Research Database

This paper provides an algorithm for computing Markov Perfect Nash Equilibria (Maskin and Tirole, 1988a and b) for dynamic models that allow for heterogeneity among firms and idiosyncratic (or firm specific) sources of uncertainty. It has two purposes. To illustrate the ability of such models to reproduce important aspects of reality, and to provide a tool which, given appropriate parameter estimates, can be used for both descriptive and policy analysis in a setting which allows firms to differ from one another in ways that are consistent with the information in firm level data sets.

We illustrate by computing the policy functions, and simulating the industry structures, generated by a class of dynamic differentiated product models in which the idiosyncratic uncertainty is due to both the random outcomes of each firm's research process, and to an autonomous aggregate demand process. The illustration focuses on comparing the effect of different regulatory and behavioral assumptions on market structure and on welfare for one particular set of parameter values. The results here are of some independent interest and can be read without delving into the technical detail of the computational algorithm.

The last part of the paper begins with an explicit consideration of the computational burden of the algorithm, and then introduces approximation techniques designed to make computation easier. The purpose of this section is to enable us to compute equilibria for industries in which a large number of firms are typically active. Its major result is analytic. We show that if the value function of a given firm is exchangeable in the state vectors of its competitors, then the number of polynomial coefficients one needs for a given order of a polynomial approximation to that function is both independent of the number of firms active in the market, and a relatively small number. This enables us to use the approximation technique to reduce the computational burden of the algorithm dramatically.

We study the aggregate implications of (S,s) inventory policies in a dynamic general equilibrium model. Firms in the model's retail sector face idiosyncratic demand risk, and (S,s) inventory policies are optimal because of fixed order costs. The model economy replicates salient features of the business cycle and reconciles evidence that orders are more volatile than sales, and that inventory investment is positively correlated with sales. There are two main results. First, we find that general equilibrium effects and the optimal order size are important for the economy's response to exogenous shocks. Second, we find that key features of our results are independent of the presence of idiosyncratic risk.

The paper examines the literature that attempts to resolve the equity premium and riskfree rate puzzles. It demonstrates that the puzzles will confront any model of asset prices that relies on three crucial assumptions: preferences have a particular parametric form, asset markets are complete, and asset trade is frictionless. A survey of the literature that relaxes these assumptions reveals that there are now several plausible explanations of the seemingly low riskfree rate, but the large size of the equity premium remains a puzzle.

For the first-order univariate autoregression without constant term, the joint p.d.f (corresponding to a flat prior) for the true coeffecient p and the least squares estimate p-hat is estimated by Monte Carlo and graphically displayed. The graphs show how the symmetric distribution of p|p-hat coexists with the assymetric distribution of p-hat|p. Treating tail areas of the p-hat|p distribution as if they summarized evidence in the data about the location of p amounts to ignoring the shrinkage in the variance of p-hat|p as p approaches one. Prior p.d.f.'s implicit in treating classical significance levels as if they were Bayesian conditional probabilities are calculated. They are shown to depend sensitively on p-hat and to put substantial probability on p's above one.

In welfare theory it is standard to pick the consumption stream that maximizes the welfare of the representative agent. We argue against this position, and show that a benevolent social planner will generally place a greater weight on future consumption than does the representative agent.

In recent years, monopolistic competition models have frequently been applied in macroeconomics, international and interregional economics, and economic growth and development. In this paper, I present a highly selective review in this area, with special emphasis on the complementarity and its role of generating multiplier processes, agglomeration, underdevelopment traps, regional disparities, and sustainable growth, or more generally, what Myrdal (1957) called the “principle of circular and cumulative causation.”

Since the primary role of international financial linkages is to facilitate consumption smoothing in the face of country-specific shocks, the degree of international financial integration should play an important role in the international transmission of business cycles. This paper therefore studies the business cycle implications of restricting international trade in financial assets. The key restriction is that domestic residents must hold all risky claims to domestic output, trading only noncontingent bonds on the international asset markets. We find that restricting asset trade may or may not change the business cycle implications of the model relative to complete markets, depending on the parameterization of the stochastic process for productivity. When there are important differences, these stem largely from differential wealth effects. We also find that restricting asset trade can resolve the chief problem inherent in complete markets models, which is their predictions of too-high consumption correlations and too-low output correlations. When technology follows a random walk process, the restricted asset markets model predicts that cross-country output correlations are positive, and cross-country consumption correlations are smaller than the output correlations, as is typically observed in the data.

This article reports the recent progress made by researchers trying to build business cycle models that can reliably reproduce aggregate U.S. time series. The article first describes some features of the U.S. data that the models are meant to reproduce. Then it describes a version of the standard business cycle model, along with the indivisible labor extension of that model, both of which assume that fluctuations in economic activity are caused only by shocks to technology. Finally, it describes a version of recent other extensions which assume that shocks to fiscal variables also contribute to the fluctuations. Adding fiscal shocks to standard business cycle models is shown to significantly improve their ability to mimic some of the data.

