Pub Date : 2021-10-21DOI: 10.1080/07474938.2021.1889176
Xun Lu, Ke Miao, Liangjun Su
Abstract In this paper, we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method.
{"title":"Determination of different types of fixed effects in three-dimensional panels*","authors":"Xun Lu, Ke Miao, Liangjun Su","doi":"10.1080/07474938.2021.1889176","DOIUrl":"https://doi.org/10.1080/07474938.2021.1889176","url":null,"abstract":"Abstract In this paper, we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"40 1","pages":"867 - 898"},"PeriodicalIF":1.2,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474938.2021.1889176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47596745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1080/07474938.2021.1889207
Qian Wang, S. Chen
Abstract In influential articles Powell (Journal of Econometrics 25(3):303–325, 1984; Journal of Econometrics 32(1):143–155, 1986) proposed optimization-based censored least absolute deviations estimator (CLAD) and general censored quantile regression estimator (CQR). It has been recognized, however, that this optimization-based estimator may perform poorly in finite samples (e.g., Khan and Powell, Journal of Econometrics 103(1–2):73–110, 2001; Fitzenberger, Handbook of Statistics. Elsevier, 1996; Fitzenberger and Winker, Computational Statistics & Data Analysis 52(1):88–108, 2007; Koenker, Journal of Statistical Software 27(6), 2008). In this paper we propose a moment-based censored quantile regression estimator (MCQR). While both the CQR and MCQR estimators have the same large sample properties, our simulation results suggest certain advantage of our moment-based estimator (MCQR). In addition, the empirical likelihood methods for the uncensored model (e.g., Whang 2006; Otsu, Journal of Econometrics 142(1):508–538, 2008) can readily be adapted to the censored model within our method of moment estimation framework. When both censoring and endogeneity are present, we develop an instrumental variable censored quantile regression estimator (IVCQR) by combining the insights of Chernozhukov and Hansen’s (Journal of Econometrics 132(2):491–525, 2006; Journal of Econometrics 142(1):379–398, 2008) instrumental variables quantile regression estimator (IVQR) and the MCQR. Simulation results indicate that the IVCQR estimator performs well.
鲍威尔(Journal of Econometrics, 25(3): 303-325, 1984;计量经济学学报，32(1):143-155,1986)提出了基于优化的截后最小绝对偏差估计(CLAD)和一般截后分位数回归估计(CQR)。然而，人们已经认识到，这种基于优化的估计器在有限样本中可能表现不佳(例如，Khan和Powell, Journal of Econometrics 103(1-2):73 - 110,2001;菲岑伯格，《统计手册》。爱思唯尔,1996;菲岑伯格和温克，计算统计与数据分析52(1):88-108,2007;Koenker, Journal of Statistical Software 27(6)， 2008)。本文提出了一种基于矩的截尾分位数回归估计器。虽然CQR和MCQR估计器都具有相同的大样本特性，但我们的仿真结果表明，我们的矩基估计器(MCQR)具有一定的优势。此外，未经审查的模型的经验似然方法(例如，Whang 2006;Otsu, Journal of Econometrics 142(1): 508-538, 2008)可以很容易地在我们的矩估计框架方法中适应删节模型。结合Chernozhukov和Hansen的见解(Journal Econometrics 132(2):491 - 525,2006)，我们开发了工具变量删节分位数回归估计(IVCQR);计量经济学报(1):1 - 4 .中国经济发展的新趋势。仿真结果表明，该IVCQR估计器性能良好。
{"title":"Moment estimation for censored quantile regression","authors":"Qian Wang, S. Chen","doi":"10.1080/07474938.2021.1889207","DOIUrl":"https://doi.org/10.1080/07474938.2021.1889207","url":null,"abstract":"Abstract In influential articles Powell (Journal of Econometrics 25(3):303–325, 1984; Journal of Econometrics 32(1):143–155, 1986) proposed optimization-based censored least absolute deviations estimator (CLAD) and general censored quantile regression estimator (CQR). It has been recognized, however, that this optimization-based estimator may perform poorly in finite samples (e.g., Khan and Powell, Journal of Econometrics 103(1–2):73–110, 2001; Fitzenberger, Handbook of Statistics. Elsevier, 1996; Fitzenberger and Winker, Computational Statistics & Data Analysis 52(1):88–108, 2007; Koenker, Journal of Statistical Software 27(6), 2008). In this paper we