Pub Date : 2023-01-31DOI: 10.1080/07350015.2023.2174548
Xu Guo, Runze Li, Jingyuan Liu, Mudong Zeng
{"title":"Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators","authors":"Xu Guo, Runze Li, Jingyuan Liu, Mudong Zeng","doi":"10.1080/07350015.2023.2174548","DOIUrl":"https://doi.org/10.1080/07350015.2023.2174548","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121103367","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 : 2023-01-31DOI: 10.1080/07350015.2023.2174549
Long Feng, Binghui Liu, Yanyuan Ma
{"title":"A one-sided refined symmetrized data aggregation approach to robust mutual fund selection","authors":"Long Feng, Binghui Liu, Yanyuan Ma","doi":"10.1080/07350015.2023.2174549","DOIUrl":"https://doi.org/10.1080/07350015.2023.2174549","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116355249","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 : 2023-01-30DOI: 10.1080/07350015.2023.2174124
Xinyu Zhang, Dongyu Li, H. Tong
{"title":"On the Least Squares Estimation of Multiple-Threshold-Variable Autoregressive Models","authors":"Xinyu Zhang, Dongyu Li, H. Tong","doi":"10.1080/07350015.2023.2174124","DOIUrl":"https://doi.org/10.1080/07350015.2023.2174124","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124567403","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 : 2023-01-12DOI: 10.1080/07350015.2023.2166515
Xinyu Zhang, Huihang Liu, Yizheng Wei, Yanyuan Ma
{"title":"Prediction using many samples with models possibly containing partially shared parameters","authors":"Xinyu Zhang, Huihang Liu, Yizheng Wei, Yanyuan Ma","doi":"10.1080/07350015.2023.2166515","DOIUrl":"https://doi.org/10.1080/07350015.2023.2166515","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115304233","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 : 2023-01-12DOI: 10.1080/07350015.2023.2166514
Yingying Ma, Chenlei Leng, Hansheng Wang
The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties. However, the vanilla version of bootstrap is no longer feasible computationally for many modern massive datasets due to the need to repeatedly resample the entire data. Therefore, several improvements to the bootstrap method have been made in recent years, which assess the quality of estimators by subsampling the full dataset before resampling the subsamples. Naturally, the performance of these modern subsampling methods is influenced by tuning parameters such as the size of subsamples, the number of subsamples, and the number of resamples per subsample. In this paper, we develop a novel hyperparameter selection methodology for selecting these tuning parameters. Formulated as an optimization problem to find the optimal value of some measure of accuracy of an estimator subject to computational cost, our framework provides closed-form solutions for the optimal hyperparameter values for subsampled bootstrap, subsampled double bootstrap and bag of little bootstraps, at no or little extra time cost. Using the mean square errors as a proxy of the accuracy measure, we apply our methodology to study, compare and improve the performance of these modern versions of bootstrap developed for massive data through simulation study. The results are promising.
{"title":"Optimal Subsampling Bootstrap for Massive Data","authors":"Yingying Ma, Chenlei Leng, Hansheng Wang","doi":"10.1080/07350015.2023.2166514","DOIUrl":"https://doi.org/10.1080/07350015.2023.2166514","url":null,"abstract":"The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties. However, the vanilla version of bootstrap is no longer feasible computationally for many modern massive datasets due to the need to repeatedly resample the entire data. Therefore, several improvements to the bootstrap method have been made in recent years, which assess the quality of estimators by subsampling the full dataset before resampling the subsamples. Naturally, the performance of these modern subsampling methods is influenced by tuning parameters such as the size of subsamples, the number of subsamples, and the number of resamples per subsample. In this paper, we develop a novel hyperparameter selection methodology for selecting these tuning parameters. Formulated as an optimization problem to find the optimal value of some measure of accuracy of an estimator subject to computational cost, our framework provides closed-form solutions for the optimal hyperparameter values for subsampled bootstrap, subsampled double bootstrap and bag of little bootstraps, at no or little extra time cost. Using the mean square errors as a proxy of the accuracy measure, we apply our methodology to study, compare and improve the performance of these modern versions of bootstrap developed for massive data through simulation study. The results are promising.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133058462","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 : 2023-01-10DOI: 10.1080/07350015.2023.2166513
Jungbin Hwang, Gonzalo Valdés
This paper develops new t and F inferences in a low-frequency transformed triangular cointegrating regression when one may not be sure the economic variables are exact unit root processes. We first show that the low-frequency transformed and augmented OLS (TA-OLS) regression exhibits an asymptotic bias term in the limiting distribution. As a result, the size distortion of the testing cointegration vector can be substantially large for even minor deviations from the unit root regressors. We develop a method to correct the asymptotic bias for the cointegration vector. Our modified TA-OLS statistics adjust the locational bias and reflect the estimation uncertainty of the long-run endogeneity parameter in the bias correction term and lead to standard t and F critical values. Based on the modified test statistics, we provide Bonferroni-based inferences to test the cointegration vector. Monte Carlo results show that our approach has the correct size and appealing power for a wide range of local to unity parameters. Also, we find that our method has advantages to the IVX approach when the serial dependence and the long-run endogeneity in the cointegration system are important.
