Knowledge that Transforms

We study the effect of income uncertainty on consumption in a model that includes precautionary saving. In contrast to previous studies, we focus on time-series variation in income uncertainty. Our time-series measure of income uncertainty is constructed from a panel of forecasts. We find evidence of precautionary saving in that increases in income uncertainty are related to increases in aggregate rates of saving. We also find evidence that anticipated income growth rates have less explanatory power for consumption growth rates after conditioning on income uncertainty. The evidence indicates the presence of forward-looking consumers who gradually adjust precautionary savings in response to changing income uncertainty.

This paper examines the responses of prices at different stages of production to monetary policy shocks. In aggregate price analysis, the VAR of Christiano et al. (1996a, 1996b) is used to identify the policy shock as the federal funds rate innovation and trace out the responses of prices. In disaggregate price analysis, the adjustment of prices is examined by comparing inflation before and after a recent policy tightening identified by Romer and Romer (1989, 1992). At early stages of production, a monetary tightening causes input prices to fall more rapidly and by a larger amount than output prices.

This paper offers a framework for forecasting aggregate sales of new motor vehicles; this framework incorporates separate models for the change in the vehicle stock and for the rate of vehicle scrappage. Because this approach requires only a minimal set of assumptions about demographic trends, the state of the economy, consumer “preferences,” new vehicle prices and repair costs, and vehicle retirements, it is shown to be especially useful as a macroeconomic forecasting tool. In addition, this paper presents a new historical annual time-series estimate of motor vehicle stocks in the United States.

Present value studies of asset market efficiency are controversial because they compare asset prices to unobserved discounted streams of future rents. As an alternative, if housing markets are efficient, then the price of residential capital or buildings should satisfy the following two conditions: (i) deviations between new building prices and construction costs should disappear faster than construction lags and have no effect on construction, and (ii) temporary building price shocks should dissipate at a similar rate for different vintage buildings. Results from an error-correction model support both hypotheses for single-family housing in Vancouver, British Columbia. This implies that the implicit market for residential buildings is efficient and that any inefficiencies in the housing market must lie in the market for land itself.

It is shown how a comparison of price levels across a group of countries can be made by chaining bilateral price indexes across a spanning tree. It is argued that we should use the spanning tree whose resulting multilateral price indexes are least sensitive to the choice of bilateral formula. This minimum-spanning tree can be easily computed using Kruskal's algorithm. Results obtained by chaining Fisher indexes across a minimum-spanning tree are compared with the Penn World Table.

A Monte Carlo analysis of the coverage accuracy and average length of alternative bootstrap confidence intervals for impulse-response estimators shows that the accuracy of equal-tailed and symmetric percentile-t intervals can be poor and erratic in small samples (both in models with large roots and in models without roots near the unit circle). In contrast, some percentile bootstrap intervals may be both shorter and more accurate. The accuracy of percentile-t intervals improves with sample size, but the sample size required for reliable inference can be very large. Moreover, for such large sample sizes, virtually all bootstrap intervals tend to have excellent coverage accuracy.

In this paper we analyze the estimation of coefficients in regression models under moment restrictions in which the moment restrictions are derived from auxiliary data. The moment restrictions yield weights for each observation that can subsequently be used in weighted regression analysis. We discuss the interpretation of these weights under two assumptions: that the target population (from which the moments are constructed) and the sampled population (from which the sample is drawn) are the same, and that these populations differ. We present an application based on omitted ability bias in estimation of wage regressions. The National Longitudinal Survey Young Men's Cohort (NLS)—in addition to containing information for each observation on wages, education, and experience—records data on two test scores that may be considered proxies for ability. The NLS is a small dataset, however, with a high attrition rate. We investigate how to mitigate these problems in the NLS by forming moments from the joint distribution of education, experience, and log wages in the 1% sample of the 1980 U.S. Census and using these moments to construct weights for weighted regression analysis of the NLS. We analyze the impacts of our weighted regression techniques on the estimated coefficients and standard errors of returns to education and experience in the NLS controlling for ability, with and without the assumption that the NLS and the Census samples are random samples from the same population.

Random utility models (RUMs) are used in the literature to model consumer choices from among a discrete set of alternatives, and they typically impose a constant marginal utility of income on individual preferences. This assumption is driven partially by the difficulty of constructing welfare estimates in models with nonlinear income effects. Recently, McFadden (1995) developed an algorithm for computing these welfare impacts using a Monte Carlo Markov chain simulator for generalized extreme-value variates. This paper investigates the empirical consequences of nonlinear RUMs in the case of sportfishing modal choice, while refining and contrasting the available methods for welfare estimation.

Using data that permit a distinction between flows of workers (directly measured) and job creation and destruction (again, directly measured), we develop employment and job flow statistics for a representative sample of French establishments from 1987 to 1990. Annual job creation can be characterized as hiring three persons and separating two for each job created in a given year. Annual job destruction can be characterized as hiring one person and separating two for each job destroyed in a given year. When an establishment is changing employment, the adjustment is made primarily by reducing entry and not by changing the separation rates. There is considerable simultaneous hiring and separation, even controlling for skill group. Two-thirds of all hiring is on short-term contracts, and more than half of all separations are due to the end of these short-term contracts.

A simple construction that will be referred to as an error-duration model is shown to generate fractional integration and long memory. An error-duration representation also exists for many familiar ARMA models, making error duration an alternative to autoregression for explaining dynamic persistence in economic variables. The results lead to a straightforward procedure for simulating fractional integration and establish a connection between fractional integration and common notions of structural change. Two examples show how the error-duration model could account for fractional integration in aggregate employment and in asset price volatility.