Research Database

We report estimates of the dynamic effects of a technology shock, and then use these to estimate the parameters of a dynamic general equilibrium model with money. We find: (i) a positive technology shock drives up hours worked, consumption, investment and output; (ii) the positive response of hours worked reflects that the Fed has in practice accommodated technology shocks; (iii) model parameter values and functional forms that match the response of macroeconomic variables to monetary policy shocks also work well for technology shocks; (iv) while technology shocks account for a large fraction of the lower frequency component of economic fluctuations, they account for only a small part of the business cycle component of fluctuations.

This paper proposes a simple method for guiding researchers in developing quantitative models of economic fluctuations. We show that a large class of models, including models with various frictions, are equivalent to a prototype growth model with time varying wedges that, at least on face value, look like time-varying productivity, labor taxes, and capital income taxes. We label the time varying wedges as efficiency wedges, labor wedges, and investment wedges. We use data to measure these wedges and then feed them back into the prototype growth model. We then assess the fraction of fluctuations accounted for by these wedges during the great depressions of the 1930s in the United States, Germany, and Canada. We find that the efficiency and labor wedges in combination account for essentially all of the declines and subsequent recoveries. Investment wedge plays at best a minor role.

This paper examines data on point and probabilistic forecasts of inflation from the Survey of Professional Forecasters. We use this data to evaluate current strategies for the empirical modeling of forecast behavior. In particular, the analysis principally focuses on the relationship between ex post forecast errors and ex ante measures of uncertainty in order to assess the reliability of using proxies based on predictive accuracy to describe changes in predictive confidence. After we adjust the data to account for certain features in the conduct and construct of the survey, we find a significant and robust correlation between observed heteroskedasticity in the consensus forecast errors and forecast uncertainty. We also document that significant compositional effects are present in the data that are economically important in the case of forecast uncertainty, and may be related to differences in respondents' access to information.

This paper is the first of a series of explorations in the relative performance and sources of productivity growth of U.S. businesses across industries and legal structure. In order to assemble the disparate data from various sources to develop a coherent productivity database, we developed a general system to manage data. The paper describes this system and then applies it by building such a database. The paper presents updated estimates of gross output, intermediate input use and value added using the BEA=s GPO data set. It supplements these data with estimates of missing data on intermediate input use and prices for the 1977-1986 period, and it concords these data, which are organized on a 1972 SIC basis, to the 1987 SIC in order to have consistent time series covering the last twenty-four years. It further refines these data by disaggregating them by legal form of organization. The paper also presents estimates of labor hours, investment, capital services and, consequently, multifactor productivity disaggregated by industry and legal form of organization, and it analyzes the contribution of various industries and business organizations to aggregate productivity. The paper also reconsiders these estimates in light of the surge in spending in advance of the century-date change.

The durable goods sector is much more interest sensitive than the non-durables sector, and these sectoral differences have important implications for monetary policy. In this paper, we perform VAR analysis of quarterly US data and find that a monetary policy innovation has a peak impact on durable expenditures that is roughly five times as large as its impact on non-durable expenditures. We then proceed to formulate and calibrate a two-sector dynamic general equilibrium model that roughly matches the impulse response functions of the data. We derive the social welfare function and show that the optimal monetary policy rule responds to sector-specific inflation rates and output gaps. We show that some commonlyprescribed policy rules perform poorly in terms of social welfare, especially rules that put a higher weight on inflation stabilization than on output gap stabilization. By contrast, it is interesting that certain rules that react only to aggregate variables, including aggregate output gap targeting and rules that respond to a weighted average of price and wage inflation, may yield a welfare level close to the optimum given a typical distribution of shocks.

We add a nominal tax system to a sticky-price monetary business cycle model. When nominal interest income is taxed, the coefficient on inflation in a Taylor-type monetary policy rule must be significantly larger than one in order for the model economy to have a determinate rational expectations equilibrium. When depreciation is treated as a charge against taxable income, an even larger weight on inflation is required in the Taylor rule in order to obtain a determinate and stable equilibrium. These results have obvious implications for assessing the historical conduct of monetary policy.

This paper presents a method to perform likelihood-based inference in nonlinear dynamic equilibrium economies. This type of models has become a standard tool in quantitative economics. However, existing literature has been forced so far to use moment procedures or linearization techniques to estimate these models. This situation is unsatisfactory: moment procedures suffer from strong small samples biases and linearization depends crucially on the shape of the true policy functions, possibly leading to erroneous answers. We propose the use of Sequential Monte Carlo methods to evaluate the likelihood function implied by the model. Then we can perform likelihood-based inference, either searching for a maximum (Quasi-Maximum Likelihood Estimation) or simulating the posterior using a Markov Chain Monte Carlo algorithm (Bayesian Estimation). We can also compare different models even if they are nonnested and misspecified. To perform classical model selection, we follow Vuong (1989) and use the Kullback-Leibler distance to build Likelihood Ratio Tests. To perform Bayesian model comparison, we build Bayes factors. As an application, we estimate the stochastic neoclassical growth model.

