Tailor-made tests for goodness of fit to semiparametric hypotheses

We introduce a new framework for constructing tests of general semiparametric hypotheses which have nontrivial power on the $n^{-1/2}$ scale in every direction, and can be tailored to put substantial power on alternatives of importance. The approach is based on combining test statistics based on stochastic processes of score statistics with bootstrap critical values.Comment: Published at http://dx.doi.org/10.1214/009053606000000137 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Household gasoline demand in the United States

Continuing rapid growth in U.S. gasoline consumption threatens to exacerbate environmental and congestion problems. We use flexible semiparametric and nonparametric methods to guide analysis of household gasoline consumption, and including this variable cuts the estimated income elasticity in half. Slower projected future growth in licensed drivers points to slower growth in gasoline consumption. A parsimonious representation of age, income, lifecycle and location effects is developed and tested. We show how flexible methods also helped reveal fundamental problems with the available price data.Supported by the MIT Center for Energy and Environmental Policy Research, the U.S. Dept. of Energy and the National Science Foundation

Censored Regressors and Expansion Bias

We show how using censored regressors leads to expansion bias, or estimated effects that are proportionally too large. We show the necessity of this effect in bivariate regression and illustrate the bias using results for normal regressors. We study the bias when there is a censored regressor among many regressors, and we note how censoring can work to undo errors-in-variables bias. We discuss several approaches to correcting expansion bias. We illustrate the concepts by considering how censored regressors can arise in the analysis of wealth effects on consumption, and on peer effects in productivity

A regression test of semiparametric index model specification

This paper presents a simple regression test of parametric and semiparametric index models against more general semiparametric and nonparametric alternative models. The test is based on the regression coefficient of the restricted model residuals on the fitted values of the more general model. A goodness-of-fit interpretation is given to the regression coefficient, where the variance of the coefficient is adjusted for the use of nonparametric estimators. An asymptotic theory is developed for the situation where kernel estimators are used to estimate unknown regression functions, and the variance adjustment terms are given for this case. The methods are applied to the empirical problem of characterizing environmental effects on housing prices in the Boston Housing data, where a partial index model is found to be preferable to a standard log-linear equation, yet not rejected against general nonparametric regression. Various issues in the asymptotic theory and other features of the test are discussed.Funded by a grant from the MIT Center for Energy and Environmental Policy Research

Semiparametric measurement of environmental effects

This paper gives the results of a semiparametric analysis of pollution effects on housing prices using the Boston Housing Data. The exposition introduces the basic ideas of modeling pollution impacts with hedonic price methods, discusses the standard log-linear model, and then introduces nonparametric estimation and semiparametric index models. We focus on the intuitive content and substantive results of the semiparametric analysis. We find that the impact of pollution is smaller than that previously estimated, and varies dramatically depending on the status level of the community. We give various interpretations of the findings, and contrast our methods with those used in previous analysis of the Boston Housing Data.Supported by the MIT Center for Energy and Environmental Policy Research

Sources of productivity growth in the American coal industry

This paper develops new techniques to assess the expanse of the geographic market under varying supply and demand conditions and applies these techniques to the current wholesale electricity market in the western United States. This paper finds that, by and large, the expanse of the geographic market extends across most of the western United States, but that conditions which create congestion along transmission lines, such as high hydroelectric flows in the Pacific Northwest, transmission line outages and deratings, and high demand for wholesale electricity, cause the expanse of the geographic market to narrow at certain times.Supported by the MIT Center for Energy and Environmental Policy Research

World energy consumption and carbon dioxide emissions : 1950-2050

Emissions of carbon dioxide form 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 1950-1990. We employ a flexible form for income effects, along with fixed time and country effects, and we handle forecast uncertainty explicitly. We find an "inverse-U" relation with a within-sample peak between carbon dioxide emissions (and energy use) per capita and per capita income. Using the income and population growth assumptions of the Intergovernmental Panel on Climate Change (IPCC), we obtain projections significantly and substantially above those of the IPCC

Panel data analysis of U.S. coal productivity

We analyze labor productivity in coal mining in the United States using indices of productivity change associated with the concepts of panel data modeling. This approach is valuable when there is extensive heterogeneity in production units, as with coal mines. We find substantial returns to scale for coal mining in all geographical regions, and find that smooth technical progress is exhibited by estimates of the fixed effects for coal mining. We carry out a variety of diagnostic analyses of our basic model and primary modeling assumptions, using recently proposed methods for addressing 'errors-in-variable' and 'weak instrument bias' problems, as well a new method for studying errors-in-variables in nonlinear contexts.Supported by the MIT Center for Energy and Environmental Policy Research

Economic development and the structure of the demand for commerial energy

To deepen the understanding of the relation between economic development and energy demand, this study estimates the Engel curves that relate per-capita energy consumption in major economic sectors to per-capita GDP. Panel data covering up to 123 nations are employed, and measurement problems are treated both in dataset construction and in estimation. Time and country fixed effects are assumed, and flexible forms for income effect are employed. There are substantial differences among sectors in the structure of country, time, and income effects. In particular, the household sector's share of aggregate energy consumption tends to fall with income, the share of transportation tends to rise, and the share of industry follows an inverse-U pattern.Financial assistance provided by the MIT Center for Energy and Environmental Policy Research