Pub Date : 2023-12-14DOI: 10.1080/07350015.2023.2293162
Rong Jiang, Keming Yu
{"title":"Unconditional quantile regression for streaming data sets","authors":"Rong Jiang, Keming Yu","doi":"10.1080/07350015.2023.2293162","DOIUrl":"https://doi.org/10.1080/07350015.2023.2293162","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"1987 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138973785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the Combination of Naive and Mean-Variance Portfolio Strategies","authors":"Nathan Lassance, Rodolphe Vanderveken, Frédéric Vrins","doi":"10.1080/07350015.2023.2256801","DOIUrl":"https://doi.org/10.1080/07350015.2023.2256801","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129325949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1080/07350015.2023.2252471
Carolina Caetano, Gregorio Caetano, Eric Nielsen
{"title":"Correcting for Endogeneity in Models with Bunching*","authors":"Carolina Caetano, Gregorio Caetano, Eric Nielsen","doi":"10.1080/07350015.2023.2252471","DOIUrl":"https://doi.org/10.1080/07350015.2023.2252471","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131195363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.1080/07350015.2023.2252881
Zaichao Du, Pei, Xuhui Wang, Tao Yang
{"title":"Powerful Backtests for Historical Simulation Expected Shortfall Models","authors":"Zaichao Du, Pei, Xuhui Wang, Tao Yang","doi":"10.1080/07350015.2023.2252881","DOIUrl":"https://doi.org/10.1080/07350015.2023.2252881","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131893274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-25DOI: 10.1080/07350015.2023.2252040
David M. Kaplan
Instead of testing for unanimous agreement, I propose learning how broad of a consensus favors one distribution over another (of income, productivity, asset returns, test scores, etc.). Specifically, I propose statistical inference methods to learn about the set of utility functions for which one distribution has higher expected utility than another. With high probability, an “inner” confidence set is contained within this true set, while an “outer” confidence set contains the true set. Such confidence sets can be formed by inverting a proposed multiple testing procedure that controls the familywise error rate. Theoretical justification comes from empirical process results, given that very large classes of utility functions are generally Donsker (subject to finite moments). The theory additionally justifies a uniform (over utility functions) confidence band of expected utility differences, as well as tests with a utility-based “restricted stochastic dominance” as either the null or alternative hypothesis. Simulated and empirical examples illustrate the methodology. JEL classification: C29
{"title":"Inference on Consensus Ranking of Distributions","authors":"David M. Kaplan","doi":"10.1080/07350015.2023.2252040","DOIUrl":"https://doi.org/10.1080/07350015.2023.2252040","url":null,"abstract":"Instead of testing for unanimous agreement, I propose learning how broad of a consensus favors one distribution over another (of income, productivity, asset returns, test scores, etc.). Specifically, I propose statistical inference methods to learn about the set of utility functions for which one distribution has higher expected utility than another. With high probability, an “inner” confidence set is contained within this true set, while an “outer” confidence set contains the true set. Such confidence sets can be formed by inverting a proposed multiple testing procedure that controls the familywise error rate. Theoretical justification comes from empirical process results, given that very large classes of utility functions are generally Donsker (subject to finite moments). The theory additionally justifies a uniform (over utility functions) confidence band of expected utility differences, as well as tests with a utility-based “restricted stochastic dominance” as either the null or alternative hypothesis. Simulated and empirical examples illustrate the methodology. JEL classification: C29","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-04DOI: 10.1080/07350015.2023.2238774
G. Kapetanios, L. Serlenga, Y. Shin
{"title":"An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects","authors":"G. Kapetanios, L. Serlenga, Y. Shin","doi":"10.1080/07350015.2023.2238774","DOIUrl":"https://doi.org/10.1080/07350015.2023.2238774","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128817844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1080/07350015.2023.2241529
M. Gechter
How informative are treatment effects estimated in one region or time period for another region or time? In this paper, I derive bounds on the average treatment effect in a context of interest using experimental evidence from another context. The bounds are based on (1) the information identified about treatment effect heterogeneity due to unobservables in the experiment and (2) using differences in outcome distributions across contexts to learn about differences in distributions of unobservables. Empirically, using data from a pair of remedial education experiments carried out in India, I show the bounds are able to recover average treatment effects in one location using results from the other while the benchmark method cannot.
{"title":"Generalizing the Results from Social Experiments: Theory and Evidence from India","authors":"M. Gechter","doi":"10.1080/07350015.2023.2241529","DOIUrl":"https://doi.org/10.1080/07350015.2023.2241529","url":null,"abstract":"How informative are treatment effects estimated in one region or time period for another region or time? In this paper, I derive bounds on the average treatment effect in a context of interest using experimental evidence from another context. The bounds are based on (1) the information identified about treatment effect heterogeneity due to unobservables in the experiment and (2) using differences in outcome distributions across contexts to learn about differences in distributions of unobservables. Empirically, using data from a pair of remedial education experiments carried out in India, I show the bounds are able to recover average treatment effects in one location using results from the other while the benchmark method cannot.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130774475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-11DOI: 10.1080/07350015.2023.2223683
Xuanli Yang, Zhoufan Zhu, Dongyu Li, K. Zhu
{"title":"Asset Pricing via the Conditional Quantile Variational Autoencoder","authors":"Xuanli Yang, Zhoufan Zhu, Dongyu Li, K. Zhu","doi":"10.1080/07350015.2023.2223683","DOIUrl":"https://doi.org/10.1080/07350015.2023.2223683","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123074830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-07DOI: 10.1080/07350015.2023.2221974
J. Morley, Trung Duc Tran, Benjamin Wong
{"title":"A Simple Correction for Misspecification in Trend-Cycle Decompositions with an Application to Estimating r*","authors":"J. Morley, Trung Duc Tran, Benjamin Wong","doi":"10.1080/07350015.2023.2221974","DOIUrl":"https://doi.org/10.1080/07350015.2023.2221974","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116218783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-08DOI: 10.1080/07350015.2023.2293166
David Ardia, A. Dufays, C. O. Criado
We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting.
{"title":"Linking Frequentist and Bayesian Change-Point Methods","authors":"David Ardia, A. Dufays, C. O. Criado","doi":"10.1080/07350015.2023.2293166","DOIUrl":"https://doi.org/10.1080/07350015.2023.2293166","url":null,"abstract":"We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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