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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
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publications
Placebo Response in Pediatric Anxiety Disorders: Results from the Child/Adolescent Anxiety Multimodal Study (CAMS).
Published in Journal of Child and Adolescent Psychopharmacology, 2017
Abstract: The aim of this study is to identify predictors of pill placebo response and to characterize the temporal course of pill placebo response in anxious youth. Data from placebo-treated patients (N=76) in the Child/Adolescent Anxiety Multimodal Study (CAMS), a multisite, randomized controlled trial that examined the efficacy of cognitive-behavioral therapy, sertraline, their combination, and placebo for the treatment of separation, generalized, and social anxiety disorders, were evaluated. Multiple linear regression models identified features associated with placebo response and models were confirmed with leave-one-outcross validation. The likelihood of improvement in patients receiving pill placebo—overtime—relative to improvement associated with active treatment was determined using probabilistic Bayesian analyses. Based on a categorical definition of response (Clinical Global Impressions-Improvement Scale score <= 2), nonresponders (n=48), and pill placebo responders (n=18) didnotdiffer in age (p=0.217),sex (p=0.980),race (p=0.743), or primary diagnosis (all ps > 0.659). In terms of change in anxiety symptoms, separation anxiety disorder and treatment expectation were associated with the degree of pill placebo response.Greater probability of placebo-related anxiety symptom improvement was observed early in the course of treatment (baseline to week 4, p < 0.0001). No significant change in the probability of placebo-related improvement was observed after week 4 (weeks 4–8, p=0.07; weeks 8–12, p=0.85), whereas the probability of improvement, in general, significantly increased week over week with active treatment. Pill placebo-related improvement occurs early in the course of treatment and both clinical factors and expectation predict this improvement. Additionally,probabilistic approaches may refine our understanding and prediction of pill placebo response.
Recommended citation: Strawn JR,Dobson ET,Mills JA,Cornwall GJ,Sakolsky D,Birmaher B,Compton SN,Piacentini J,McCracken JT,Ginsburg GS, Kendall PC, Walkup JT, Albano AM,Rynn MA. "Placebo Response in Pediatric Anxiety Disorders: Results from the Child/Adolescent Anxiety Multimodal Study (CAMS)." Journal of Child and Adolescent Psychopharmacology. (2017) 17.6: 501-508.
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At the frontier between local and global interactions.
Published in Regional Research Frontiers Volume II, 2017
Recommended citation: Cornwall, G., Kim C., & Parent O. " At the frontier between local and global interactions. " Regional Research Frontiers Volume II (Sprinter, Edited by Randall JAckson and Peter Schaeffer), 2017. ISSN 1430-9602.
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Embracing heterogeneity: the spatial autoregressive mixture model
Published in Regional Science and Urban Economics, 2017
Abstract:In this paper a mixture distribution model is extended to include spatial dependence of the autoregressive type. The resulting model nests both spatial heterogeneity and spatial dependence as special cases. A data generation process is outlined that incorporates both a finite mixture of normal distributions and spatial dependence. Whether group assignment is completely random by nature or displays some locational “pattern”, the proposed spatial-mix estimation procedure is always able to recover the true parameters. As an illustration, a basic hedonic price model is investigated that includes sub-groups of data with heterogeneous coefficients in addition to spatially clustered elements.
Recommended citation: Cornwall, Gary J., and Olivier Parent. "Embracing heterogeneity: the spatial autoregressive mixture model." Regional Science and Urban Economics . (2017) 64, 148-161.
