Pub Date : 2024-01-08DOI: 10.1080/07474938.2023.2292383
Ignace De Vos, Gerdie Everaert, Vasilis Sarafidis
We develop a binary classifier to evaluate whether the rank condition (RC) is satisfied or not for the Common Correlated Effects (CCE) estimator. The RC postulates that the number of unobserved fac...
{"title":"A method to evaluate the rank condition for CCE estimators","authors":"Ignace De Vos, Gerdie Everaert, Vasilis Sarafidis","doi":"10.1080/07474938.2023.2292383","DOIUrl":"https://doi.org/10.1080/07474938.2023.2292383","url":null,"abstract":"We develop a binary classifier to evaluate whether the rank condition (RC) is satisfied or not for the Common Correlated Effects (CCE) estimator. The RC postulates that the number of unobserved fac...","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"164 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139397026","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 : 2024-01-03DOI: 10.1080/07474938.2023.2292377
Dalei Yu, Heng Lian, Yuying Sun, Xinyu Zhang, Yongmiao Hong
This article considers the problem of post-averaging inference for optimal model averaging estimators in a generalized linear model (GLM). We establish the asymptotic distributions of optimal model...
{"title":"Post-averaging inference for optimal model averaging estimator in generalized linear models","authors":"Dalei Yu, Heng Lian, Yuying Sun, Xinyu Zhang, Yongmiao Hong","doi":"10.1080/07474938.2023.2292377","DOIUrl":"https://doi.org/10.1080/07474938.2023.2292377","url":null,"abstract":"This article considers the problem of post-averaging inference for optimal model averaging estimators in a generalized linear model (GLM). We establish the asymptotic distributions of optimal model...","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"91 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139375200","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 : 2023-11-29DOI: 10.1080/07474938.2023.2280825
Chaoxia Yuan, Fang Fang, Jialiang Li
While plenty of frequentist model averaging methods have been proposed, existing weight selection criteria for generalized linear models (GLM) are usually based on a model size penalized Kullback-L...
{"title":"Model averaging for generalized linear models in diverging model spaces with effective model size","authors":"Chaoxia Yuan, Fang Fang, Jialiang Li","doi":"10.1080/07474938.2023.2280825","DOIUrl":"https://doi.org/10.1080/07474938.2023.2280825","url":null,"abstract":"While plenty of frequentist model averaging methods have been proposed, existing weight selection criteria for generalized linear models (GLM) are usually based on a model size penalized Kullback-L...","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"52 ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138515158","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 : 2023-10-11DOI: 10.1080/07474938.2023.2255438
Giampiero Marra, Rosalba Radice, David Zimmer
Motivated by three health economics-related case studies, we propose a unifying and flexible regression modeling framework that involves regime switching. The proposal can handle the peculiar distributional shapes of the considered outcomes via a vast range of marginal distributions, allows for a wide variety of copula dependence structures and permits to specify all model parameters (including the dependence parameters) as flexible functions of covariate effects. The algorithm is based on a computationally efficient and stable penalized maximum likelihood estimation approach. The proposed modeling framework is employed in three applications in health economics, that use data from the Medical Expenditure Panel Survey, where novel patterns are uncovered. The framework has been incorporated in the R package GJRM, hence allowing users to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries.
{"title":"A unifying switching regime regression framework with applications in health economics","authors":"Giampiero Marra, Rosalba Radice, David Zimmer","doi":"10.1080/07474938.2023.2255438","DOIUrl":"https://doi.org/10.1080/07474938.2023.2255438","url":null,"abstract":"Motivated by three health economics-related case studies, we propose a unifying and flexible regression modeling framework that involves regime switching. The proposal can handle the peculiar distributional shapes of the considered outcomes via a vast range of marginal distributions, allows for a wide variety of copula dependence structures and permits to specify all model parameters (including the dependence parameters) as flexible functions of covariate effects. The algorithm is based on a computationally efficient and stable penalized maximum likelihood estimation approach. The proposed modeling framework is employed in three applications in health economics, that use data from the Medical Expenditure Panel Survey, where novel patterns are uncovered. The framework has been incorporated in the R package GJRM, hence allowing users to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211194","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 : 2023-09-05DOI: 10.1080/07474938.2023.2246823
T. Bouezmarni, Mohamed Doukali, A. Taamouti
This paper aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup-Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are de(cid:133)ne as least squares analogue of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a su¢ cient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good (cid:133)nite-sample size and power properties for a variety of data-generating processes and di⁄erent sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests. ABSTRACT This paper aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup-Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are de(cid:133)ne as least squares analogue of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a su¢ cient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good (cid:133)nite-sample size and power properties for a variety of data-generating processes and di⁄erent sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests.
