Anthony Hall

Diagnostic tests as residual analysis

Many applied workers are strongly oriented to residual analysis for assessing model adequacy. Formal test statistics of adequacy however are frequently derived from likelihood theory, particularly through Lagrange Multipliers. In contraGt, the present paper derives the formal statistics by concentrating Upon the distribution of residuals. It is shown that most existing tests can be derived in this way from a few elementary principles of specification analysis. One advantage of this alternative methodology is that it highlights some difficulties in existing approaches and simultaneously indicates a resolution of them; a good example being testing for heteroscedasticity in simultaneous equations. Other issues such as independence and robustness of diagnostic tests are also easily explored within the proposed framework.

Assessing the variability of inflation

Although there has been much argument over the impact of variable inflation rates upon economic performance, there has been surprisingly little attempt to define the term variability of inflation carefully or to test proposed hypotheses connecting variability and the level of inflation. Precise definitions are given in the paper and a model is constructed showing that the level/variability hypothesis may be formulated in terms of the presence of heteroscedasticity in a regression model. This theoretical model is used to criticize existing studies, while an empirical study with Australian data illustrates the application of the approach.

A Monte Carlo Study of Some Tests of Model Adequacy in Time Series Analysis

In this article, we conduct a Monte Carlo experiment of several computationally attractive tests of model adequacy in time series analysis. The experiment is designed to examine the small-sample properties of tests of fitted autoregressive and moving average models, to compare the performances of asymptotically equivalent procedures, to consider the effects on estimated significance levels and powers of testing against both appropriate and inappropriate alternatives, to evaluate the effects on power of progressively (and unnecessarily) overfitting a model, and to examine the robustness of the tests to departures from normality.

Worldwide Rankings of Research Activity in Econometrics: 1980–1985

We provide rankings of academic institutions according to the publication record of their faculty members in the specialization of econometrics. The rankings are based on standardized page counts of articles published by faculty members in 14 journals over the period 1980 to 1985. Separate rankings of the 200 leading institutions are provided for their theoretical contributions, as well as their contributions in theory and applied work. The rankings are compared with a ranking in the general field of economics.

Worldwide Rankings of Research Activity in Econometrics: An Update: 1980–1988

Fourteen leading international journals that publish econometrics articles are used to provide data on institutional activity in econometrics over the period 1980–1988. From this data base, institutional rankings are constructed separately for theoretical econometrics and all econometrics publications, according to standardized page counts of articles published in these journals. Some indication is given as to how institutional rankings have changed over this period.

Tests of non-nested linear regression models subject to linear restrictions☆

Abstract The standard methods for testing between non-nested or separate regression models require that the models have the same dependent variable. If the models are subject to non-homogeneous linear restrictions, substituting out these restrictions results in a redefinition of the dependent variable before estimation and the standard results will not apply. We derive a Wald-type test statistic to cater for this situation.