propose a moment-based censored quantile regression estimator (MCQR). While both the CQR and MCQR estimators have the same large sample properties, our simulation results suggest certain advantage of our moment-based estimator (MCQR). In addition, the empirical likelihood methods for the uncensored model (e.g., Whang 2006; Otsu, Journal of Econometrics 142(1):508–538, 2008) can readily be adapted to the censored model within our method of moment estimation framework. When both censoring and endogeneity are present, we develop an instrumental variable censored quantile regression estimator (IVCQR) by combining the insights of Chernozhukov and Hansen’s (Journal of Econometrics 132(2):491–525, 2006; Journal of Econometrics 142(1):379–398, 2008) instrumental variables quantile regression estimator (IVQR) and the MCQR. Simulation results indicate that the IVCQR estimator performs well.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"40 1","pages":"815 - 829"},"PeriodicalIF":1.2,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474938.2021.1889207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45054361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21DOI: 10.1080/07474938.2021.1889206
Yu Sun, Karen X Yan, Qi Li
Abstract This paper considers the estimation of average treatment effect using propensity score method. We propose to use a semiparametric single-index model to estimate the propensity score. This avoids the curse of dimensionality problem with the nonparametric method based propensity score estimator. We establish the asymptotic distribution of the average treatment effect estimator. Monte Carlo simulation results show that the proposed method works well in finite samples and outperforms the conventional nonparametric kernel approach. We apply the proposed method to an empirical data examining the efficacy of right heart catheterization on medical outcomes.
{"title":"Estimation of average treatment effect based on a semiparametric propensity score","authors":"Yu Sun, Karen X Yan, Qi Li","doi":"10.1080/07474938.2021.1889206","DOIUrl":"https://doi.org/10.1080/07474938.2021.1889206","url":null,"abstract":"Abstract This paper considers the estimation of average treatment effect using propensity score method. We propose to use a semiparametric single-index model to estimate the propensity score. This avoids the curse of dimensionality problem with the nonparametric method based propensity score estimator. We establish the asymptotic distribution of the average treatment effect estimator. Monte Carlo simulation results show that the proposed method works well in finite samples and outperforms the conventional nonparametric kernel approach. We apply the proposed method to an empirical data examining the efficacy of right heart catheterization on medical outcomes.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"40 1","pages":"852 - 866"},"PeriodicalIF":1.2,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474938.2021.1889206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43268956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-09DOI: 10.1080/07474938.2020.1862505
László Balázsi, F. Chan, L. Mátyás
Abstract This paper proposes a new estimation procedure called Event Count Estimator (ECE). The estimator is straightforward to implement and is robust against outliers, censoring and ‘excess zeros’ in the data. The paper establishes asymptotic properties of the new estimator and the theoretical results are supported by several Monte Carlo experiments. Monte Carlo experiments also show that the estimator has reasonable properties in moderate to large samples. As such, the cost of trading efficiency for robustness here is negligible from an applied viewpoint. The practical usefulness of the new estimator is demonstrated via an empirical application of the Gravity Model of trade.
