Research Database

Some Revenue Sharing programs, including the Federal government’s General Revenue Sharing program, reward higher tax effort with larger aid payments. A natural, game-theoretic generalization of the standard consumer demand based theory of grants-in-aid is used to examine the impacts such tax effort provisions have on the recipient government’s tax effort, spending levels, and welfare. Nonlinear simulation is used to provide rough quantitative estimates of the impacts General Revenue Sharing had in 1972.

On our interpretation, real bills advocates favor unfettered intermediation, while their critics, who we call quantity theorists, favor legal restrictions on intermediation geared to separate “money” from “credit.” We display examples of economies in which quantity-theory assertions about “money-supply” and price-level behavior under the real bills regime are valid. In particular, both the price level and an asset total that quantity theorists would identify as money fluctuate more under a real bills regime than under a regime with restrictions like those favored by quantity theorists. Despite this, the Pareto criterion does not support the quantity-theory position.

This paper proposes a method for estimating the parameters of continuous time, stochastic rational expectations models from discrete time observations. The method is important since various heuristic procedures for deducing the implications for discrete time data of continuous time models, such as replacing derivatives with first differences, can sometimes give rise to very misleading conclusions about parameters. Our proposal is to express the restrictions imposed by the rational expectations model on the continuous time process generating the observable variables. Then the likelihood function of a discrete time sample of observations from this process is obtained. Parameter estimates are computed by maximizing the likelihood function with respect to the free parameters of the continuous time model.

This paper presents a frequency-domain technique for estimating distributed lag coefficients (the impulse-response function) when observations are randomly missed. The technique treats stationary processes with randomly missed observations as amplitude-modulated processes and estimates the transfer function accordingly. Estimates of the lag coefficients are obtained by taking the inverse transform of the estimated transfer function. Results with artificially created data show that the technique performs well even when the probability of an observation being missed is one-half and in some cases when the probability is as low as one-fifth. The approximate asymptotic variance of the estimator is also calculated in the paper.

This paper reconsiders the aliasing problem of identifying the parameters of a continuous time stochastic process from discrete time data. It analyzes the extent to which restricting attention to processes with rational spectral density matrices reduces the number of observationally equivalent models. It focuses on rational specifications of spectral density matrices since rational parameterizations are commonly employed in the analysis of the time series data.

Iterated contraction by dominance produces a generalized equilibrium. This solution to game theory is motivated, generated, analyzed, and compared to Nash equilibrium. One implication drawn is that a realized event in a social situation need not be uniquely determined by simple individual choices, even though the preference orderings implying those choices are the appropriate primitive.

In a recent article, J. A. Kay has proposed a useful measure of the deadweight loss arising from a commodity tax system. The measure answers the question, How much more would the taxed consumer be willing to pay in a lump sum rather than as a commodity tax? Kay’s computation of the marginal deadweight loss does not yield the change in this measure for small changes in commodity tax rates, however. This note clarifies Kay’s otherwise excellent contribution, derives the measure for Cobb-Douglas utilities, and examines a useful property of it.

This paper describes the continuous time stochastic process for money and inflation under which Cagan’s adaptive expectations model is optimal. It then analyzes how data formed by sampling money and prices at discrete points in time would behave.

A prediction formula for geometrically declining sums of future forcing variables is derived for models in which the forcing variables are generated by a vector autoregressive-moving average process. This formula is useful in deducing and characterizing cross-equation restrictions implied by linear rational expectations models.

This paper illustrates how to use instrumental variables procedures to estimate the parameters of a linear rational expectations model. These procedures are appropriate when disturbances are serially correlated and the instrumental variables are not exogenous.