Trucks without Bailouts: Equilibrium Product Characteristics for Commercial Vehicles

Wollmann, Thomas

doi:10.1257/aer.20160863

Cited by 104 publications

(57 citation statements)

References 30 publications

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“…We generate data using d X = 3, (θ 1 θ 2 β 1 β 2 β 3 ) = (0 3 0 5 0 05 0 0), and δ = 0 6, where δ is the probability of selecting A i = (1 0) in the region of multiple equilibria. The identified set for each coordinate of (θ 1 θ 2 β 1 β 2 β 3 ) is given by Having a five dimensional parameter θ already presents challenges for projection-based tests and represents a case of empirical relevance (e.g., see Dickstein and Morales (2015) and Wollmann (2015)). For example, a grid with 100 points in the (0 1) interval for each element in θ (imposing the restrictions in Θ for (β 1 β 2 β 3 )) involves 1,025 million evaluations of test statistics and critical values.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Bugni

Canay²,

Shi

2017

Quantitative Economics

125

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This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided testing problems. In the most common case, this function selects a subvector of the parameter, such as a single component. The new inference method we propose controls asymptotic size uniformly over a large class of data distributions and improves upon the two existing methods that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.

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Section: Monte Carlo Simulationsmentioning

confidence: 99%

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Bugni

Canay²,

Shi

2017

Quantitative Economics

125

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“…Notably, simulations of both a proposed and ultimately blocked merger and in hypothetical mergers between local competitors, firms reduce news quality, generating consumer surplus losses beyond those due to higher prices alone. The set-up in Wollmann [2018] is more similar to ours -market participants in the truck manufacturing industry can choose from a menu of discrete options that vary in a smaller number of horizontal characteristics (e.g., size, cab design). Data on the products offered by various market participants allows inferences of the sunk costs of offering a particular product from each firm's menu.…”

Section: Introductionmentioning

confidence: 99%

“…Data on the products offered by various market participants allows inferences of the sunk costs of offering a particular product from each firm's menu. Wollmann [2018] then uses these sunk cost estimates to simulate new market structure patterns after a change due, for example, to a merger. 4 We begin by outlining our modeling approach, which generalizes the work of Draganska et al [2009].…”

Section: Introductionmentioning

confidence: 99%

The Welfare Consequences of Mergers with Endogenous Product Choice

Mazzeo

Seim

Varela

2018

The J Industrial Economics

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Merger simulations focus on the price changes that result once previously independent competitors set prices jointly and other market participants respond. We consider the incentives for firms to adjust the set of offered products after a merger. Using a model of product choice and pricing, we conduct simulations of equilibrium market outcomes of a merger in a variety of scenarios. Product offering adjustments result in additional effects on profitability and consumer welfare not realized by price responses only, particularly when the merging parties offer relatively similar products pre‐merger. Cost synergies may furthermore entail the pro‐competitive introduction of additional products.

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“…In 2010, fleet buyers accounted for 19 per cent of 7 The only other paper analyzing the responsiveness of vehicle purchases made by organizations is by Li et al [2015], who find no effect from diesel price changes on the U.S. bus fuel economy. Most other works on heavy-duty trucks have instead focused on fuel use and travel intensity (Leard et al [2015]; Cohen and Roth [2017]) or vehicle attributes other than fuel cost savings (e.g., Rust [1987] and Wollmann [2018]). Moreover, it is unclear whether inferences for heavy-duty truck demand are applicable to passenger vehicle fleet demand.…”

Section: Ii(i) Background Of Fleet Vehicle Demandmentioning

confidence: 99%

The Effect of Fuel Price Changes on Fleet Demand for New Vehicle Fuel Economy

Leard

McConnell²,

Zhou

2019

The J Industrial Economics

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New vehicle purchases by private companies and government agencies, or ‘fleet’ buyers, represent a significant percentage of overall new vehicle sales in the United States. Yet little is known about fleet demand for new vehicle fuel economy including how it responds to fuel price changes. Using unique disaggregated data on fleet and household registrations of new vehicles from 2009 to 2016, we estimate how fleet demand for new vehicle fuel economy responds to fuel price changes. We find that fleet purchases of low fuel economy vehicles fall relative to high fuel economy vehicles when gasoline prices increase, a finding that is consistent with fleet buyers’ taking into account capitalization of fuel costs in the second‐hand market. Our estimates imply that raising gasoline prices by one dollar would increase fuel economy of new vehicles acquired by fleet buyers by 0.33 miles per gallon. We estimate a similar response for household buyers during the same period. This result justifies basing fuel economy responses to fuel cost changes on household data alone, an assumption widely used in the vehicle demand literature and the fuel economy valuation literature. We also find, however, that the response to fuel price changes varies across the types of fleet buyers: rental companies respond strongly to fuel price changes, whereas commercial and government buyers are insensitive. Our estimates imply that an increase in the federal gasoline tax would modestly increase fuel economy of vehicles bought by households and rental companies but would have little to no impact on fuel economy of vehicles bought by non‐rental companies and governments.

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Trucks without Bailouts: Equilibrium Product Characteristics for Commercial Vehicles

Cited by 104 publications

References 30 publications

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Inference for subvectors and other functions of partially identified parameters in moment inequality models

The Welfare Consequences of Mergers with Endogenous Product Choice

The Effect of Fuel Price Changes on Fleet Demand for New Vehicle Fuel Economy

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