Household Gasoline Demand in the United States

By R. Schmalensee, Thomas M. Stoker. 1999

Supported by the MIT Center for Energy and Environmental Policy Research, the U.S. Dept. of Energy and the National Science Foundation.

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “This parameter is critical for assessing the welfare consequences of tax changes, and it has been studied in Hausman and Newey (1995); Schmalensee and Stoker (1999); Yatchew and No (2001); Blundell et al. (2012).”
Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions (2017, methodology): “We apply our methods to study the household demand for gasoline, a question studied in Haus-man and Newey (1995), Schmalensee and Stoker (1999), Yatchew and No (2001) and Blundell et al. (2012).”
Conditional quantile processes based on series or many regressors (2011, background): “[21], [34], and [39] estimated nonparametrically the average demand function.”
Instrumental regression in partially linear models (2012, unknown): “The above mentioned study of Schmalensee and Stoker (1999) uses that strategy, assuming that the estimator of β in the first step has”
Partially Linear Models (2000, background): “From Schmalensee and Stoker (1999), with the permission from the Journal of Econometrica.”
Massachusetts Institute of Technology Department of Economics Working Paper Series Conditional Quantile Processes Based on Series or Many Regressors Conditional Quantile Processes Based on Series or Many Regressors (unknown): “[21], [34], and [39] estimated nonparametrically the average demand function.”
Estimation in partially linear models with missing responses at random (2007, background): “Schmalensee and Stoker [16] used the partially linear model to analyze household gasoline consumption in the United States.”
Statistical estimation in partial linear models with covariate data missing at random (2009, background): “Schmalensee and Stoker (1999 ) u sed the partiallylinearmodeltoanalyzehouseholdgasolineconsumptionintheUnitedStates.”
THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL (2014, methodology): “Schmalensee and Stoker (1999) question the reliability of the price data and the specification used in Hausman and Newey (1995), but Yatchew and No (2001) address those concerns using more detailed Canadian data and arrive at a gasoline demand elasticity estimate of -0.9, very close to Hausman and ”
Estimating the effect of a gasoline tax on carbon emissions (2011, unknown): “Whereas the RTECS is valuable for cross-sectional studies such as Schmalensee and Stoker (1999) , it is not appropriate in our context because the survey is carried out only once every 3 years.”
Journal of the American Statistical Association Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models (unknown): “Interesting applications include the analysis of city electricity (Engle et al. 1986), household gasoline consumption in the United States (Schmalensee and Stoker 1999), a marketing price-volume study in the petroleum distribution industry (Green and Silverman 1994), the logistic analysis of…”
Automobile Fuel Demand: A Critical Assessment of Empirical Methodologies (2007, background): “17 The finding of Schmalensee and Stoker (1999) and Yatchew and No (2001) regarding the influence of the number of licensed drivers on consumption and the estimated income elasticities is also of major importance.”
$2.00 Gas! Studying the Effects of a Gas Tax Moratorium (2008, unknown): “Demand conditions can vary with demographics such as income levels and commuting patterns (Schmalensee and Stoker, 1999).”