Journal Article Corporate Taxation and Dividend Behaviour: a Reply and Extension Get access Martin S. Feldstein Martin S. Feldstein Harvard University Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 39, Issue 2, April 1972, Pages 235–240, https://doi.org/10.2307/2296876 Published: 01 April 1972

Journal Article Wage Bargains, Threshold Effects, and the Phillips Curve: A Reply Get access Daniel S. Hamermesh Daniel S. Hamermesh Princeton University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 86, Issue 2, May 1972, Pages 339–341, https://doi.org/10.2307/1880570 Published: 01 May 1972

I. Introduction, 175. — II. The optimal price, 176. — III. The welfare loss due to incorrect pricing, 182. — IV. An example, 183. — V. Concluding remarks, 187.

Wallace and Hussain (1969) considered the use of an error components regression model in the analysis of time series of cross-sections and developed an estimator of the coefficient vector based on an estimated variance-covariance matrix of error terms. In this paper, we have shown that under the set of assumptions adopted by Wallace and Hussain there are an infinite number of estimators which have the same asymptotic variancecovariance matrix as the Wallace-Hussain estimator and also that it is not possible to choose an estimator on the basis of asymptotic efficiency. We have developed an alternative estimator of the variance-covariance matrix of error terms and have used this estimator in developing a feasible Aitken type estimator for the coefficient vector. We have derived some small sample properties of this estimator and have compared them with those of other estimators of the coefficient vector.

Holmes' comment was addressed to Dr. Watson who had just expressed astonishment that Holmes was ignorant of the Copernican theory of the solar system. In truth, Holmes' reply to Watson expressed my exact sentiments after reading Ronen's review. Why? Because I found that I would have to answer the following question in the negative after almost every point he made: Would I do anything differently in view of what Ronen has stated?

[This survey of the use of structural equation models and methods by social scientists emphasizes the treatment of unobservable variables and attempts to redress economists' neglect of the work of Sewall Wright.]

[This paper deals with two single-equation estimators in a set of simultaneous linear stochastic equations--namely, ordinary least squares (OLS) and two-stage least squares (2SLS). Under the assumption that all predetermined variables in the model are exogenous, necessary and sufficient conditions are obtained for the existence of even moments of the above estimators. It is shown that for the general case with an arbitrary number of included endogenous variables, even moments of the 2SLS estimator are finite if and only if the order is less than K2 - G1 + 1. Furthermore, even moments of the OLS estimator exist if and only if the order is less than N - K1 - G1 + 1, where N is the sample size, G1 + 1 is the number of included endogenous variables, K1 and K2 respectively are the number of included and excluded exogenous variables in the equation to be estimated.]

1. TO BE CLEAR from the beginning, this note does not provide practical aids to identification; the information is very unlikely to be available. Rather, this paper serves an expository purpose, that of seeking to clarify two ideas: first, multiple-identification, by presenting and analyzing an example within a linear system, and second, Working's and Fisher's ideas on relative variances as identification aids, by showing the consequences of knowing that one variance is a specific large multiple of another. Along the way we provide a correction to the econometrics literature.

We do not have any quarrel with the statistical analysis in Kinney's note. In our first paper on this subject, we acknowledged that an auditor may sample high-valued items because of their normal association with high variance.' Similarly, sampling from a high error rate category may be explained as contributing to representative sampling because of the higher degree of contribution in reducing the error of estimate. Finally, random sampling is consistent with the auditor wishing to make statistically valid statements about the population from which a sample is chosen. Our papers,2 however, attempted to point out that there may be nonstatistical reasons which also explain the above set of auditors' behavior; i.e., auditors may sample high-valued items to cover as much of the total dollar value in the population as possible (protective sampling); they may sample high error rate categories to correct as many items as possible; and they may use random sampling to create a greater degree of uncertainty as to the scope of future audits to deter potential errors in the future (preventive sampling).