{"title":"Testing Granger non-causality in expectiles","authors":"T. Bouezmarni, Mohamed Doukali, A. Taamouti","doi":"10.1080/07474938.2023.2246823","DOIUrl":"https://doi.org/10.1080/07474938.2023.2246823","url":null,"abstract":"This paper aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup-Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are de(cid:133)ne as least squares analogue of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a su¢ cient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good (cid:133)nite-sample size and power properties for a variety of data-generating processes and di⁄erent sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests. ABSTRACT This paper aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup-Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are de(cid:133)ne as least squares analogue of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a su¢ cient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good (cid:133)nite-sample size and power properties for a variety of data-generating processes and di⁄erent sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48645294","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 : 2023-08-20DOI: 10.1080/07474938.2023.2241223
Panayiotis C. Andreou, Sofia Anyfantaki, Esfandiar Maasoumi, Carlo Sala
AbstractWe employ extreme value theory to identify stock price crashes, featuring low-probability events that produce large, idiosyncratic negative outliers in the conditional distribution. Traditional methods employ approximations under Gaussian assumptions and central moments. This is inherently imprecise and susceptible to misspecifications, especially for tail events. We instead propose new definitions and measures for crash risk based on conditional extremal quantiles (CEQ) of idiosyncratic stock returns. CEQ provide information on quantile-specific impact of covariates, and shed light on prior empirical puzzles and shortcomings in identifying crashes. Additionally, to capture the magnitude of crashes, we provide an expected shortfall analysis of the losses due to crash. Our findings have important implications for a burgeoning literature in financial economics that relies on traditional approximations.KEYWORDS: Extremal quantilesextreme value theoryquantile regressionstock price crashesJEL Classification: C14D81G11G12G32 AcknowledgmentsThe views expressed in this article are those of the authors and not necessarily reflect those of the Bank of Greece or the Eurosystem.Notes1 Some notable examples, inter alia, are: Chen et al. (Citation2001); Jin and Myers (Citation2006); Hutton et al. (Citation2009); Kim et al. (Citation2011); Callen and Fang (Citation2015); Andreou et al. (Citation2016); Kim et al. (Citation2016); Andreou et al. (Citation2017); Chang et al. (Citation2017); Ertugrul et al. (Citation2017); Cheng et al. (Citation2020); Li and Zeng (Citation2019).2 The nonclustering condition is of the Meyer (Citation1973) type and states that the probability of two extreme events co-occurring at nearby dates is much lower than the probability of just one extreme event. This assumption is convenient because it leads to limit distributions of extremal quantile regression estimators as if independent sampling had taken place. The plausibility of the nonclustering assumption is an empirical matter.3 Due to data limitation issues, we cannot perform our analysis on a per-firm basis. However, we performed also the analysis by (a) pooling per-year-industry and (b) pooling data per-year and then take the average over all years. We find that our findings are not sensitive to the way we split the data. All robustness checks are available upon request.4 Excess return is typically computed as deviation from a given risk free return. Here, idiosyncratic weekly return is computed as deviation from a statistically determined, stable, weekly market, and industry return. An interpretation is that we are removing a linear projection expected value of market and/or industry returns. This is a partialling out of returns that accounts for the expected value of market and common industry factors, before a quantile regression is conducted on other conditioning covariates. An alternative approach would be a single step estimation of quantiles, controlling for quantil
非聚类条件属于Meyer (Citation1973)类型，并指出两个极端事件在附近日期同时发生的概率远低于一个极端事件的概率。例如，它假设一场大的市场崩盘不太可能立即引发另一场大崩盘。Ramon lull大学esade商学院，Avenida de Torreblanca 59,08172，圣库加特，巴塞罗那，西班牙;电子邮件:carlo.sala@esade.edu。感谢AGAUR - SGR 2017-640基金和西班牙科学与创新部- PID2019-1064656GBI00/AEI/10.13039/501100011033的资金支持。
