Knowledge that Transforms

Strotz (1956) and Pollak (1968) were among the first to study the behaviour of an economic agent whose preferences change over time. They suggested that such an agent would choose a “consistent plan” which they described as “the best plan that he would actually follow”. A Markov-consistent plan has a particularly simple structure: current decisions are independent of past decisions, except insofar as past decisions affect the current values of state variables. Unfortunately, Markov-consistent plans do not generally exist. In this paper, we demonstrate that the existence problem dissappears for finite horizon problems when one introduces even a small amount of smooth uncertainty into production.

I analyse occupational licensing as an input regulation that requires minimum levels of human capital investment by professionals. By raising professionals' training levels, licensing helps alleviate moral hazard problems associated with the provision of high quality services. I also consider certification, whereby consumers are provided information about professionals' training levels. I show that licensing and certification tend to benefit consumers who value quality highly at the expense of those who do not. Licensing may raise total surplus if sellers' investments are not observable, but is Pareto-worsening if training levels are observable. Certification may, however, lead to excessive investment as a signalling device, and thus be Pareto Inferior to either licensing or to a policy of laissez-faire.

An identified model is not necessary for statistical inference, but ambiguities can arise. This paper examines some simple examples and proposes a framework that distinguishes between the “refutation” and “confirmation” aspects of testing in an unidentified model. One particular problem is the interpretation given to overidentifying restrictions: a common view is that these are somehow not properly testable.

This paper will exploit the structural similarity of the two period overlapping generations model and the dynamic capital accumulation model to provide a pricing characterization of Pareto optimal distributions. With some very simple structural interpretations, the techniques which were developed to characterize efficient capital accumulation, apply directly to the overlapping generations model of the most general form; where generations consist of heterogenous consumer types of varying lifespans.

Because sunspot equilibria seem to be of central importance for an understanding of rational expectations, we seek here to characterize completely a limited class of sunspot equilibria (stationary ones with two possible natural events) in the simplest overlapping generations model of production. We present a sufficient condition for the existence of stationary sunspot equilibria, examine how these are related to strictly periodic equilibria of the same order, and investigate how deterministic stationary equilibria bifurcate to stationary sunspot equilibria. A concluding section examines how our results survive in more general settings.

Overlapping generations models with or without production or a portfolio demand for money display a fundamental indeterminacy. Expectations matter; and they are not, in the short run, constrained by the hypotheses of agent optimization, rational expectations, and market clearing. No short run policy analysis is possible without some explicit understanding of how agents expect the economy to respond to the policy. In this framework of perfect foresight and market clearing prices, it is possible to make Keynesian assumptions about the rigidity of money wages and the exogeneity of “animal spirits” of investors, to use the standard IS-LM apparatus, and to derive Keynesian conclusions about the short run effectiveness of policy. Alternatively, starting from different but no less rational expectations, one can derive the “new classical” neutrality propositions.

This paper aims to provide a unified treatment of the properties of two stage estimators. General conditions are set forth for consistency, efficiency and correct inferences. Applications of the general theorems are made to models with expectations and diagnostic tests. The general approach frequently enables much simpler derivation of existing results, and provides a number of new ones.

This paper provides a framework for testing for a unit root in an observed time series against some alternatives considered previously by Anderson (1948). Some new tests for the unit root null hypothesis for the errors affecting a classical regression model against the non-stationary (including explosive) alternative hypothesis are developed. The previous results of Sargan and Bhargava (1983) and the new test statistics are then applied to test the simple random walk and the random walk with a constant drift null hypotheses against stationary and non-stationary one-sided alternatives. In each case, the test statistic is simplified in order that it could be viewed as a von Neumann type ratio and the exact significance points are tabulated. Finally, the unit root null hypotheses are tested using U.S. data on the velocity of money and the Michigan PSID.

In this note we show that the conditions that guarantee uniqueness and optimality of Walrasian equilibrium under uncertainty are not sufficient to guarantee these results for non-noisy rational expectations equilibrium under asymmetric information.

For regression models alternative asymptotically equivalent misspecification tests may lead to conflicting inference in small samples. Effective misspecification tests should have correct significance levels irrespective of the true parameters and any redundant regressors in the model, and reasonable power against a wide class of alternative specifications. A simulation study of various tests for serial correlation and predictive failure in models with lagged dependent variables finds many tests defective in small samples. Only particular degrees of freedom adjustments to the test statistics yield improved small sample behaviour.