which in turn 'mean adjusts' the Likelihood-Ratio...

The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modelled by a small number of indicator variables. Throughout this extensive research activity...

This paper proposes and discusses an instrumental variable estimator that can be of particular relevance when many instruments are available. Intuition and recent work (see, e.g., Hahn (2002)) suggest that parsimonious devices used in the construction of the final instruments, may provide...

The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest use of the principal component methodology of Stock and Watson (2002) for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard (1994). The method is...

Datasets in a variety of disciplines require methods where both the sample size and the dataset dimensionality are allowed to be large. This framework is drastically different from the classical asymptotic framework where the number of observations is allowed to be large but the dimensionality...

This paper suggests a new nonparametric testing procedure for determining the rank of nonstationary multivariate cointegrated systems. The asymptotic properties of the procedure are determined and a Monte Carlo study is carried out.

In this note we suggest a new iterative least squares method for estimating scalar and vector ARMA models. A Monte Carlo study shows that the method has better small sample properties than existing least squares methods and compares favourably with maximum likelihood estimation as well.

In this note we look at sufficient conditions for stationarity of a simple random coefficient model and find that this model is guaranteed to be stationary under strict conditions.<br> <br> J.E.L. classification codes.