“Assessing the Mechanisms Underlying Property Diminution Damages,” (with Stephanie Biehl), Natural Resources & Environment, ABA Section on Environment, Energy, and Resources, 2024 [published version]
In this article, we discuss the mechanisms underlying property diminution damages following an environmental harm, ranging from one-time remediation costs to ongoing loss of use and enjoyment. We highlight the importance of understanding these mechanisms to ensure that the damages awarded are appropriate and effective in compensating for the harm caused by environmental contamination.
"Just My Type: Modernizing Analytical Approaches for ‘Minor’ Natural Resource Damages," Superfund and Cost Recovery Committee Newsletter, ABA Section on Environment, Energy, and Resources, 2023 [published version]
Assessing natural resource losses with accuracy is inherently difficult and site-specific, leading the US Department of the Interior to request comments on reliable, but simplified methods for calculating interim lost use values. I discuss the advantages and limitations of two methods (equivalency analysis and benefit transfer) in this article.
“Understanding the Econometric Tools of Antitrust—With No Math!” (with Michael Cragg and Loren Smith), ANTITRUST Magazine, 2021, 35(2) [paper | published version]
In this article we group econometric techniques used by antitrust economists into three broad categories that generally follow a continuum of their specific connection to economic theory: summary statistics, regression analysis, and structural models. By understanding the benefits and limitations of these categories of econometric analyses, a practitioner is well on the way to comprehending the possibilities of antitrust econometrics--and more useful and enjoyable meetings with economists.
Winner of the Concurrences Antitrust Writing Award, Economics Category
"Broken or Fixed Effects?" (with Juan Carlos Suárez Serrato and Mike Urbancic), Journal of Econometric Methods, 2018 [pre-print | published version | data and code | R package]
We replicate eight influential papers to provide empirical evidence that, in the presence of heterogeneous treatment effects, OLS with fixed effects (FE) is generally not a consistent estimator of the average treatment effect (ATE). We propose two alternative estimators that recover the ATE in the presence of group-specific heterogeneity. We document that heterogeneous treatment effects are common and the ATE is often statistically and economically different from the FE estimate. In all but one of our replications, there is statistically significant treatment effect heterogeneity and, in six, the ATEs are either economically or statistically different from the FE estimates.
"Natural Resource Damages from Fishing Site Impairments: Evidence from a Repeat Cross Section of New York Anglers" (with David Sunding), Land Economics, accepted for publication [working paper]
Survey data eliciting fishing trips taken by New York State anglers are used to estimate their willingness-to-pay to avoid fish consumption advisories. A random sample of all waterbody-county pairs serve as potential destinations for each angler, reducing attenuation bias in the impact of travel costs on site choice. Repeated cross section data enable site-specific constants to control for unobservable factors that influence site choice. Estimates of the WTP are provided directly by our estimation procedure, yielding more reliable estimates in the case of mixed logit. Flat logit models produce values between $2.91 and $5.96, while mixed logit model give average WTP values of $1.16 to $1.72.
"Quantile Regression for Peak Demand Forecasting" (with Ahmad Faruqui) [working paper]
We demonstrate that annual peak demand days are characterized by both extreme values of predictors (such as weather) and large unpredictable "shocks" to demand. OLS approaches incorporate the former feature, but not the latter, leading OLS to produce downwardly-biased estimates of the annual peak. We develop a new estimation procedure, optimal forecast quantile regression (OFQR), that uses quantile regression to estimate a model of daily peak demand, then uses a loss function framework to estimate a quantile to predict the annual peak. We compare the results of the OLS and OFQR estimation approaches for 32 utility zones. While the OFQR approach is unbiased, OLS under-forecasts by nearly 5% on average. Further, OFQR reduces the average absolute percent error by 43%. A bootstrapping procedure generates forecast intervals with accurate 95% coverage in sample and 87% coverage out of sample.
D3: Clan of Dan (in honor of Dan McFadden's 80th birthday) [website | Github]
R package: bfe (companion to academic paper "Broken or Fixed Effects?," Journal of Econometric Methods 2018) [Github]
R package: BrattleExtras (utilities for internal use) [Github]
TeX document class: brattlereport (document formatting for internal use) [Github]