{"title":"Extremal quantiles and stock price crashes","authors":"Panayiotis C. Andreou, Sofia Anyfantaki, Esfandiar Maasoumi, Carlo Sala","doi":"10.1080/07474938.2023.2241223","DOIUrl":"https://doi.org/10.1080/07474938.2023.2241223","url":null,"abstract":"AbstractWe employ extreme value theory to identify stock price crashes, featuring low-probability events that produce large, idiosyncratic negative outliers in the conditional distribution. Traditional methods employ approximations under Gaussian assumptions and central moments. This is inherently imprecise and susceptible to misspecifications, especially for tail events. We instead propose new definitions and measures for crash risk based on conditional extremal quantiles (CEQ) of idiosyncratic stock returns. CEQ provide information on quantile-specific impact of covariates, and shed light on prior empirical puzzles and shortcomings in identifying crashes. Additionally, to capture the magnitude of crashes, we provide an expected shortfall analysis of the losses due to crash. Our findings have important implications for a burgeoning literature in financial economics that relies on traditional approximations.KEYWORDS: Extremal quantilesextreme value theoryquantile regressionstock price crashesJEL Classification: C14D81G11G12G32 AcknowledgmentsThe views expressed in this article are those of the authors and not necessarily reflect those of the Bank of Greece or the Eurosystem.Notes1 Some notable examples, inter alia, are: Chen et al. (Citation2001); Jin and Myers (Citation2006); Hutton et al. (Citation2009); Kim et al. (Citation2011); Callen and Fang (Citation2015); Andreou et al. (Citation2016); Kim et al. (Citation2016); Andreou et al. (Citation2017); Chang et al. (Citation2017); Ertugrul et al. (Citation2017); Cheng et al. (Citation2020); Li and Zeng (Citation2019).2 The nonclustering condition is of the Meyer (Citation1973) type and states that the probability of two extreme events co-occurring at nearby dates is much lower than the probability of just one extreme event. This assumption is convenient because it leads to limit distributions of extremal quantile regression estimators as if independent sampling had taken place. The plausibility of the nonclustering assumption is an empirical matter.3 Due to data limitation issues, we cannot perform our analysis on a per-firm basis. However, we performed also the analysis by (a) pooling per-year-industry and (b) pooling data per-year and then take the average over all years. We find that our findings are not sensitive to the way we split the data. All robustness checks are available upon request.4 Excess return is typically computed as deviation from a given risk free return. Here, idiosyncratic weekly return is computed as deviation from a statistically determined, stable, weekly market, and industry return. An interpretation is that we are removing a linear projection expected value of market and/or industry returns. This is a partialling out of returns that accounts for the expected value of market and common industry factors, before a quantile regression is conducted on other conditioning covariates. An alternative approach would be a single step estimation of quantiles, controlling for quantil","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135876785","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 : 2023-08-17DOI: 10.1080/07474938.2023.2243696
E. Maasoumi, Robert J. Taylor
Our dear friend and world-leading econometrician, Professor Michael John McAleer, passed away on 8 July 2021 after a long and graceful fight with cancer. Mike’s father was Irish and his mother Japanese. His formative years were spent in Japan, with fluency in Japanese and a lifelong affinity with Asian cultures. His grace and good humor during his battle with cancer is an example to all, and a true model of resilience and the power of a positive mental attitude. Mike continued to be a highly active researcher right up until his untimely death. Mike obtained his PhD from Queens University, Canada. Mike spent most of his professional career in Australia, including appointments at the Australian National University and the University of Western Australia. He also held distinguished positions at a number of higher educational institutions spanning several continents. Mike was a passionate debater and thought deeply and argued effectively on the pros and cons of various econometric methods and approaches. His research interests ranged widely in econometrics, financial econometrics, finance, energy economics, economics