{"title":"Event count estimation","authors":"László Balázsi, F. Chan, L. Mátyás","doi":"10.1080/07474938.2020.1862505","DOIUrl":"https://doi.org/10.1080/07474938.2020.1862505","url":null,"abstract":"Abstract This paper proposes a new estimation procedure called Event Count Estimator (ECE). The estimator is straightforward to implement and is robust against outliers, censoring and ‘excess zeros’ in the data. The paper establishes asymptotic properties of the new estimator and the theoretical results are supported by several Monte Carlo experiments. Monte Carlo experiments also show that the estimator has reasonable properties in moderate to large samples. As such, the cost of trading efficiency for robustness here is negligible from an applied viewpoint. The practical usefulness of the new estimator is demonstrated via an empirical application of the Gravity Model of trade.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"147 - 176"},"PeriodicalIF":1.2,"publicationDate":"2021-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43265193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-03DOI: 10.1080/07474938.2021.1957283
D. Henderson, A. Soberón, Juan M. Rodríguez-Póo
Abstract Multidimensional panel datasets are routinely employed to identify marginal effects in empirical research. Fixed effects estimators are typically used to deal with potential correlation between unobserved effects and regressors. Nonparametric estimators for one-way fixed effects models exist, but are cumbersome to employ in practice as they typically require iteration, marginal integration or profile estimation. We develop a nonparametric estimator that works for essentially any dimension fixed effects model, has a closed form solution and can be estimated in a single step. A cross-validation bandwidth selection procedure is proposed and asymptotic properties (for either a fixed or large time dimension) are given. Finite sample properties are shown via simulations, as well as with an empirical application, which further extends our model to the partially linear setting.
{"title":"Nonparametric multidimensional fixed effects panel data models","authors":"D. Henderson, A. Soberón, Juan M. Rodríguez-Póo","doi":"10.1080/07474938.2021.1957283","DOIUrl":"https://doi.org/10.1080/07474938.2021.1957283","url":null,"abstract":"Abstract Multidimensional panel datasets are routinely employed to identify marginal effects in empirical research. Fixed effects estimators are typically used to deal with potential correlation between unobserved effects and regressors. Nonparametric estimators for one-way fixed effects models exist, but are cumbersome to employ in practice as they typically require iteration, marginal integration or profile estimation. We develop a nonparametric estimator that works for essentially any dimension fixed effects model, has a closed form solution and can be estimated in a single step. A cross-validation bandwidth selection procedure is proposed and asymptotic properties (for either a fixed or large time dimension) are given. Finite sample properties are shown via simulations, as well as with an empirical application, which further extends our model to the partially linear setting.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"321 - 358"},"PeriodicalIF":1.2,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48168929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-14DOI: 10.1080/07474938.2021.1889205
Xiaoyong Cao, Xirong Chen, Wenzheng Gao, C. Hsiao
Abstract This paper develops a two-stage semiparametric procedure to estimate the preference parameters of a binary choice model under uncertainty. In the model, the agent’s decision rule is affected by the conditional expectation. We nonparametrically estimate the conditional expectation in the first stage. Then, in the second stage, the preference parameters are estimated by the smoothed maximum score method. We establish the consistency and asymptotic distribution of the two-stage estimator. Furthermore, we also characterize the conditions under which the first-stage nonparametric estimation will not affect the asymptotic distribution of the smoothed maximum score estimator. Monte Carlo simulation results demonstrate that our proposed estimator performs well in finite samples.
{"title":"Smoothed maximum score estimation with nonparametrically generated covariates","authors":"Xiaoyong Cao, Xirong Chen, Wenzheng Gao, C. Hsiao","doi":"10.1080/07474938.2021.1889205","DOIUrl":"https://doi.org/10.1080/07474938.2021.1889205","url":null,"abstract":"Abstract This paper develops a two-stage semiparametric procedure to estimate the preference parameters of a binary choice model under uncertainty. In the model, the agent’s decision rule is affected by the conditional expectation. We nonparametrically estimate the conditional expectation in the first stage. Then, in the second stage, the preference parameters are estimated by the smoothed maximum score method. We establish the consistency and asymptotic distribution of the two-stage estimator. Furthermore, we also characterize the conditions under which the first-stage nonparametric estimation will not affect the asymptotic distribution of the smoothed maximum score estimator. Monte Carlo simulation results demonstrate that our proposed estimator performs well in finite samples.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"40 1","pages":"796 - 813"},"PeriodicalIF":1.2,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474938.2021.1889205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46075717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-14DOI: 10.1080/07474938.2021.1889179
Qiuling Hua, Zhijie Xiao, Hongtao Zhou
Abstract The right tail of the distribution of financial variables provides important information to investors and decision-makers. In this paper, we study the role of the right tail distributional information in finance. First, we propose semiparametric estimators for the right tail mean (RTM) and right tail variance (RTV). The proposed estimators use parsimonious parametric models to capture the dynamics of the data, and also allow for nonparametric flexibility in the distribution. These estimators can be estimated at the rate of root-T and are asymptotically normal. We then conduct a comparative study on the dynamics and empirical feature of the RTM and RTV in two international equity markets: The US and The Chinese stock markets. Third, we study the effect of right tail measures in the cross-sectional pricing of stock returns. Our empirical investigation indicates that the right tail information plays a significant role in explaining the cross-section pricing of stock returns. In addition, the RTV and left tail variance (LTV) have opposite impacts on asset prices. Finally, we use simulation based analysis to examine the impact of RTM on the optimal investment strategy. Our results have important implications for portfolio management in financial market.
