Chris D. Orme

Worker Absenteeism: An Analysis Using Microdata

This paper reports initial results from a study of worker absenteeism in the context of an experience-rated sickpay scheme. Under the scheme, workers are assigned to three discrete grade according to their absence history. The authors concentrate on estimating the effect of the grade boundaries on absence behavior and find significant effects on duration of absences, but not on their incidence. The data are drawn from payroll and personnel records of three manufacturing plants employing around 3,500 workers. They cover a period of eighteen months. Copyright 1991 by Royal Economic Society.

The Small Sample Performance of the Information Matrix Test

Abstract This paper reviews the Information-Matrix (IM) test procedure [see White (1982)]. It investigates the size performance of several variants of the IM test statistic and, in particular, exposes the ‘nR2’ variant of the test, as proposed by Chesher (1983) and Lancaster (1984), as being extremely poor. Alternative ‘nR2’ IM test statistics are considered and it appears that substantial improvements can be made by incorporating expected values of third derivatives of the log-density into the IM test calculation procedure. Interestingly, these are exactly the sort of calculations that the Chesher (1983) and Lancaster (1984) variant avoids.

The Sensitivity of Some General Checks to Omitted Variables in the Linear Model

This paper examines the usefulness of H. Whites (1982) information matrix test and test for heteroskedasticity of unspecified form as general checks of adequacy in the context of linear models. Asymptotic analysis and Monte Carlo results are provided, with the latter covering a number of general misspecification tests. Comments are made on the value of testing for normality. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Testing for skewness of regression disturbances

Abstract The importance of testing for symmetry of regression disturbances is discussed and it is argued that it is useful to employ a test that it is robust to non-normality. A suitable large sample test is provided.

The robustness, reliabiligy and power of heteroskedasticity tests

Several tests for heteroskedasticity in linear regression models are examined. Asymptoticrobustness to heterokurticity, nonnormality and skewness is discussed. The finite sample eliability of asymptotically valid tests is investigated using Monte Carlo experiments. It is found that asymptotic critical values cannot, in general. be relied upon to give good agreement between nominal and actual finite sample significance levels. The use of the bootstrap overcomes this problem for general approaches that lead to asymptotically pivotal test statistics. Power comparisons are made for bootstrap tests and modified Glejser and Koenker tests are recommended.

Simulation-based tests for heteroskedasticity in linear regression models: Some further results

Journal of Econometrics 122 Dufour, Khalaf, Bernard and GenestAs shown by the results of Dufour, Khalaf, Bernard and Genest (2004, Journal of Econometrics 122, 317-347), exact tests for heteroskedasticity in linear regression models can be obtained, by using Monte Carlo (MC) techniques, if either (i) it is assumed that the true form of the error distribution under homoskedasticity is known, or (ii) the null hypothesis specifies both homoskedasticity and the form of the error distribution. Non-parametric bootstrap tests of homoskedasticity alone are only asymptotically valid, but do not require specification of the error law. Since information about the precise form of the error distribution is not often available to applied workers, two questions merit attention. First, if the primary purpose is to check for heteroskedasticity, how sensitive are MC tests to incorrect assumptions/claims about the error distribution? Second, what can be said about the relative merits of MC tests and non-parametric bootstrap tests? Theoretical results relevant to these two questions are derived using asymptotic analysis and evidence is provided from simulation experiments.

The Asymptotic Distribution of the F-Test Statistic for Individual Effects

(the number of cross-sections) and T is fixed (the number of time periods). Three theoretical results emerge: (i) the standard F-test procedure will still deliver asymptotically valid inferences; (ii) under (pure) local random effects, the F-test and random effects test procedures have identical asymptotic power; (iii) under local fixed, or random effects which are correlated with the regressors, the F-test will have higher asymptotic power than the random effects test. Copyright Royal Economic Society 2006

Controlling the significance levels of prediction error tests for linear regression models

This paper provides evidence on problems associated with using standard tests for predictive failure when the errors of a linear regression model are not normally distributed. The ability of a simple bootstrap procedure to give a useful degree of control over the significance levels is examined.

First order asymptotic theory for parametric misspecification tests of GARCH models

This paper develops a framework for the construction and analysis of parametric misspecification tests for generalized autoregressive conditional heteroskedastic (GARCH) models, based on first-order asymptotic theory. The principal finding is that estimation effects from the correct specification of the conditional mean (regression) function can be asymptotically nonnegligible. This implies that certain procedures, such as the asymmetry tests of Engle and Ng (1993, Journal of Finance 48, 1749–1777) and the nonlinearity test of Lundbergh and Terasvirta (2002, Journal of Econometrics 110, 417–435), are asymptotically invalid. A second contribution is the proposed use of alternative tests for asymmetry and/or nonlinearity that, it is conjectured, should enjoy improved power properties. A Monte Carlo study supports the principal theoretical findings and also suggests that the new tests have fairly good size and very good power properties when compared with the Engle and Ng (1993) and Lundbergh and Terasvirta (2002) procedures.

On the Behavior of Conditional Moment Tests in the Presence of Unconsidered Local Alternatives

This paper employs asymptotic local analysis in order to examine the sensitivity of individual conditional moment tests to sources of misspecification for which they were not specifically designed. In the context of maximum likelihood estimation, the authors find an exact equivalence between the conditions for asymptotic insensitivity of tests and their asymptotic independence. This equivalence does not hold for models estimated by extremum methods. Examples are given to illustrate the general results. Copyright 1996 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.