Knowledge that Transforms

[This paper presents a Bayesian analysis of a single equation from a simultaneous equations system. The analysis is carried out under "limited information" because no prior information (other than a list of endogenous and exogenous variables) is introduced on the parameters of the remaining equations in the in the system. These parameters are integrated out analytically. The equation of interest may or may not be identified by means of exact a priori information; probabilistic prior information is equally acceptable. The prior density is either of the non-informative or the natural conjugate type. The kernel of the posterior density for the regression coefficients is a ratio of t kernels. The existence of posterior moments is ascertained. This approach is applied for illustrative purposes to Tintner's model of the meat market.]

A multiple equation nonlinear regression model with serially independent disturbances is considered. The estimation of the parameters in this model by maximum likelihood and minimum distance methods is discussed and our main subject is the relationship between these procedures. We establish that if the number of observations in a sample is sufficiently large, the iterated minimum distance procedure converges almost surely and the limit of this sequence of iterations is the quasi-maximum likelihood estimator.

The paper develops a simple iterative procedure for deriving linear decision rules which provide the optimal control policy for a stochastic dynamic linear system. The procedure works for a quadratic objective function with any time horizon up to and including infinity, either with or without time discounting. The role of target variables is conisidered and there is a discussion of the results which ensue if these targets are incompatible, that is, if they do not satisfy the underlying structural model. The paper concludes with some consideration of the convergence and other properties of the controlled system. THIS PAPER DEVELOPS a simple iterative method for deriving linear decision rules which provide the control policy for a stochastic dynamic linear system which is optimal for a quadratic criterion. The basic theory in economics was developed by Holt, Simon, Theil, Phillips, and others2 in the fifties and has recently been extended by Aoki [1], Chow [2 and 3], and Turnovsky [10]. The method described here is similar to that used by Chow [3] where the dynamic structure of the model is used to develop a suitable iterative procedure. This procedure is computationally simple, of low dimensionality, and may be applied to a system with any number of lags, irrespective of whether it is stable or unstable. For economic applications, the underlying system would typically be an econometric model in reduced form which has either been specially estimated as a completely linear model or has been suitably linearized. In Section 2 of the paper we derive a general procedure for solving an infinite horizon quadratic programming problem, proving both its convergence and optimality properties. In Sections 3 and 4 we discuss how this procedure may be adapted to solve finite and infinite horizon stochastic control problems and demonstrate some properties of the optimal path. Since the method produces an analytically explicit solution we are enabled to develop some further convergence properties of the infinite horizon, optimal path in Section 5. The specific control problem to be discussed in this paper is one of the following

[In this paper, we propose a procedure based on the use of the Moore-Penrose inverse of matrices for deriving unique indirect least squares (ILS) estimates of the structural parameters in the overidentified case. The procedure makes use of all reduced form estimates in deriving the unique structural estimates. The estimator is shown to be consistent. We derive the relationship between this estimator, the two stage least squares (2SLS) estimator, and instrumental variables (IV) estimators. We also derive the asymptotic distribution of the proposed estimator, and extend the procedure to a full information ILS estimator (FILS). The results of sampling experiments are summarized.]

THE MEASUREMENT of economic inequality is a timely and important topic. Often the Gini index or the entire Lorenz curve is used; however, the relative mean deviation (or Pietra ratio) has been used by Schutz [9] and Budd [1] to study United States data. Eltet6 and Frigyes [2] developed new measures to aid in their analysis of Hungary's income distribution, and Kondor [6] has shown that these new indices are related to the relative mean deviation. In order to draw valid conclusions from actual samples, one needs to know the sampling distribution of the statistic used to estimate the measure of inequality. The purpose of the present paper is to adapt methods used by the author [3 and 4] in another context to obtain the large sample theory of the mean deviation, Pietra ratio, and the measures of Eltet6 and Frigyes. Since several of these measures estimate some parameters of the underlying income distribution function, the asymptotic theory of the estimators is more complicated than might appear at first glance.

The primary aim of this paper is to propose a new measure of poverty, which should avoid some of the shortcomings of the measures currently in use. An axiomatic approach is used to derive the measure. The conception of welfare in the axiom set is ordinal. The information requirement for the new measure is quite limited, permitting practical use.

Charitable contributions are an important source of basic finance for a wide variety of private nonprofit organizations that perform quasi-public functions. The tax treatment of charitable contributions substantially influences the volume and distribution of these gifts. The current study presents new estimates of the price and income elasticities of charitable giving. The parameter estimates are then used with the United States Treasury Tax File to simulate the effects of several possible alternatives to the current tax treatment of charitable giving. INDIVIDUAL CHARITABLE CONTRIBUTIONS are an important source of basic finance for a wide variety of private nonprofit organizations. Higher education, research, health care, the visual and performing arts, welfare services, and community and religious activities rely heavily on the voluntary institution. In 1970, American families contributed more than $17 billion for their support. The volume and distribution of charitable gifts is influenced by the personal income tax treatment of charitable contributions. There are today a number of widely discussed proposals for changing these rules. The appropriate tax treatment of such gifts involves a complex series of economic issues. Critical to a resolution of these issues is an understanding of the likely quantitative effects of alternative tax rules: the effects on the total volume of charitable gifts and its distribution among the different types of donees; the effects on the distribution of tax burdens