A recent class of factor demand models is discussed and used to analyze U.S. state-level production data. The approach accommodates output risk, heterogeneous technologies, technological change, endogenous variables, aggregation across agents, and more general flexible functional forms than...

Cost function and factor demand estimation and measurement are among the most useful tools in economic analysis. This paper defines cost function flexibility in a new way that extends results and concepts from consumer demand theory to models of joint production. This applies to production...

Agricultural production is subject to supply risk. Expected and realized farm outputs and output prices are unknown and unobservable when inputs are chosen. Crop and livestock production decisions are linked over time. Producers’ expectations are particularly difficult to model. This paper...

An econometric model of the U.S. dairy market is estimated for the period 1950–80. The economic costs of the dairy program for the period 1965–80 are calculated using ex ante, ex post, and long-run demand and supply functions. Results indicate that the costs of the dairy program...

Dynamic regression equations are estimated for each beef cattle breeding herd and beef cattle inventories at two levels of aggregation, the U.S. and Montana. The analysis for Montana was utilized as a guide for specification of the national equation to reduce the inference problem associated...

A common feature of certain kinds of data is a high level of statistical dependence across space and time. This spatial and temporal dependence contains useful information that can be exploited to significantly reduce the uncertainty surrounding local distributions. This chapter develops a...

The focus of this paper is the impact on welfare measurement of a common assumption in applied demand analysis, weak separability. A standard empirical practice is to estimate conditional demand models for a set of goods which are assumed to be weakly separable from all other goods with...