Pub Date : 2023-12-12eCollection Date: 2024-01-01DOI: 10.1080/07350015.2023.2290739
Max-Sebastian Dovì, Anders Bredahl Kock, Sophocles Mavroeidis
We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments-possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.
{"title":"A Ridge-Regularized Jackknifed Anderson-Rubin Test.","authors":"Max-Sebastian Dovì, Anders Bredahl Kock, Sophocles Mavroeidis","doi":"10.1080/07350015.2023.2290739","DOIUrl":"10.1080/07350015.2023.2290739","url":null,"abstract":"<p><p>We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments-possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.</p>","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"42 3","pages":"1083-1094"},"PeriodicalIF":3.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11182230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.1080/07350015.2023.2282497
Haitian Xie
Abstract–This paper studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, which expands upon the traditional LATE model to include multivalued treatments. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of causal parameters: the local average structural function (LASF) and the local average structural function for the treated (LASFT). The moment conditions generated by the EIF satisfy two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment conditions, we propose the double/debiased machine learning (DML) estimator for estimating the LASF. The DML estimator is well-suited for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application of these methods, we examine the potential health outcome across different types of health insurance plans using data from the Oregon Health Insurance Experiment.Keywords: Double RobustnessEfficient Influence FunctionMultivalued TreatmentNeyman OrthogonalityUnordered MonotonicityWeak Identification.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Efficient and Robust Estimation of the Generalized LATE Model","authors":"Haitian Xie","doi":"10.1080/07350015.2023.2282497","DOIUrl":"https://doi.org/10.1080/07350015.2023.2282497","url":null,"abstract":"Abstract–This paper studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, which expands upon the traditional LATE model to include multivalued treatments. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of causal parameters: the local average structural function (LASF) and the local average structural function for the treated (LASFT). The moment conditions generated by the EIF satisfy two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment conditions, we propose the double/debiased machine learning (DML) estimator for estimating the LASF. The DML estimator is well-suited for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application of these methods, we examine the potential health outcome across different types of health insurance plans using data from the Oregon Health Insurance Experiment.Keywords: Double RobustnessEfficient Influence FunctionMultivalued TreatmentNeyman OrthogonalityUnordered MonotonicityWeak Identification.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"53 46","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/07350015.2023.2277164
Sung Jae Jun, Sokbae Lee
We study causal inference under case-control and case-population sampling. For this purpose, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risk defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We then discuss averaging the conditional (log) odds ratio and propose an algorithm for semiparametrically efficient estimation when averaging is based only on the (conditional) distributions of the covariates that are identified in the data. We also offer algorithms for causal inference if the true population distribution of the covariates is desirable for aggregation. We show the usefulness of our approach by studying two empirical examples from social sciences: the benefit of attending private school for entering a prestigious university in Pakistan and the causal relationship between staying in school and getting involved with drug-trafficking gangs in Brazil.
{"title":"Causal inference under outcome-based sampling with monotonicity assumptions","authors":"Sung Jae Jun, Sokbae Lee","doi":"10.1080/07350015.2023.2277164","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277164","url":null,"abstract":"We study causal inference under case-control and case-population sampling. For this purpose, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risk defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We then discuss averaging the conditional (log) odds ratio and propose an algorithm for semiparametrically efficient estimation when averaging is based only on the (conditional) distributions of the covariates that are identified in the data. We also offer algorithms for causal inference if the true population distribution of the covariates is desirable for aggregation. We show the usefulness of our approach by studying two empirical examples from social sciences: the benefit of attending private school for entering a prestigious university in Pakistan and the causal relationship between staying in school and getting involved with drug-trafficking gangs in Brazil.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"50 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135765836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/07350015.2023.2277857
Pallavi Basu, Luella Fu, Alessio Saretto, Wenguang Sun
In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.
