Journal Article An Econometric Model of the Medicare System: Reply Get access Martin S. Feldstein Martin S. Feldstein Harvard University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 87, Issue 3, August 1973, Pages 490–494, https://doi.org/10.2307/1882018 Published: 01 August 1973
Journal Article The Harried Leisure Class: A Demurrer Get access Edmund S. Phelps Edmund S. Phelps Columbia University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 87, Issue 4, November 1973, Pages 641–645, https://doi.org/10.2307/1882033 Published: 01 November 1973
The traditional literature applying statistical sampling to auditing has recognized neither the special structure of auditing populations nor the unique environment in which this sampling occurs. Much of the literature is based on techniques developed for sample surveys. But the auditor typically has a great deal more information about his population than is available to the social scientist in sample surveys. Counterbalancing this, the auditor operates under much tighter precision requirements than the sample survey investigator. In this paper, we explore these issues in greater detail and show how the auditor must use statistical estimators which explicitly use all the auxiliary information available to him. We investigate a class of such estimators and show that, for typical auditing populations, the standard distribution theory is not always appropriate for statistical inference. Therefore, we conclude that entirely new approaches may be required for statistical sampling in auditing.
[The problem of collinearity suggests the search for an alternative to ordinary least squares which, although biased, might reduce the mean square error of the coefficient of interest. Two types of estimators are examined, and the corresponding mean square error loss functions are calculated.]
SOME FIFTEEN YEARS have passed since Phillips [15] first showed that the application of certain types of stabilization policies to multiplier-accelerator macroeconomic models could result in undesired oscillations or instabilities. It has become clear from this and other analyses of macroeconomic policy [1, 3, 5, 16] that, because of the dynamic structure of the economy, well-intentioned policies may have unexpected and counterintuitive results. In recent years a number of economists have demonstrated the potential application of the mathematical techniques of optimal control theory to economic policy formulation for stabilization [6, 20, 22] as well as long-run growth and development [7, 8, 12, 13, 21]. While much of this work has been successful in showing how optimal control could be applied to policy problems, there has been little attempt made to actually apply it to a realistic policy problem, particularly in the area of short-run stabilization. A goal of this paper is to show that if one is willing to work with a linear or linearized economic model and quadratic cost criteria, optimal control theory can provide a viable tool for both analyzing and understanding the dynamic properties of the model, and for formulating stabilization policies based on the model. In this paper economic stabilization will be approached as a dual tracking problem in optimal control. The problem that is defined and solved involves tracking nominal state and nominal policy trajectories, subject to a quadratic cost function and the constraint of a linear system. This is actually quite general and will enable us to penalize for variations in, as well as the levels of, the state variables and control variables. Moreover, this lets us structure the problem as one without absolute limitations on the sets of allowable controls and allowable states; any restrictions that are to be imposed on the motion of control or state variables are expressed by assigning higher costs to their deviations. We will also
Roberto S. Mariano, Approximations to the Distribution Functions of the Ordinary Least-Squares and Two-Stage Least-Squares Estimators in the Case of Two Included Endogenous Variables, Econometrica, Vol. 41, No. 1 (Jan., 1973), pp. 67-77