Forecasts from regression models are frequently made conditional on a set of values for the regressor variables. We describe and illustrate how to obtain forecasts when some of those regressors are stochastic and their values have not yet been realized. The forecasting device is a Bayesian...

Recent theoretical advances in total factor productivity (TFP) measurement mean that TFP indexes can now be exhaustively decomposed into unambiguous measures of technical change and efficiency change. To date, all applications of this new methodology have involved decomposing indexes that have...

In this article, we present one of the first real-world empirical applications of state-contingent production theory. Our state-contingent behavioural model allows us to analyse production under both inefficiency and uncertainty without regard to the nature of producer risk preferences. Using...

Markov Chain Monte Carlo (MCMC) methods are used to estimate a seemingly unrelated regression (SUR) system of input demand functions for U.S. agriculture. Our demand functions have flexible forms and allow for nonrandom technical inefficiency. Concavity constraints are imposed at individual data...

We formally assess competitive buying and selling behavior in the Australian grains and oilseeds industries using a more realistic empirical model and a less aggregated data set than previously available. We specify a duality model of profit maximization that allows for imperfect competition in...