{"title":"An Empirical Bayes Approach to Controlling the False Discovery Exceedance","authors":"Pallavi Basu, Luella Fu, Alessio Saretto, Wenguang Sun","doi":"10.1080/07350015.2023.2277857","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277857","url":null,"abstract":"In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"134 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/07350015.2023.2277171
Davide Delle Monache, Andrea De Polis, Ivan Petrella
AbstractWe model permanent and transitory changes of the predictive density of US GDP growth. A substantial increase in downside risk to US economic growth emerges over the last 30 years, associated with the long-run growth slowdown started in the early 2000s. Conditional skewness moves procyclically, implying negatively skewed predictive densities ahead and during recessions, often anticipated by deteriorating financial conditions. Conversely, positively skewed distributions characterize expansions. The modelling framework ensures robustness to tail events, allows for both dense or sparse predictor designs, and delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks.Keywords: Business cycledownside riskskewnessscore drivenfinancial conditionsDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Modeling and Forecasting Macroeconomic Downside Risk*","authors":"Davide Delle Monache, Andrea De Polis, Ivan Petrella","doi":"10.1080/07350015.2023.2277171","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277171","url":null,"abstract":"AbstractWe model permanent and transitory changes of the predictive density of US GDP growth. A substantial increase in downside risk to US economic growth emerges over the last 30 years, associated with the long-run growth slowdown started in the early 2000s. Conditional skewness moves procyclically, implying negatively skewed predictive densities ahead and during recessions, often anticipated by deteriorating financial conditions. Conversely, positively skewed distributions characterize expansions. The modelling framework ensures robustness to tail events, allows for both dense or sparse predictor designs, and delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks.Keywords: Business cycledownside riskskewnessscore drivenfinancial conditionsDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"465 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/07350015.2023.2277172
Xiaorong Yang, Jia Chen, Degui Li, Runze Li
AbstractThis paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.Keywords: Cluster analysisfunctional-coefficient modelsincidental parameterlatent groupslocal linear estimationpanel dataquantile regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure","authors":"Xiaorong Yang, Jia Chen, Degui Li, Runze Li","doi":"10.1080/07350015.2023.2277172","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277172","url":null,"abstract":"AbstractThis paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.Keywords: Cluster analysisfunctional-coefficient modelsincidental parameterlatent groupslocal linear estimationpanel dataquantile regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"53 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/07350015.2023.2271538
Karsten Reichold, Carsten Jentsch
Traditional tests of hypotheses on the cointegrating vector are well known to suffer from severe size distortions in finite samples, especially when the data are characterized by large levels of endogeneity or error serial correlation. To address this issue, we combine a vector autoregressive (VAR) sieve bootstrap to construct critical values with a self-normalization approach that avoids direct estimation of long-run variance parameters when computing test statistics. To asymptotically justify this method, we prove bootstrap consistency for the self-normalized test statistics under mild conditions. In addition, the underlying bootstrap invariance principle allows us to prove bootstrap consistency also for traditional test statistics based on popular modified OLS estimators. Simulation results show that using bootstrap critical values instead of asymptotic critical values reduces size distortions associated with traditional test statistics considerably, but combining the VAR sieve bootstrap with self-normalization can lead to even less size distorted tests at the cost of only small power losses. We illustrate the usefulness of the VAR sieve bootstrap in empirical applications by analyzing the validity of the Fisher effect in 19 OECD countries.
{"title":"Bootstrap Inference in Cointegrating Regressions: Traditional and Self-Normalized Test Statistics","authors":"Karsten Reichold, Carsten Jentsch","doi":"10.1080/07350015.2023.2271538","DOIUrl":"https://doi.org/10.1080/07350015.2023.2271538","url":null,"abstract":"Traditional tests of hypotheses on the cointegrating vector are well known to suffer from severe size distortions in finite samples, especially when the data are characterized by large levels of endogeneity or error serial correlation. To address this issue, we combine a vector autoregressive (VAR) sieve bootstrap to construct critical values with a self-normalization approach that avoids direct estimation of long-run variance parameters when computing test statistics. To asymptotically justify this method, we prove bootstrap consistency for the self-normalized test statistics under mild conditions. In addition, the underlying bootstrap invariance principle allows us to prove bootstrap consistency also for traditional test statistics based on popular modified OLS estimators. Simulation results show that using bootstrap critical values instead of asymptotic critical values reduces size distortions associated with traditional test statistics considerably, but combining the VAR sieve bootstrap with self-normalization can lead to even less size distorted tests at the cost of only small power losses. We illustrate the usefulness of the VAR sieve bootstrap in empirical applications by analyzing the validity of the Fisher effect in 19 OECD countries.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1080/07350015.2023.2271071
Michela Bia, Martin Huber, Lukáš Lafférs
