It is well known that consistent estimators of errors-in-variables models require knowledge of the ratio of error variances. What is not well known is that a Joint Least Squares estimator is robust to a wide misspecification of that ratio. Through a series of Monte Carlo experiments we show that an easy-to-implement estimator produces estimates that are nearly unbiased for a wide range of the ratio of error variances. These MC analyses encompass linear and nonlinear specifications and also a system on nonlinear equations where all the variables are measured with errors.

The Dynamic Positive Equilibrium Problem (DPEP) is a methodology for dealing with time series about economic agents' decisions, regardless of the amount of available information. The approach is articulated in three phases, as in the static counterpart Symmetric Positive Equilibrium Problem (SPEP), with the variant that it must be preceded by the estimation of the equation of motion which characterizes a dynamic model. Furthermore, the definition of marginal cost in the DPEP model is different from the same notion in the static SPEP. In this paper, the DPEP approach was applied to a panel data dealing with annual crops from California agriculture for a horizon of eight years. The dynamic character of the DPEP model is based upon then assumption of output price adaptive expectations that follows a Nerlove-type specification.

This paper presents a theory of technical progress that interprets the price-induced conjecture of Hicks. It provides also an exhaustive set of comparative statics conditions that constitute the scaffolding for an empirical test of the theory. A crucial assumption is that entrepreneurs make decisions about techniques on the basis of expected information about prices and quantities. Another assumption is that these decisions are made in order to fulfill a profitability objective. The novelty of our approach is that expected relative prices enter the production function as shifter of the technology frontier. The consequence of this assumption is an expansion of the traditional Shephard lemma that is useful for identifying the portion of input quantities that have been determined by the conjecture of price-induced technical progress (PITP). The theory is applied to a sample of 80 years of US agriculture. Three versions of the general model are presented. The first version deals only with expected relative prices. The empirical results do not reject the PITP hypothesis. The second and third versions introduce lagged expected relative prices, lagged R&D expenditures and lagged extension expenditures as explanatory variables of the portion of the input quantities that may be attributable to technical progress.

With the advent of the almost ideal demand system (AIDS) of Deaton and Muellbauer, the estimation of consumer demand functions revolves around specifications that use flexible functional forms of the indirect utility function. This dual approach has put on the backburner the traditional primal approach because the direct utility function exists only in a latent state. The lack of explicit, analytical invertibility of either system, however, is an indication that focusing exclusively on the dual side of the consumer problem is equivalent to disregard potentially important and independent information residing with the primal side. This paper suggests that efficient estimates (in the sense of using all the available information) of the demand functions require the joint estimation of all the primal and dual relations. The specification of this objective assumes that risk- neutral households maximize their expected utility subject to their expected budget constraint. This theoretical framework leads to a two-step procedure that produces consistent and efficient estimates of the model’s parameters. The generality of the approach proposed here can handle also the frequently encountered case when all the sample units face the same observed commodity prices. Finally, we present a general solution of the nonlinear errors-in-variables problem with a novel estimation procedure that avoids the pitfalls of the traditional approach.

Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls of their own. The ridge estimator is not generally accepted as a vital alternative to the ordinary least-squares (OLS) estimator because it depends upon unknown parameters. The generalized maximum entropy (GME) estimator of Golan, Judge and Miller depends upon subjective exogenous information that affects the estimated parameters in an unpredictable way. This paper presents novel maximum entropy estimators inspired by the theory of light that do not depend upon any additional information. Monte Carlo experiments show that they are not affected by any level of multicollinearity and dominate OLS uniformly. The Leuven estimators are consistent and asymptotically normal.