Pre‐test Estimation and Testing in Econometrics: Recent Developments

Abstract

This paper surveys a range of important developments in the area of preliminary-test inference in the context of econometric modelling. Both pre-test estimation and pre-test testing are discussed. Special attention is given to recent contributions and results. These include analyses of pre-test strategies under model mis-specification and generalised regression errors; exact sampling distribution results; and pre-testing inequality constraints on the model's parameters. In many cases, practical advice is given to assist applied econometricians in appraising the relative merits of pre-testing. It is shown that there are situations where pre-testing can be advantageous in practice

The impact of a Hausman pretest on the size of a hypothesis test: The panel data case

“…It has been long known that pretests have an impact on the risk properties of estimators and the size properties of tests, see Giles and Giles (1993) and Judge and Bock (1978) for an excellent comprehensive survey of the pretest literature, and for more recent references Guggenberger (in press). If one were to consider the regression-based version of the Hausman test (see e.g.…”

Measuring the impact of R&D on Productivity from a Econometric Time Series Perspective

“…However, if research activity is undertaken in a given year is it any more likely to have an impact on productivity immediately or in 5 or 10 years time? That biases will be induced by any SRA is well known (Giles and Giles, 1995;Judge et al, 1985), but often ignored. Moreover, alternative approaches are often dismissed in favour of the SRA without due consideration.…”

Asymptotic behaviour of regression pre-test estimators with minimal Bayes risk

“…Using a traditional PTE, one prefers b a to b a ð0Þ if jtj is greater than a certain critical value c. Traditional PTE have been intensively investigated, the literature of the related periods is thoroughly discussed in Judge and Bock (1978), Giles and Giles (1993) and Magnus (1999). In complex procedures selecting a linear model, t-tests are often used in several steps, see e.g., Hoover and Perez (1999).…”