AbstractThis paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning- based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R.Keywords: sample selectiondouble machine learningdoubly robust estimationefficient scoreDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Double machine learning for sample selection models+","authors":"Michela Bia, Martin Huber, Lukáš Lafférs","doi":"10.1080/07350015.2023.2271071","DOIUrl":"https://doi.org/10.1080/07350015.2023.2271071","url":null,"abstract":"AbstractThis paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning- based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R.Keywords: sample selectiondouble machine learningdoubly robust estimationefficient scoreDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/07350015.2023.2223592
Julie Holland Mortimer
Click to increase image sizeClick to decrease image size Notes1 Figure 1 is available online at: https://insurance.ohio.gov/wps/wcm/connect/gov/ea3f5cf0-181b-47ed-bdbd-a060b6613a4d/CompleteAutoGuide+2022.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=ROOTWORKSPACE.Z18_M1HGGIK0N0JO00QO9DDDDM3000-ea3f5cf0-181b-47ed-bdbd-a060b6613a4d-n.SMM7v. I don’t know the state from which the authors’ data are collected, and the format of insurance quotes in the author’s data may differ from that shown here; this is meant for illustrative purposes only.2 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#types. Accessed on January 5, 2023; current version was updated March 2, 2023.3 See https://www.forbes.com/advisor/car-insurance/comprehensive-vs-collision-auto-insurance/. Last accessed on March 26, 2023.4 According to Statista, roughly 85% of new cars (and 40% of used cars) are financed. The number of new cars that are leased has been falling in recent years from a high of almost 1 in 3 in 2020 to roughly 1 in 5 today.5 See https://www.forbes.com/advisor/car-insurance/comprehensive-vs-collision-auto-insurance/. Last accessed on March 26, 2023.6 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#types. Accessed on March 26, 2023.7 It’s important to recognize that we need ex-ante estimates of claim amounts, analogous to claim probabilities.8 See https://www.moneygeek.com/insurance/auto/do-i-need-comprehensive-collision/. Accessed on March 26, 2023.9 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#impact. Accessed on March 26, 2023.
{"title":"Discussion of Levon Barseghyan and Francesca Molinari’s “Risk Preference Types, Limited Consideration, and Welfare”","authors":"Julie Holland Mortimer","doi":"10.1080/07350015.2023.2223592","DOIUrl":"https://doi.org/10.1080/07350015.2023.2223592","url":null,"abstract":"Click to increase image sizeClick to decrease image size Notes1 Figure 1 is available online at: https://insurance.ohio.gov/wps/wcm/connect/gov/ea3f5cf0-181b-47ed-bdbd-a060b6613a4d/CompleteAutoGuide+2022.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=ROOTWORKSPACE.Z18_M1HGGIK0N0JO00QO9DDDDM3000-ea3f5cf0-181b-47ed-bdbd-a060b6613a4d-n.SMM7v. I don’t know the state from which the authors’ data are collected, and the format of insurance quotes in the author’s data may differ from that shown here; this is meant for illustrative purposes only.2 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#types. Accessed on January 5, 2023; current version was updated March 2, 2023.3 See https://www.forbes.com/advisor/car-insurance/comprehensive-vs-collision-auto-insurance/. Last accessed on March 26, 2023.4 According to Statista, roughly 85% of new cars (and 40% of used cars) are financed. The number of new cars that are leased has been falling in recent years from a high of almost 1 in 3 in 2020 to roughly 1 in 5 today.5 See https://www.forbes.com/advisor/car-insurance/comprehensive-vs-collision-auto-insurance/. Last accessed on March 26, 2023.6 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#types. Accessed on March 26, 2023.7 It’s important to recognize that we need ex-ante estimates of claim amounts, analogous to claim probabilities.8 See https://www.moneygeek.com/insurance/auto/do-i-need-comprehensive-collision/. Accessed on March 26, 2023.9 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#impact. Accessed on March 26, 2023.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"35 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/07350015.2023.2216740
Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu
Abstract–Barseghyan and Molinari give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but context-independent. In this comment, we build on their insights and present identification results in a setting where the risk preferences are allowed to be context-dependent.KEYWORDS: Discrete choiceRandom limited considerationRandom utilitySemi-nonparametric identification AcknowledgmentsWe thank Francesca Molinari and the participants at the 2023 ASSA meetings (JBES Session: Risk Preference Types, Limited Consideration, and Welfare) for comments.Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingCattaneo gratefully acknowledges financial support from the National Science Foundation through grants SES-1947805 and SES-2241575.
{"title":"Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023)","authors":"Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu","doi":"10.1080/07350015.2023.2216740","DOIUrl":"https://doi.org/10.1080/07350015.2023.2216740","url":null,"abstract":"Abstract–Barseghyan and Molinari give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but context-independent. In this comment, we build on their insights and present identification results in a setting where the risk preferences are allowed to be context-dependent.KEYWORDS: Discrete choiceRandom limited considerationRandom utilitySemi-nonparametric identification AcknowledgmentsWe thank Francesca Molinari and the participants at the 2023 ASSA meetings (JBES Session: Risk Preference Types, Limited Consideration, and Welfare) for comments.Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingCattaneo gratefully acknowledges financial support from the National Science Foundation through grants SES-1947805 and SES-2241575.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135900503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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