We study how decision-makers' concerns about robustness affect prices and quantities in a stochastic growth model. In the model economy, growth rates in technology are altered by infrequent large shocks and continuous small shocks. An investor observes movements in the technology level but cannot perfectly distinguish their sources. Instead the investor solves a signal extraction problem. We depart from most of the macro-economics and finance literature by presuming that the investor treats the specification of technology evolution as an approximation. To promote a decision rule that is robust to model misspecification, an investor acts as if a malevolent player threatens to perturb the actual data-generating process relative to his approximating model. We study how a concern about robustness alters asset prices. We show that the dynamic evolution of the risk-return trade-off is dominated by movements in the growth-state probabilities and that the evolution of the dividend-price ratio is driven primarily by the capital-technology ratio.

This sequel to Lucas and Sargent (1978) tells how equilibrium Markov processes underlie much applied dynamic economics today. It recalls how Robert E. Lucas Jr. saw Keynesian and rational expectations revolutions as interconnected transformations of economic theories and econometric practices. It describes rules that Lucas used to guide and constrain his research by restricting himself to equilibrium Markov processes and to conserving quantitative successes achieved by previous researchers, including those attained by quantitative Keynesian macroeconometric modelers.

Under the Articles of Confederation, the central government of the United States had limited power to tax. Therefore, large debts accumulated during the US War for Independence traded at deep discounts. That situation framed a US fiscal crisis in the 1780s. A political revolution—for that was what scuttling the Articles of Confederation in favor of the Constitution of the United States of America was—solved the fiscal crisis by transferring authority to levy tariffs from the states to the federal government. The Constitution and acts of the First Congress of the United States in August 1790 gave Congress authority to raise enough revenues to service a big government debt. In 1790, the Congress carried out a comprehensive bailout of state governments’ debts, part of a grand bargain that made creditors of the states become advocates of ample federal taxes. That bailout created expectations about future federal bailouts that a costly episode in the early 1840s proved to be unwarranted.

This paper describes two models of an agency that is collecting and reporting observations on a dynamical linear stochastic economy. The first is a "classical" model, with the agency reporting data that are the sum of a vector of "true" variables and a vector of measurement errors that are orthogonal to the true variables. The second is a model of an agency that uses an optimal filtering method to construct least-squares estimates of the true variables. These two models of the reporting agency imply different likelihood functions. A model of the investment accelerator is used as an example to illustrate the differing implications of the models.

This paper explores some of the implications for econometric practice of the principle that people's observed behavior will change when their constraints change. In dynamic contexts, a proper definition of people's constraints includes among them laws of motion that describe the evolution of the taxes they must pay and the prices of the goods that they buy and sell. Changes in agents' perceptions of these laws of motion (or constraints) will in general produce changes in the schedules that describe the choices they make as a function of the information that they possess. Until very recently, received dynamic econometric practice ignored this principle. The practice of dynamic econometrics should be changed so that it is consistent with the principle that people's rules of choice are influenced by their constraints. This is a substantial undertaking and involves major adjustments in the ways that we formulate, estimate, and simulate econometric models.

A dynamic linear demand schedule for labor is estimated and tested. The hypothesis of rational expectations and assumptions about the orders of the Markov processes governing technology impose over-identifying restrictions on a vector autoregression for straight-time employment, overtime employment, and the real wage. The model is estimated by the full information maximum likelihood method. The model is used as a vehicle for re-examining some of the paradoxical cyclical behavior of real wages described in the famous Dunlop-Tarshis-Keynes exchange.

Estimates of a rational expectations version of Friedman's time-series consumption model are obtained by imposing the pertinent restrictions across the stochastic processes for consumption and income. A likelihood ratio test is used to test the adequacy of three joint hypotheses: namely, Friedman's model, rational expectations, and some arbitrary conditions on the disturbance process in the consumption function. The paper treats both the cases in which income is econometrically exogenous with respect to consumption and those in which it is not. The macroeconomics of this exogeneity condition are briefly discussed.