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.