This article reviews recent work comparing properties of international business cycles with those of dynamic general equilibrium models. Two discrepancies between theory and data are described. One concerns the correlation across countries of fluctuations in consumption, output, and productivity: in the data, the output correlation is generally the largest; in theoretical economies, however, for a wide range of parameter values, the consumption correlation is the largest. The other discrepancy concerns relative price movements: the standard deviation of the terms of trade is considerably larger in the data than in theoretical economies. Also described here are several changes in theoretical structure that researchers have attempted, without success, to bring the theory and the data closer together.

In a world where time series show clear seasonal fluctuations, rational agents will take account of those fluctuations in planning their own behavior. Using seasonally adjusted data to model behavior of such agents throws away information and introduces possibly severe bias. Nonetheless it may be true fairly often that rational expectations modeling with seasonally adjusted data, treating the adjusted data as if it were actual data, gives approximately correct results; and naive extensions of standard modeling techniques to seasonally unadjusted data may give worse results than naive use of adjusted data. This paper justifies these claims with examples and detailed arguments.

Capital accumulation and creative destruction is modeled together with risk-averse households. The novel aspect—risk-averse households—allows to use well-known models not only for analyzing long-run growth as in the literature but also short-run fluctuations. The model remains analytically tractable due to a very convenient property of the household’s investment decision in this stochastic continuous-time setup.

This paper identifies a novel form of dynamic inconsistency of stabilization policy in increasing returns models that generate multiple equilibria. We present a two-period version of the Benhabib-Farmer (1994) externalities model and derive closed-form solutions for all endogenous variables in every perfect foresight equilibrium. We provide conditions under which the stabilization policy that maximizes time zero consumer welfare is not time consistent. Furthermore, we characterize the time consistent stabilization policy. Our results cast doubts on the usefulness of government coordination of economic activity when the government lacks a commitment mechanism. Without commitment, a benevolent government can rule out multiplicity only by ensuring that a pareto dominated equilibrium obtains.

Using U.S. data it is shown that as the stock market goes into a period of high volatility, nondurables consumption is unaffected but durables consumption falls substantially. It is argued that a plausible explanation for this is that consumers face irreversibilities when adjusting their durables stock. They will thus apply Ss-type rules with bandwidths with widths that vary over time as in Hassler (1996). To quantify the aggregate implications of such behavior an aggregated irreversible investment model for consumer durables is estimated on U.S. It is found that a shift to higher risk leads to a simultaneous widening of individual Ss-bands which causes demand to fall substantially. This effect diminishes over time but is substantial also after a year.

The current literature offers two views on the nature of the labor income process. According to the first view, which we call the “restricted income profiles” (RIP) model, individuals are subject to large and very persistent shocks while facing similar life-cycle income profiles (MaCurdy, 1982). According to the alternative view, which we call the “heterogeneous income profiles” (HIP) model, individuals are subject to income shocks with modest persistence while facing individual-specific income profiles (Lillard and Weiss, 1979). In this paper we study the restrictions imposed by the RIP and HIP models on consumption data—in the context of a life-cycle model—to distinguish between these two hypotheses. In the life-cycle model with a HIP process, which has not been studied in the previous literature, we assume that individuals enter the labor market with a prior belief about their individual-specific profile and learn over time in a Bayesian fashion. We find that learning is slow, and thus initial uncertainty affects decisions throughout the life cycle. The resulting HIP model is consistent with several features of consumption data including (i) the substantial rise in within-cohort consumption inequality, (ii) the non-concave shape of the age-inequality profile, and (iii) the fact that consumption profiles are steeper for higher educated individuals. The RIP model we consider is also consistent with (i), but not with (ii) and (iii). These results bring new evidence from consumption data on the nature of labor income risk.

The article shows that in a finite-trader version of the Diamond and Dybvig model (1983), the ex ante efficient allocation can be implemented as a unique equilibrium. This is so even in the presence of the sequential service constraint, as emphasized by Wallace (1988), whereby the bank must solve a sequence of maximization problems as depositors contact it at different times. A three-trader example with constant relative risk-aversion utility is used in order to illustrate clearly the requirements that the sequential service constraint imposes on implementation.

This study assesses five common explanations for the large decline in U.S. total factor productivity (TFP) during the Great Depression: changes in capacity utilization, factor input quality, and production composition; labor hoarding; and increasing returns to scale. The study finds that these factors explain less than one-third of the 18 percent TFP decline between 1929 and 1933. The rest of the decline remains unexplained. The study offers a potential explanation: declines in organization capital, the knowledge firms use to organize production, caused by breakdowns in relationships between firms and their suppliers, for example. As some firms failed during the Depression, efficiency in surviving firms decreased; managers had to shift time away from production in order to establish new relationships, and firms had to shift to unfamiliar technologies that initially were operated inefficiently.

We study the existence and robustness of expectationally-driven price volatility in experimental overlapping generation economies. Iin the theoretical model under study there exist “pure sunspot” equilibria which can be “learned” if agents use some adaptive learning rules. Our data show the existence of expectationally-driven cycles, but only after subjects have been exposed to a sequence of real shocks and “learned” a real cycle. In this sense, we show evidence of path-dependent price volatility.