of patents, bibliometrics, tourism, and lastly COVID-19-related research. Mike was generous and easy to work with and was given to respect and kindness toward his collaborators and students. Michael McAleer is one of the most published econometricians in the world, in a record of scholarly collaboration that is unique in its breadth and width, involving many coauthors, especially younger scholars. In particular, Mike coauthored 415 publications, with 6786 citations, as indicated on Publons, and 1270 referenced pieces on Google Scholar, with 23,273 citations. He was ranked 62 on REPEC for work in economics over a recent 10 year period, 46 in econometrics globally on Google Scholar, and 8 in Financial Econometrics. Mike was also an outstanding Associate Editor of Econometric Reviews, with one of the longest years of service for the journal since the late 1980s. He was the Editor-in-Chief of six international journals and was a member of the editorial boards of forty international journals. Among others, Mike edited and coedited numerous special issues of the Journal of Econometrics, providing timely state of art collections of contributions to the latest topics, some under-covered were it not for his tireless efforts. He contributed to the launching of several journals and showed special sensitivity to the needs of younger scholars. Mike was also a superb host and a great friend, always generous and graceful. He is sorely missed by all those of us who were privileged to call him a friend. This special issue of Econometric Reviews is dedicated to Mike’s memory and honors his contributions as scholar, author, teacher, mentor, and editor. We now provide a short summary of each of the papers (in alphabetical order of the first author) that comprise this special issue, Vol. 42; 9-10. Each was anonymously reviewed in accordance with the usual st
{"title":"In memory of Michael McAleer: special issue of Econometric Reviews","authors":"E. Maasoumi, Robert J. Taylor","doi":"10.1080/07474938.2023.2243696","DOIUrl":"https://doi.org/10.1080/07474938.2023.2243696","url":null,"abstract":"Our dear friend and world-leading econometrician, Professor Michael John McAleer, passed away on 8 July 2021 after a long and graceful fight with cancer. Mike’s father was Irish and his mother Japanese. His formative years were spent in Japan, with fluency in Japanese and a lifelong affinity with Asian cultures. His grace and good humor during his battle with cancer is an example to all, and a true model of resilience and the power of a positive mental attitude. Mike continued to be a highly active researcher right up until his untimely death. Mike obtained his PhD from Queens University, Canada. Mike spent most of his professional career in Australia, including appointments at the Australian National University and the University of Western Australia. He also held distinguished positions at a number of higher educational institutions spanning several continents. Mike was a passionate debater and thought deeply and argued effectively on the pros and cons of various econometric methods and approaches. His research interests ranged widely in econometrics, financial econometrics, finance, energy economics, economics of patents, bibliometrics, tourism, and lastly COVID-19-related research. Mike was generous and easy to work with and was given to respect and kindness toward his collaborators and students. Michael McAleer is one of the most published econometricians in the world, in a record of scholarly collaboration that is unique in its breadth and width, involving many coauthors, especially younger scholars. In particular, Mike coauthored 415 publications, with 6786 citations, as indicated on Publons, and 1270 referenced pieces on Google Scholar, with 23,273 citations. He was ranked 62 on REPEC for work in economics over a recent 10 year period, 46 in econometrics globally on Google Scholar, and 8 in Financial Econometrics. Mike was also an outstanding Associate Editor of Econometric Reviews, with one of the longest years of service for the journal since the late 1980s. He was the Editor-in-Chief of six international journals and was a member of the editorial boards of forty international journals. Among others, Mike edited and coedited numerous special issues of the Journal of Econometrics, providing timely state of art collections of contributions to the latest topics, some under-covered were it not for his tireless efforts. He contributed to the launching of several journals and showed special sensitivity to the needs of younger