{"title":"Right tail information and asset pricing","authors":"Qiuling Hua, Zhijie Xiao, Hongtao Zhou","doi":"10.1080/07474938.2021.1889179","DOIUrl":"https://doi.org/10.1080/07474938.2021.1889179","url":null,"abstract":"Abstract The right tail of the distribution of financial variables provides important information to investors and decision-makers. In this paper, we study the role of the right tail distributional information in finance. First, we propose semiparametric estimators for the right tail mean (RTM) and right tail variance (RTV). The proposed estimators use parsimonious parametric models to capture the dynamics of the data, and also allow for nonparametric flexibility in the distribution. These estimators can be estimated at the rate of root-T and are asymptotically normal. We then conduct a comparative study on the dynamics and empirical feature of the RTM and RTV in two international equity markets: The US and The Chinese stock markets. Third, we study the effect of right tail measures in the cross-sectional pricing of stock returns. Our empirical investigation indicates that the right tail information plays a significant role in explaining the cross-section pricing of stock returns. In addition, the RTV and left tail variance (LTV) have opposite impacts on asset prices. Finally, we use simulation based analysis to examine the impact of RTM on the optimal investment strategy. Our results have important implications for portfolio management in financial market.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"40 1","pages":"728 - 749"},"PeriodicalIF":1.2,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474938.2021.1889179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41482335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-16DOI: 10.1080/07474938.2021.1960752
K. Kim, Suyong Song
Abstract We study the identification and estimation of semiparametric models with mismeasured endogenous regressors using control variables that ensure the conditional covariance restriction on endogenous regressors and unobserved causes. We provide a set of sufficient conditions for identification, which control for both endogeneity and measurement error. We propose a sieve-based estimator and derive its asymptotic properties. Given the sieve approximation, our proposed estimator is easy to implement as weighted least squares. Monte Carlo simulations illustrate that our proposed estimator performs well in the finite samples. In an empirical application, we estimate the return to education on earnings using U.K. twin data, in which self-reported education is potentially measured with error and is also correlated with unobserved factors. Our approach utilizes the twin’s reported education as a control variable to obtain consistent estimates. We find that a one-year increase in education leads to an 11% increase in hourly wage. The estimate is significantly higher than those from OLS and IV approaches which are potentially biased. The application underscores that our proposed estimator is useful to correct for both endogeneity and measurement error in estimating returns to education.