Trend-cycle decomposition has been problematic in equilibrium business cycle research. Many models are fundamentally based on the concept of balanced growth, and so have clear predictions concerning the nature of the multivariate trend that should exist in the data if the model is correct. But the multivariate trend that is removed from the data in this literature is not the same one that is predicted by the model. This is understandable, because unexpected changes in trends are difficult to model under a rational expectations assumption. A learning assumption is more appropriate here. We include learning in a standard equilibrium business cycle model with explicit growth. We ask how the economy might react to the important trend-changing events of the postwar era in industrialized economies, such as the productivity slowdown, increased labor force participation by women, and the "new economy" of the 1990s. This tells us what the model says about the trend that should be taken out of the data before the business cycle analysis begins. Thus we use learning to address the trend-cycle decomposition problem that plagues equilibrium business cycle research. We argue that a model-consistent approach, such as the one we suggest here, is necessary if the goal is to obtain an accurate assessment of an equilibrium business cycle model.

Previous research has suggested that discrete and occasional plant-level capital adjustments have significant aggregate implications. In particular, it has been argued that changes in plants’ willingness to invest in response to aggregate shocks can at times generate large movements in total investment demand. In this study, I re-assess these predictions in a general equilibrium environment. Specifically, assuming nonconvex costs of capital adjustment, I derive generalized (S,s) adjustment rules yielding lumpy plant-level investment within an otherwise standard equilibrium business cycle model. In contrast to previous partial equilibrium analyses, model results reveal that the aggregate effects of lumpy investment are negligible. In general equilibrium, households’ preference for relatively smooth consumption profiles offsets changes in aggregate investment demand implied by the introduction of lumpy plant-level investment. As a result, adjustments in wages and interest rates yield quantity dynamics that are virtually indistinguishable from the standard model.

Recent empirical analysis has found nonlinearities to be important in understanding aggregated investment. Using an equilibrium business cycle model, we search for aggregate nonlinearities arising from the introduction of nonconvex capital adjustment costs. We find that, while such costs lead to nontrivial nonlinearities in aggregate investment demand, equilibrium investment is effectively unchanged. Our finding, based on a model in which aggregate fluctuations arise through exogenous changes in total factor productivity, is robust to the introduction of shocks to the relative price of investment goods.

What is the force of attraction of cities? Leading explanations include the advantages of a concentrated market and knowledge spillovers. This paper develops a model of firm location decisions in which it is possible to distinguish the importance of the concentrated-market motive from other motives, including knowledge spillovers. A key aspect of the model is that it allows for the firm to choose multiple locations. The theory is applied to study the placement of manufacturing sales offices. The implications of the concentrated-market motive are found to be a salient feature of U.S. Census micro data. The structural parameters of the model are estimated. The concentrated-market motive is found to account for approximately half of the concentration of sales offices in large cities.

The central puzzle in international business cycles is that fluctuations in real exchange rates are volatile and persistent. We quantity the popular story for real exchange rate fluctuations: they are generated by monetary shocks interacting with sticky goods prices. If prices are held fixed for at least one year, risk aversion is high, and preferences are separable in leisure, then real exchange rates generated by the model are as volatile as in the data and quite persistent, but less so than in the data. The main discrepancy between the model and the data, the consumption—real exchange rate anomaly, is that the model generates a high correlation between real exchange rates and the ratio of consumption across countries, while the data show no clear pattern between these variables.

We construct a competitive model of innovation and growth under constant returns to scale. Previous models of growth under constant returns cannot model technological innovation. Current models of endogenous innovation rely on the interplay between increasing returns and monopolistic markets. In fact, established wisdom claims monopoly power to be instrumental for innovation and sees the nonrivalrous nature of ideas as a natural conduit to increasing returns. The results here challenge the positive description of previous models and the normative conclusion that monopoly through copyright and patent is socially beneficial.

We develop a parsimonious model of innovating firms rich enough to confront firm-level evidence. It captures the dynamic behavior of individual heterogenous firms, describes the evolution of an industry with simultaneous entry and exit, and delivers a general equilibrium model of technological change. While unifying the theoretical analysis of firms, industries, and the aggregate economy, the model yields insights into empirical work on innovating firms. It accounts for the persistence over time of firms’ R&D investment, the concentration of R&D among incumbent firms, and the link between R&D and patenting. Furthermore, it explains why R&D as a fraction of revenues is strongly related to firm productivity yet largely unrelated to firm size or growth.