scholars. Mike was also a superb host and a great friend, always generous and graceful. He is sorely missed by all those of us who were privileged to call him a friend. This special issue of Econometric Reviews is dedicated to Mike’s memory and honors his contributions as scholar, author, teacher, mentor, and editor. We now provide a short summary of each of the papers (in alphabetical order of the first author) that comprise this special issue, Vol. 42; 9-10. Each was anonymously reviewed in accordance with the usual st","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"700 - 702"},"PeriodicalIF":1.2,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45255394","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 : 2023-08-04DOI: 10.1080/07474938.2023.2237274
Zhongfang He
AbstractThis article studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time-dependent conditional variances. This TVP model is flexible to accommodate a wide variety of time variation patterns but requires effective shrinkage on the state variances to avoid over-fitting. A Bayesian shrinkage prior is proposed based on reparameterization that translates the variance shrinkage problem into a variable shrinkage one in a conditionally linear regression with fixed coefficients. The proposed prior allows strong shrinkage for the state variances while maintaining the flexibility to accommodate local signals. A Bayesian estimation method is developed that employs the ancilarity-sufficiency interweaving strategy to boost sampling efficiency. Simulation study and an empirical application to forecast inflation rate illustrate the benefits of the proposed approach.KEYWORDS: ASISBayesian shrinkagehorseshoeMCMCTVPJEL Classification: C01C11C22E37 AcknowledgmentsI would like to thank Professor Esfandiar Maasoumi (the editor), an AE and two referees for many invaluable comments that have greatly improved the article. All remaining errors are my own. The views in this article are solely the author’s responsibility and are not related to the company the author works in. The author reports that there are no competing interests to declare.Notes1 Other recent examples of shrinkage TVP models with homoskedastic latent states include Cadonna et al. (Citation2020), Chan et al. (Citation2020) etc.2 Another strand of the literature allows heteroskedastic latent states by applying time-dependent spike-and-slab mixture priors for state variances (e.g. Giordani and Kohn (Citation2008), Chan et al. (Citation2012), Hauzenberger (Citation2021), Rockova and McAlinn (Citation2021)) but faces the computational hurdle due to the combinatorial complexity of sampling the mixture indicators of the spike-and-slab priors.3 See Hauzenberger et al. (Citation2020) for similar strategies for versions of TVP models where latent states follow independent Gaussian distributions rather than random walks.4 To see this, let βt* = βt - β0. The TVP model can be rewritten as yt = xt′β0 + xt′βt* + ϵt, βt* = βt−1* + ηt and β0* = 0.5 Alternative shrinkage priors for linear regressions include the spike-and-slab one (George and McCulloch (Citation1993), Ishwaran and Rao (Citation2005)) and the normal-gamma one (Griffin and Brown (Citation2010)) etc. A comprehensive comparison of the various shrinkage priors in the current TVP context is left for future research.6 The density of an inverted beta distribution IB(a, b) is p(x)=xa−1(1+x)−a−bB(a,b)I{x>0} where B(·,·) is the beta function and a and b are positive real numbers. If x∼IB(0.5,0.5), then x∼C+(0,1) and vice versa, where C+(0,1) is a standard half-Cauchy distribution with the density p(z)=2π(1+z2)I{z>0}.7 If ±x∼N(0,a), then x∼G(0.5,2a) and vice versa, where the gamma dist
{"title":"Time-dependent shrinkage of time-varying parameter regression models","authors":"Zhongfang He","doi":"10.1080/07474938.2023.2237274","DOIUrl":"https://doi.org/10.1080/07474938.2023.2237274","url":null,"abstract":"AbstractThis article studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time-dependent conditional variances. This TVP model is flexible to accommodate a wide variety of time variation patterns but requires effective shrinkage on the state variances to avoid over-fitting. A Bayesian shrinkage prior is proposed based on reparameterization that translates the variance shrinkage problem into a variable shrinkage one in a conditionally linear regression with fixed coefficients. The proposed prior allows strong shrinkage for the state variances while maintaining the flexibility to accommodate local signals. A Bayesian estimation method is