{"title":"Control variables approach to estimate semiparametric models of mismeasured endogenous regressors with an application to U.K. twin data","authors":"K. Kim, Suyong Song","doi":"10.1080/07474938.2021.1960752","DOIUrl":"https://doi.org/10.1080/07474938.2021.1960752","url":null,"abstract":"Abstract We study the identification and estimation of semiparametric models with mismeasured endogenous regressors using control variables that ensure the conditional covariance restriction on endogenous regressors and unobserved causes. We provide a set of sufficient conditions for identification, which control for both endogeneity and measurement error. We propose a sieve-based estimator and derive its asymptotic properties. Given the sieve approximation, our proposed estimator is easy to implement as weighted least squares. Monte Carlo simulations illustrate that our proposed estimator performs well in the finite samples. In an empirical application, we estimate the return to education on earnings using U.K. twin data, in which self-reported education is potentially measured with error and is also correlated with unobserved factors. Our approach utilizes the twin’s reported education as a control variable to obtain consistent estimates. We find that a one-year increase in education leads to an 11% increase in hourly wage. The estimate is significantly higher than those from OLS and IV approaches which are potentially biased. The application underscores that our proposed estimator is useful to correct for both endogeneity and measurement error in estimating returns to education.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"448 - 483"},"PeriodicalIF":1.2,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44693926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-14DOI: 10.1080/07474938.2021.1957284
Kien C. Tran, M. Tsionas
Abstract An efficient sieve maximum likelihood estimation procedure for regression models with endogenous regressors using a copula-based approach is proposed. Specifically, the joint distribution of the endogenous regressor and the error term is characterized by a parametric copula function evaluated at the nonparametric marginal distributions. The asymptotic properties of the proposed estimator are derived, including semiparametrically efficient property. Monte Carlo simulations reveal that the proposed method performs well in finite samples comparing to other existing methods. An empirical application is presented to demonstrate the usefulness of the proposed approach.
{"title":"Efficient semiparametric copula estimation of regression models with endogeneity","authors":"Kien C. Tran, M. Tsionas","doi":"10.1080/07474938.2021.1957284","DOIUrl":"https://doi.org/10.1080/07474938.2021.1957284","url":null,"abstract":"Abstract An efficient sieve maximum likelihood estimation procedure for regression models with endogenous regressors using a copula-based approach is proposed. Specifically, the joint distribution of the endogenous regressor and the error term is characterized by a parametric copula function evaluated at the nonparametric marginal distributions. The asymptotic properties of the proposed estimator are derived, including semiparametrically efficient property. Monte Carlo simulations reveal that the proposed method performs well in finite samples comparing to other existing methods. An empirical application is presented to demonstrate the usefulness of the proposed approach.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"485 - 504"},"PeriodicalIF":1.2,"publicationDate":"2021-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46543133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-09DOI: 10.1080/07474938.2021.1889196
Yan Meng, Jiti Gao, Xibin Zhang, Xueyan Zhao
Abstract Inequality between private and public patients in Australia has been an ongoing concern due to its two tiered insurance system. This article investigates the variations in hospital length of stay for hip replacements using the Victorian Admitted Episodes Dataset from 2003/2004 to 2014/2015, employing a Bayesian hierarchical random coefficients model with trend. We find systematic differences in the length of stay between public and private hospitals, after observable patient complexity is controlled. This suggests shorter stays in public hospitals due to pressure from the Activity-based funding scheme, and longer stays in private system due to potential moral hazard. Our counterfactual analysis shows that public patients stay 1.8 days shorter than private patients in 2014, which leads to the “quicker but sicker” concern that is commonly voiced by the public. We also identify widespread variations among individual hospitals. Sources for such variation warrant closer investigation by policy makers.
{"title":"A panel data model of length of stay in hospitals for hip replacements","authors":"Yan Meng, Jiti Gao, Xibin Zhang, Xueyan Zhao","doi":"10.1080/07474938.2021.1889196","DOIUrl":"https://doi.org/10.1080/07474938.2021.1889196","url":null,"abstract":"Abstract Inequality between private and public patients in Australia has been an ongoing concern due to its two tiered insurance system. This article investigates the variations in hospital length of stay for hip replacements using the Victorian Admitted Episodes Dataset from 2003/2004 to 2014/2015, employing a Bayesian hierarchical random coefficients model with trend. We find systematic differences in the length of stay between public and private hospitals, after observable patient complexity is controlled. This suggests shorter stays in public hospitals due to pressure from the Activity-based funding scheme, and longer stays in private system due to potential moral hazard. Our counterfactual analysis shows that public patients stay 1.8 days shorter than private patients in 2014, which leads to the “quicker but sicker” concern that is commonly voiced by the public. We also identify widespread variations among individual hospitals. Sources for such variation warrant closer investigation by policy makers.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"40 1","pages":"688 - 707"},"PeriodicalIF":1.2,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474938.2021.1889196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49311068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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