Ronald C. Mittelhammer

A Semiparametric Basis for Combining Estimation Problems Under Quadratic Loss

When there is uncertainty concerning the appropriate statistical model-estimator to use in representing the data sampling process, we consider a basis for optimally combining estimation problems. The objective is to produce natural adaptive estimators that are free of subjective choices and tuning parameters. In the context of two competing multivariate linear statistical models–estimators, we demonstrate a semiparametric Stein-like (SPSL) estimator, , that, under quadratic loss, has superior risk performance relative to the conventional least squares estimator. The relationship of the SPSL estimator to the family of Stein estimators is noted, and asymptotic and analytic finite-sample risk properties of the estimator are developed for some special cases. As an application we consider the problem of combining two polar linear models and demonstrate a corresponding SPSL estimator. An extensive sampling experiment is used to investigate the finite-sample performance of the SPSL estimator over a wide range of data sampling designs and symmetric and skewed distributions. Bootstrapping procedures are used to develop confidence sets and serve as a basis for inference.

Empirical evidence concerning the finite sample performance of El-type structural equation estimation and inference methods

This paper presents empirical evidence concerning the finite sample performance of conventional and generalized empirical likelihood-type estimators that utilize instruments in the context of linear structural models characterized by endogenous explanatory variables. There are suggestions in the literature that traditional and non-traditional asymptotically efficient estimators based on moment equations may, for the relatively small sample sizes usually encountered in econometric practice, have relatively large biases and/or variances and provide an inadequate basis for estimation and inference. Given this uncertainty we use a range of data sampling processes and Monte Carlo sampling procedures to accumulate finite sample empirical evidence concerning these questions for a family of generalized empirical likelihood-type estimators in comparison to conventional 2SLS and GMM estimators. Solutions to EL-type empirical momentconstrained optimization problems present formidable numerical challenges. We identify effective optimization algorithms for meeting these challenges.

Food Stamp Participation Among Low-Income Households: Theoretical Considerations Of The Impact On The Demand For Food

The USDAs Food Stamp Program (FSP) is a major item in the departments budget. In effect from 1939 to 1943 and revived as a pilot program in 1961, FSP has grown until, in 1973, it provided nearly

AN EMPIRICAL BARGAINING MODEL OF PRICE DISCOVERY: AN APPLICATION TO THE WASHINGTON/OREGON ASPARAGUS INDUSTRY

4 billion in food stamps to an average of 12 million persons per month. About 55 percent of the

Permanent Housing for Seasonal Workers? A Generalized Peak Load Investment Model for Farm Worker Housing

4 billion is federal subsidy. The program is continuing to expand as a result of a congressional mandate that FSP be in effect nationwide after June 30, 1974. Because of the FSPs growth, questions are now being asked about the programs impact on demand for food in the United States. In its pre-World War II inception, FSP was developed as an alternative to direct distribution of commodities to relief families. Although the objective of improving food consumption among needy households was recognized, FSP was viewed primarily as a method for stimulating demand for farm products.

ASSESSING CONSUMER PREFERENCES FOR ORGANIC, ECO-LABELED, AND REGULAR APPLES

The bargaining process and its role in price discovery within the Pacific Northwest asparagus industry is analyzed using a general empirical bargaining model. Growers and processors inverse supply and demand functions define boundaries for the negotiated prices. OLS and Heckmans two-stage estimation procedures are used to estimate a stochastic bargaining model of price determination. The results indicate that basic supply and demand forces exert substantial influences on the bargaining process. In particular, expected levels of supply play a paramount role in the level of prices offered, while past prices also influence current offers. The general framework of analysis used in relation to asparagus can be generalized to other commodities where bargaining plays a role in price discovery. The model can be used to investigate the extent to which major economic forces impact bargaining behavior.

ASSESSING THE EFFECTIVENESS OF MPP AND TEA ADVERTISING AND PROMOTION EFFORTS IN THE JAPANESE MARKET FOR MEATS

Many seasonal workers are housed in transitory accommodations, including tents and vehicles. In this study, we analyze the supply side of this problem by assuming that a public agent must house the workers through direct public investment. A peak load model is adapted to develop investment rules for the least-cost provision of seasonal worker housing, adding an interacting multi-season component to existing models. Based on this model and the data from three prototype projects, the majority of the least-cost investment would be in permanent, but seasonally occupied, housing.