Knowledge that Transforms

Emissions of carbon dioxide from the combustion of fossil fuels, which may contribute to long-term climate change, are projected through 2050 using reduced-form models estimated with national-level panel data for the period of 1950–1990. Using the same set of income and population growth assumptions as the Intergovernmental Panel on Climate Change (IPCC), we find that the IPCC's widely used emissions growth projections exhibit significant and substantial departures from the implications of historical experience. Our model employs a flexible form for income effects, along with fixed time and country effects, and we handle forecast uncertainty explicitly. We find clear evidence of an “inverse U” relation with a within-sample peak between carbon dioxide emissions (and energy use) per capita and per-capita income.

Traditional concerns about concentration in product markets have centered on the social loss associated with the mispricing that occurs when market power is exercised. This paper focuses on a potentially greater loss from market power—a reduction in cost efficiency brought about by the lack of market discipline in concentrated markets. We employ data from the commercial banking industry, which produces very homogeneous products in multiple markets with differing degrees of market concentration. We find the estimated efficiency cost of concentration to be several times larger than the social loss from mispricing as traditionally measured by the welfare triangle.

Bias-corrected bootstrap confidence intervals explicitly account for the bias and skewness of the small-sample distribution of the impulse response estimator, while retaining asymptotic validity in stationary autoregressions. Monte Carlo simulations for a wide range of bivariate models show that in small samples bias-corrected bootstrap intervals tend to be more accurate than delta method intervals, standard bootstrap intervals, and Monte Carlo integration intervals. This conclusion holds for VAR models estimated in levels, as deviations from a linear time trend, and in first differences. It also holds for random walk processes and cointegrated processes estimated in levels. An empirical example shows that bias-corrected bootstrap intervals may imply economic interpretations of the data that are substantively different from standard methods.

This paper examines the out-of-sample performance of various financial variables as predictors of U.S. recessions. Series such as interest rates and spreads, stock prices, and monetary aggregates are evaluated individually and in comparison with other financial and nonfinancial indicators. The analysis focuses on out-of-sample performance from one to eight quarters ahead. Results show that stock prices are useful with one- to three-quarter horizons, as are some well-known macroeconomic indicators. Beyond one quarter, however, the slope of the yield curve emerges as the clear individual choice and typically performs better by itself out of sample than in conjunction with other variables.

Mixed logit models, also called random-parameters or error-components logit, are a generalization of standard logit that do not exhibit the restrictive “independence from irrelevant alternatives” property and explicitly account for correlations in unobserved utility over repeated choices by each customer. Mixed logits are estimated for households' choices of appliances under utility-sponsored programs that offer rebates or loans on high-efficiency appliances.

Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or “spatial” dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.