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Buspirone in children and adolescents with anxiety: a review and Bayesian analysis of abandoned randomized controlled trials
Published in Journal of Child and Adolescent Psychopharmacology, 2018
Abstract: An increasing number of abandoned clinical trials have forestalled efforts to advance the evidence base for the treatment of mood and anxiety disorders in children and adolescents. With this in mind, we sought to present and validate a Bayesian approach for the reanalysis of summary data in abandoned clinical trials and to review and re-evaluate available pharmacokinetic, tolerability, and efficacy data from two large, randomized controlled trials of buspirone in pediatric patients with generalized anxiety disorder (GAD). Prospective, randomized, parallel-group controlled trials of buspirone in pediatric patients with GAD as well as associated pharmacokinetic studies were identified and data were extracted. In addition to descriptive statistics, marginal posterior densities for each variable ofinterest were determined and a Monte Carlo pseudosample was generated with random draws obtained from the Student’s t-distribution to assess, with inferential statistics, differences in variables of interest. Buspirone was evaluated in one flexibly dosed (N=227) and one fixed-dose (N=341) trial in children and adolescents aged 6–17 years with a primary diagnosis of GAD.With regard to improvement in the sum of the Columbia Schedule for Affective Disorders and Schizophrenia GAD items, buspirone did not separate from placebo in the fixed-dose trial at low (95%CI:-0.78to2.39, p=0.32)or high dose(95% CI:-0.87 to 1.87,p=0.47) nor did it separate from placebo in the flexibly dosed study (95% CI:-0.3 to 1.9, p=0.15). Drop out as a result of a treatment-emergent adverse event was significantly greater in buspirone-treated patients compared to placebo (p=0.011). Side effects were consistent with the known profile of buspirone with light headedness occurring more frequently in buspirone-treated patients (p < 0.001). Buspirone is well tolerated in pediatric patients with GAD, although two randomized controlled trials were underpowered to detect small effect sizes (Cohen’s d < 0.15). Finally, Bayesian approaches may facilitate re-examination of data from abandoned clinical trials.
Recommended citation: Strawn, Jeffrey R., Jeffrey A. Mills, Gary J. Cornwall, Sarah A. Mossman, Sara T. Varney, Brooks R. Keeshin, and Paul E. Croarkin. "Buspirone in children and adolescents with anxiety: a review and Bayesian analysis of abandoned randomized controlled trials." Journal of Child and Adolescent Psychopharmacology. 28, no. 1 (2018): 2-9.
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Predictive Testing for Granger Causality via Posterior Simulation and Cross-Validation.
Published in Advances in Econometrics, 2019
Abstract: This paper develops a predictive approach to Granger causality testing that utilizes k-fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on Granger causality between inflation and unemployment rates.
Recommended citation: Cornwall, Gary J., et al. " Predictive Testing for Granger Causality via Posterior Simulation and Cross-Validation. " Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A (Advances in Econometrics 40), (2019): 275-292.
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Data Science for Public Policy
Published in Springer International Publishing, 2021
Recommended citation: Chen, Jeffrey C., Edward A. Rubin, & Gary J. Cornwall. " Data Science for Public Policy " Springer International Publishing , 2021.
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Indirect effects and causal Inference: reconsidering regression discontinuity
Published in Journal of Spatial Econometrics, 2021
Abstract: Causal inference models, like regression discontinuity (RD) design, rely upon some variation of the no-interference assumption, where peer effects or spatial spillovers are null. Given the increased application of network, spatial, and peer effects models, this paper reconsiders RD design when this assumption is not satisfied, yielding indirect effects of the treatment in addition to the traditionally measured direct effects. Using a combination of residualization and numeric integration we develop a method using the Spatial Durbin Framework—which retains the full adjacency matrix and allows for a full accounting of these cross-sectional interactions. As an application, we revisit a well-known RD design using U.S. House of Representatives election results from 1945–1995, finding close election wins have substantial indirect effects which previously were unaccounted.
Recommended citation: Cornwall, G., & Beau Sauley. "Indirect effects and causal Inference: reconsidering regression discontinuity " Journal of Spatial Econometrics, 2(1), p.8.
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It’s The Smell: How Resolving Uncertainty about Local Disamenties Affects the Housing Market
Published in Land Economics, 2025
Abstract: This study examines how the housing market responds to closing a major environmental disamenity nearby, particularly when the credibility of local policy is uncertain. Fresh Kills Landfill (NY) provides an empirical setting to examine this question across multiple distinct events with varying credibility signals. Results from a difference-in-differences analysis show that the market prices and volumes respond sharply to credible actions (i.e., capping the landfill and park transitioning) rather than policy announcements. The findings suggest resolving uncertainty can have a powerful supply effect for housing markets, applying downward pressure on prices in the short run, thereby overshadowing plausibly positive demand effects.