{"title":"Low Frequency Cointegrating Regression with Local to Unity Regressors and Unknown Form of Serial Dependence *","authors":"Jungbin Hwang, Gonzalo Valdés","doi":"10.1080/07350015.2023.2166513","DOIUrl":"https://doi.org/10.1080/07350015.2023.2166513","url":null,"abstract":"This paper develops new t and F inferences in a low-frequency transformed triangular cointegrating regression when one may not be sure the economic variables are exact unit root processes. We first show that the low-frequency transformed and augmented OLS (TA-OLS) regression exhibits an asymptotic bias term in the limiting distribution. As a result, the size distortion of the testing cointegration vector can be substantially large for even minor deviations from the unit root regressors. We develop a method to correct the asymptotic bias for the cointegration vector. Our modified TA-OLS statistics adjust the locational bias and reflect the estimation uncertainty of the long-run endogeneity parameter in the bias correction term and lead to standard t and F critical values. Based on the modified test statistics, we provide Bonferroni-based inferences to test the cointegration vector. Monte Carlo results show that our approach has the correct size and appealing power for a wide range of local to unity parameters. Also, we find that our method has advantages to the IVX approach when the serial dependence and the long-run endogeneity in the cointegration system are important.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"19 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114115973","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 : 2023-01-09DOI: 10.1080/07350015.2023.2166048
S. Hannadige, Jiti Gao, M. Silvapulle, P. Silvapulle
{"title":"Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors","authors":"S. Hannadige, Jiti Gao, M. Silvapulle, P. Silvapulle","doi":"10.1080/07350015.2023.2166048","DOIUrl":"https://doi.org/10.1080/07350015.2023.2166048","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122731003","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 : 2022-11-30DOI: 10.1080/07350015.2022.2151449
Ying Lun Cheung
{"title":"Identification of Time-varying Factor Models","authors":"Ying Lun Cheung","doi":"10.1080/07350015.2022.2151449","DOIUrl":"https://doi.org/10.1080/07350015.2022.2151449","url":null,"abstract":"","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116384689","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 : 2022-11-27DOI: 10.1080/07350015.2023.2249509
Yuya Sasaki, Yulong Wang
Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are tailored to middle quantiles and do not work well for such subpopulations. This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles. With its asymptotic normality, we also propose a method of statistical inference, which is simple to implement. Based on simulation studies, we propose to use our extreme CIC estimator for extreme, such as below 5% and above 95%, quantiles, while the conventional CIC estimator should be used for intermediate quantiles. Applying the proposed method, we study the effects of income gains from the 1993 EITC reform on infant birth weights for those in the most critical conditions. This paper is accompanied by a Stata command.
{"title":"Extreme Changes in Changes*","authors":"Yuya Sasaki, Yulong Wang","doi":"10.1080/07350015.2023.2249509","DOIUrl":"https://doi.org/10.1080/07350015.2023.2249509","url":null,"abstract":"Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are tailored to middle quantiles and do not work well for such subpopulations. This paper proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles. With its asymptotic normality, we also propose a method of statistical inference, which is simple to implement. Based on simulation studies, we propose to use our extreme CIC estimator for extreme, such as below 5% and above 95%, quantiles, while the conventional CIC estimator should be used for intermediate quantiles. Applying the proposed method, we study the effects of income gains from the 1993 EITC reform on infant birth weights for those in the most critical conditions. This paper is accompanied by a Stata command.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130939740","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 : 2022-11-14DOI: 10.1080/07350015.2022.2146696
Shen-Da Chang, P. Cheng, M. Liou
In testing hypotheses pertaining to Lorenz dominance (LD), researchers have examined second- and third-order stochastic dominance using empirical Lorenz processes and integrated stochastic processes with the aid of bootstrap analysis. Among these topics, analysis of third-order stochastic dominance (TSD) based on the notion of risk aversion has been examined using crossing (generalized) Lorenz curves. These facts motivated the present study to characterize distribution pairs displaying the TSD without second-order (generalized Lorenz) dominance. It further motivated the development of likelihood ratio (LR) goodness-of-fit tests for examining the respective hypotheses of the LD, crossing (generalized) Lorenz curves, and TSD through approximate Chi-squared distributions. The proposed LR tests were assessed using simulated distributions, and applied to examine the COVID-19 regional death counts of bivariate samples collected by the World Health Organization between March 2020 and February 2021.
{"title":"Likelihood Ratio Tests for Lorenz Dominance","authors":"Shen-Da Chang, P. Cheng, M. Liou","doi":"10.1080/07350015.2022.2146696","DOIUrl":"https://doi.org/10.1080/07350015.2022.2146696","url":null,"abstract":"In testing hypotheses pertaining to Lorenz dominance (LD), researchers have examined second- and third-order stochastic dominance using empirical Lorenz processes and integrated stochastic processes with the aid of bootstrap analysis. Among these topics, analysis of third-order stochastic dominance (TSD) based on the notion of risk aversion has been examined using crossing (generalized) Lorenz curves. These facts motivated the present study to characterize distribution pairs displaying the TSD without second-order (generalized Lorenz) dominance. It further motivated the development of likelihood ratio (LR) goodness-of-fit tests for examining the respective hypotheses of the LD, crossing (generalized) Lorenz curves, and TSD through approximate Chi-squared distributions. The proposed LR tests were assessed using simulated distributions, and applied to examine the COVID-19 regional death counts of bivariate samples collected by the World Health Organization between March 2020 and February 2021.","PeriodicalId":118766,"journal":{"name":"Journal of Business & Economic Statistics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125601831","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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