Colin McKenzie

Variable Addition and LaGrange Multiplier Tests for Linear and Logarithmic Regression Models

The purpose of this paper is to examine the properties of various tests of linear and logarithmic (or log-linear) regression models. The test procedures may be categorized as follows: (1) tests that exploit the fact that the two models are intrinsically non-nested; (2) tests based on the Box-Cox data transformation; and (3) diagnostic tests of functional form misspecification against an unspecified alternative. The small-sample properties of several tests are investigated through a Monte Carlo experiment, as is their robustness to non-normality of the errors. Copyright 1988 by MIT Press.

When are two step estimators efficient

Kruskals theorem is used to provide simple and elegant alternative derivations of the efficiency of some two step estimators (2SE) for models containing anticipated and unanticipated variables. Several new results are established: 2SE is not efficient for a structural equation with current and lagged values of both anticipated and unanticipated variables; 2SE is always efficient for the parameter associated with the current unanticipated variable, and for the parameter associated with the lagged unanticipated variable if there is no lagged dependent variable in the expectations equation; the inclusion of additional regressors in the structural equation and contemporaneous correlation of the structural and expectations errors can both be analysed in a straightforward manner; the single-equation generalized least squares estimator can be as efficient as the systems maximum likelihood estimator.

On efficient estimation and correct inference in models with generated regressors: A general approach

Two approaches have been developed for deriving the properties of efficiency and consistency of standard errors of two step estimators of linear models containing current or lagged unobserved expectations of a single variable. One method is based on the derivatives of the likelihood function and information matrix, while the other uses the true covariance matrix of the disturbance vector when unknown parameters or variables are replaced by corresponding estimates. In this paper, the second approach is extended to cases where the structural equation is nonlinear and the model contains expectations of more than one variable or expectations of future variables. The properties of a frequently used estimator to deal with missing observations problems, a model involving a variance as an explanatory variable, and a recently developed estimator for autoregressive moving average models can be easily derived using the results of the paper. Methods for improving the efficiency of two step estimators are outlined. JEL Classification Number: C13

Alternative forms and properties of the score test

TWO different issues relating to the score (S) test are investigated. Firstly, we study the finite sample properties of a number of asymptotically equivalent forms of the S test. From our simulation results we observe that these forms can behave very differently in finite samples. Secondly, we investigate the power properties of the S test and find that it compares favorably to those of the likelihood ratio (LR) test although the former does not use information about the precise forms of the alternatives.

Tests for normality with stable alternatives

This paper is concerned with testing normality of observations and regression disturbances when the alternative belongs to the stable family.

SIMPLE PROCEDURES FOR TESTING AUTOREGRESSIVE VERSUS MOVING AVERAGE ERRORS IN REGRESSION MODELS

This paper develops several simple separate (or non-nested) procedures for testing autoregressive versus moving average errors in regression models. These asymptotically valid tests are straightforward to calculate: after estimating both models by maximum likelihood methods, the procedure involves testing the significance of variables added to a linearized version of the null model, the added variables being the predictions, or the residuals from the specified alternative model, or the difference of the predictions of the two models. Some small sample evidence on the properties of the tests is presented, as is an empirical application on the Australian unexpected inflation rate series. JEL Classification Numbers: C12, C22, C52, E31.

Cointegration and Direct Tests of the Rational Expectations Hypothesis

The paper is concerned with direct tests of the rational expectations hypothesis (REH) in the presence of stationary and non-stationary variables. Alternative methods of converting qualitative survey responses into quantitative expectations series are examined. Testing of orthogonality and the issue of generated regressors for models estimated by two step methods are re-evaluated when the variable to be explained is stationary. A methodological approach for testing the REH is provided for models using qualitative response data when there are unit roots and cointegration, and alternative reasons are examined for rejecting the null hypothesis of orthogonality. The usefulness of cointegration analysis for both the probability and regression conversion procedures is also analysed. Cointegration is found to be directly applicable for the probability conversion approach with uniform, normal and logistic distributions of expectations and for the linear regressicn conversion approach. In the light of new techniqu...

Occupation, Retirement and Cognitive Functioning

ABSTRACT The purpose of this paper is to examine the causal impact of the duration of retirement on the cognitive functioning of male elderly workers in Japan using data from the National Survey of Japanese Elderly. We explore how the complexity of a workers longest served job affects cognitive functioning after retirement. In particular, we investigate eight dimensions of the longest served job using information listed in the United States Dictionary of Occupational Titles, namely physical demands, mathematical development, reasoning development, language development, the jobs relationship to data, the jobs relationship to people, the jobs relationship to things and the specific vocational preparation required. Our estimator takes account of the potential endogeneity of the duration of retirement and the left-censoring of the duration of retirement. Our empirical evidence suggests that the duration of retirement has a negative and significant impact on cognitive functioning. Moreover, among the eight dimensions of job characteristics, high complexity in the jobs relation to data is found to be an important job characteristic in delaying the deterioration of cognitive functioning after retirement.

Testing the rationality of expectations in the Australian foreign exchange market using survey data with missing observations

This paper is concerned with testing the rationality of exchange rate expectations in the Australian foreign exchange market when there are missing observations in the survey data on expectations due to National or other holidays. The survey data analysed contains weekly observations on 1-week and 4-week ahead forecasts of the

Money demand in an open economy

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