Yuzo Honda

A size correction to the Lagrange multiplier test for heteroskedasticity

Abstract Making use by the recent work by Harris, this paper provides the formula for size correction to the Lagrange multiplier test for heteroskedasticity. Monte Carlo experiments indicate that our size correction is effective in improving the accuracy of the size of the test. Since the Lagrange multiplier test tends to have a true size less than the nominal size in our experiments, the application of the test at a higher true significance level tends to increase the power of the test as well. Reasonable results in our numerical experiments also give a partial check on the validity of the derived formula.

Production uncertainty and the input decision of the competitive firm facing the futures market

Abstract Production uncertainty is introduced into the model of the competitive firm facing the futures market. The firms input decision is shown to be highly sensitive to the nature of the production uncertainty.

A standardized test for the error components model with the two-way layout

Abstract This paper proposed a standardized test for the error components model with the twoway layout. The proposed test statistic has mean zero and variance one in finite samples under the null hypothesis, and will improve the accuracy of the size of the test.

Small sample properties of the mixed regression estimator

Abstract In this paper, the small sample properties of the mixed regression estimator are examined when prior information may be biased and when the ration of the variance of the prior restriction errors to the variance of the sample errors is unknown. The mean square error of the mixed regression estimator is derived, and it is shown that the mixed regression estimator gets dominated by the ordinary least squares estimator in terms of the mean square error as the bias of prior information gets larger.

On small sample properties of the mixed regression predictor under misspecification

This paper relaxes the Mittelhammers (1981) assumption that the value of the true variance is known in the mixed regression model and examines the small sample, properties of the feasible mixed regression predictor under misspecification. The paper shows that the feasible mixed regression predictor is not always superior to the ordinary least squares predictor in terms of the weak mean square error when there exist omitted variables in the model. Further it shows that misspecificstion works favorably for the ordinary least squares predictor.

Information criteria in identifying regression models

This paper proposes to use information criteria to discriminate the standard regression model from error components models, heteroskedastic models, or models with autocorrelated errors.

Algebraic equivalences among Wald, LM and Hausman's tests in the linear regression model

Abstract Under certain conditions, the Hausmans statistic is identical to the Wald statistic, if we use the unconstrained maximum likelihood estimate for the variance of disturbances, while it becomes identical to the LM statistic, if we use the constrained maximum likelihood estimate for the variance.

Risk, Risk Aversion and Many Control Variables

Since the path-breaking work of Rothschild and Stiglitz (1970, 1971), comparative statics theorems of economic choice under uncertainty have been accumulated as well as successful applications of theorems. To examine the effects of uncertainty on economic choice, these theorems took one of the following two approaches. The first approach changes a random variable with utility functions unchanged, and examines its effects on the optimal economic choice. The literature along this line of thought includes Rothschild and Stiglitz sufficiency conditions for comparative statics (1971), a theorem about a mean preserving introduction of risk in Kraus (1979), and theorems about some special type of mean preserving spread in Feder (1977). These theorems may be called comparative statics theorems of risk. The second approach, on the other hand, changes utility functions with a random variable unchanged, and investigates its effects. Examples taking this path are the theorems of increased risk aversion in Diamond and Stiglitz (1974) and in Baron (1973). These theorems may be called comparative statics theorems of risk aversion.