Recommended citation: Chen, J., Cornwall, G., & Scott Wentland. "Its The Smell: How Resolving Uncertainty about Local Disamenties Affects the Housing Market " Land Economics, Forthcoming.
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Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach
Published in AEA Papers and Proceedings, 2025
Recommended citation: Cornwall, Gary and Marina Gindelsky. 2025. " Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach " AEA Papers and Proceedings. 115:79-84.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Time Series Econometrics
Graduate Course, George Washington University, Department of Economics, 2025
Topics in Machine Learning for Economists
Graduate Course, George Washington University, Department of Economics, 2025
working
The Dark Side of the Moon: Searching for the Other Half of Seasonality
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Abstract: Seasonality is among the most visible properties in time series data, yet a multitude of statistical tests devised over decades of research have only achieved limited success in its detection. In this paper we examine eight existing tests of seasonality and show that there is significant variation in how they classify a series. We then show how this variation, combined with characteristics of the time series (e.g. autocorrelation, frequency, skewness, kurtosis, etc.), can be exploited by a Random Forest framework to map the hypothesis test space and make more accurate predictions regarding the seasonal disposition of a series. Our proposed method reduces Type II errors by approximately sixty percentage points over the next best alternative.
Roots from Trees: A Machine Learning Approach to Unit Root Detection
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Abstract: In this paper we draw inspiration from the ensemble forecasting and model averaging literature and use a gradient descent boosting algorithm to exploit variation between test statistics used to determine if a series contains a unit root. The result is a pseudo-composite ML-based test for unit roots which is four to six percentage points more accurate than the next best traditional test. Through a train-validation framework this method allows for control over Type I error rates and the gains in power come with little variation in specificity (empirical size). Additionally, the proposed method is agnostic towards deterministic elements traditionally needed in the established testing environment and thus closes off an additional error path for unit root testing; that of model misspecification. We illustrate this new testing procedure by applying it to an established benchmark data set and examining the state-level hypothesis of unemployment hysteresis.
For What It’s Worth: Measuring Land Value in the Era of Big Data and Machine Learning
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Abstract: This paper develops a new method for valuing land, a key asset on a nation’s balance sheet. The method first employs an unsupervised machine learning method, kmeans clustering, to discretize unobserved heterogeneity, which we then combine with a supervised learning algorithm, gradient boosted trees (GBT), to obtain property-level price predictions and estimates of the land component. Our initial results from a large national dataset show this approach routinely outperforms hedonic regression methods (as used by the U.K.’s Office for National Statistics, for example) in out-of-sample price predictions. To exploit the best of both methods, we further explore a composite approach using model stacking, finding it outperforms all methods in out-of-sample tests and a benchmark test against nearby vacant land sales. In an application, we value residential, commercial, industrial, and agricultural land for the entire contiguous U.S. from 2006-2015. The results offer new insights into valuation and demonstrate how a unified method can build national and subnational estimates of land value from detailed, parcel-level data. We discuss further applications to economic policy and the property valuation literature more generally.
Alloy Inference: Tests of a Single Null
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Abstract: This paper presents a new joint testing framework that fuses multiple test statistics into a single, more powerful inference tool. Using the probability integral transform to confine the support to the unit-hypercube, I use simulated null cases and Archimedean copulas to approximate the underlying joint null distribution of two or more statistics. Analogous to an alloy in metallurgy, where the final product has [typically] stronger properties than its constituent parts, I show how two or more tests can be combined to outperform a single test statistic in finite samples. To illustrate the performance of this approach, I provide a stylized example using the game of craps such that trade-offs can be be assessed in economic terms. Under potential uncertainty in the fairness of game dice, the proposed method–a combination of the Student-t and $\chi^2$ statistic–provides increased power, producing a revenue distribution which second-order stochastically dominates its constituent parts.