Linear time series models have come to dominate the macroeconomic literature on rational expectations and equilibrium business cycle theory. But the explicit solution of such models has generally required strong restrictions upon the exogenous process of stochastic shocks (e.g., temporal independence) as well as upon the values of various demand and supply elasticities. This paper exhibits a solution technique, the method of z-transforms, which does not require one to impose such restrictions. The value of this method is illustrated by applying it to completely characterize the symmetric, stationary, rational expectations equilibria of a naive linear model of land speculation. This approach also permits systematic study of the informationally asymmetric equilibria of the model. THIS PAPER develops a method for analyzing rational expectations (RE) equilibria in linear economic models. The methods I shall discuss usually enable one to determine whether or not a given model has a RE equilibrium and, if one exists, to exhibit an explicit expression for the stochastic process of equilibrium prices. The techniques apply to linear models driven by stationary processes of exogenous random shocks.

[Let the time series Y_t satisfy extlesstex-math extgreater$Y_ \= extbackslashalpha + extbackslashrho Y_ -1\+e_ $ extless/tex-math extgreater, where Y_1 is fixed and the e_t are normal independent (0, σ ^2) random variables. The likelihood ratio test of the hypothesis that (α, ρ) = (0, 1) is investigated and a limit representation for the test statistic is presented. Percentage points for the limiting distribution and for finite sample distributions are estimated. The distribution of the least squares estimator of α is also discussed. A similar investigation is conducted for the model containing a time trend.]

[A conceptual framework is suggested for integrating fixed effects and random effects models into one framework. In that framework, the pertinent distribution is a convolution of two distributions; one is a degenerate distribution. A method is suggested and analyzed for separating between the two distributions when the second distribution is normal.]

An important purpose in pooling time-series and cross-section data is to control for individual-specific unobservable effects which may be correlated with other explanatory variables, e.g. latent ability in measuring returns to schooling in earnings equations or managerial ability in measuring returns to scale in firm cost functions. Using instrumental variables and the time-invariant characteristics of the latent variable, we derive: 1. (1) a test for the presence of this effect and for the over-identifying restriction we use; 2. (2) necessary and sufficient conditions for identification of all the parameters in the model; and 3. (3) the asymptotically efficient instrumental variables estimator and conditions under which it differs from the within-groups estimator. We calculate efficient estimates of a wage equation from the Michigan income dynamics data which indicate substantial differences from within-groups and Balestra-Nerlove estimates — particularly a significantly higher estimate of the returns to schooling.

Economists have been paying increasing attention to the study of situations in which csumers face a discrete rather than a continous set of choices.Such models are potentially very important in evaluating the impact of government programs upon consi.mterwelfare.But very little has been said in general regarding the tools of applied welfare economics in discrete choice situations.This paper shows how the conventional methods of applied welfare economics can be modified to handle such cases.It focuses on the cornputation of the excess burden of taxation, and the evaluation of gua].itychange.The results are applied to stochastic utility models, including the popular cases of prohit and logit analysis.Throughout, the ernp)-asis is on providing rigorous guidelines for carrying out applied work.

In this paper [the authors discuss] five procedures for incorporating demographic variables into theoretically plausible demand systems: translating scaling and the Gorman reverse Gorman and implicit Prais-Houthakker procedures.... These five procedures are used to incorporate a single demographic variable--the number of children in a household--into the generalized CES demand system using household budget data for the United Kingdom for the period 1966-1972 (EXCERPT)

THE ARROW-PRATT MEASURES of risk aversion for von Neumann-Morgenstern utility functions have become workhorses for analyzing problems in the microeconomics of uncertainty. They have been used to characterize the qualitative properties of demand in insurance and asset markets, to examine the properties of risk taking in taxation models, and to study the interaction between risk and life-cycle savings problems to name just a few applications. Equally importantly, they have generated the linear risk tolerance class of utility functions which has provided canonical examples in such diverse areas as portfolio theory and the theory of teams. Despite these successes, there have been a number of areas for which the results have been weaker than hoped. It is natural to use the risk aversion measures to compare the behavior of individuals in risky choice situations. For example, consider the individual portfolio choice problem in a two asset world with a riskless asset and a risky asset. If individual A has a uniformly higher Arrow-Pratt coefficient of risk aversion than individual B, then B will always choose a portfolio combination with more wealth invested in the risky asset. But, suppose that both assets are risky. Now, there is no obvious sense in which the more risk averse individual can be said to hold a less risky portfolio, but it seems strange that such a simple alteration should destroy the analytics which support the basic intuition. Similarly, consider the basic insurance problem. If one individual, A, is more risk averse than another, B, in the Arrow-Pratt sense, it follows that A will pay a larger premium to insure against a random loss than will B. Typically, though, an individual evaluates partial rather than total insurance, that is, only some gambles can be insured against and others must be retained. In this case, even when the gambles which are retained are independent from those which are insured, it is no longer true that the individual whose Arrow-Pratt measure of risk aversion is higher will pay a larger insurance premium. The situation is no better when we consider comparative statics exercises for a single individual. Decreasing absolute risk aversion in the sense of Arrow and

[This paper investigates the distribution of the least squares estimator of the coefficient α in the model @c"t = @a@c"t -"1 + @?"t where the @?"t where the @?"t are independently distributed N (O, @s extasciicircum2). The exact finite sample and limiting distributions are calculated when α ≥ 1 and finite sample distributions when α extless 1. These distributions are used to compute the power functions of tests of the random walk hypothesis α = 1 as well as the hypotheses.]

A. Ronald Gallant, Alberto Holly, Statistical Inference in an Implicit, Nonlinear, Simultaneous Equation Model in the Context of Maximum Likelihood Estimation, Econometrica, Vol. 49, No. 2 (Mar., 1981), p. 554