developed that employs the ancilarity-sufficiency interweaving strategy to boost sampling efficiency. Simulation study and an empirical application to forecast inflation rate illustrate the benefits of the proposed approach.KEYWORDS: ASISBayesian shrinkagehorseshoeMCMCTVPJEL Classification: C01C11C22E37 AcknowledgmentsI would like to thank Professor Esfandiar Maasoumi (the editor), an AE and two referees for many invaluable comments that have greatly improved the article. All remaining errors are my own. The views in this article are solely the author’s responsibility and are not related to the company the author works in. The author reports that there are no competing interests to declare.Notes1 Other recent examples of shrinkage TVP models with homoskedastic latent states include Cadonna et al. (Citation2020), Chan et al. (Citation2020) etc.2 Another strand of the literature allows heteroskedastic latent states by applying time-dependent spike-and-slab mixture priors for state variances (e.g. Giordani and Kohn (Citation2008), Chan et al. (Citation2012), Hauzenberger (Citation2021), Rockova and McAlinn (Citation2021)) but faces the computational hurdle due to the combinatorial complexity of sampling the mixture indicators of the spike-and-slab priors.3 See Hauzenberger et al. (Citation2020) for similar strategies for versions of TVP models where latent states follow independent Gaussian distributions rather than random walks.4 To see this, let βt* = βt - β0. The TVP model can be rewritten as yt = xt′β0 + xt′βt* + ϵt, βt* = βt−1* + ηt and β0* = 0.5 Alternative shrinkage priors for linear regressions include the spike-and-slab one (George and McCulloch (Citation1993), Ishwaran and Rao (Citation2005)) and the normal-gamma one (Griffin and Brown (Citation2010)) etc. A comprehensive comparison of the various shrinkage priors in the current TVP context is left for future research.6 The density of an inverted beta distribution IB(a, b) is p(x)=xa−1(1+x)−a−bB(a,b)I{x>0} where B(·,·) is the beta function and a and b are positive real numbers. If x∼IB(0.5,0.5), then x∼C+(0,1) and vice versa, where C+(0,1) is a standard half-Cauchy distribution with the density p(z)=2π(1+z2)I{z>0}.7 If ±x∼N(0,a), then x∼G(0.5,2a) and vice versa, where the gamma dist","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"2 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":"136143503","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 : 2023-07-31DOI: 10.1080/07474938.2023.2227019
Feifei Guo, S. Ling
Abstract This article studies the first-order vector error correction (VEC(1)) model when its noise is a linear process of independent and identically distributed (i.i.d.) heavy-tailed random vectors with a tail index . We show that the rate of convergence of the least squares estimator (LSE) related to the long-run parameters is n (sample size) and its limiting distribution is a stochastic integral in terms of two stable random processes, while the LSE related to the short-term parameters is not consistent. We further propose an automated approach via adaptive shrinkage techniques to determine the cointegrating rank in the VEC(1) model. It is demonstrated that the cointegration rank r 0 can be consistently selected despite the fact that the LSE related to the short-term parameters is not consistently estimable when the tail index . Simulation studies are carried out to evaluate the performance of the proposed procedure in finite samples. Last, we use our techniques to explore the long-run and short-run behavior of the monthly prices of wheat, corn, and wheat flour in the United States.
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Pub Date : 2023-07-14DOI: 10.1080/07474938.2023.2222634
David I. Harvey, S. Leybourne, A. Taylor
Abstract– Predictive regression methods are widely used to examine the predictability of (excess) stock returns by lagged financial variables characterized by unknown degrees of persistence and endogeneity. We develop a new hybrid test for predictability in these circumstances based on simple regression t-statistics. Where the predictor is endogenous, the optimal, but infeasible, test for predictability is based on the t-statistic on the lagged predictor in the basic predictive regression augmented with the current period innovation driving the predictor. We propose a feasible version of this augmented test, designed for the case where the predictor is an endogenous near-unit root process, using a GLS-based estimate of the innovation used in the infeasible test regression. The limiting null distribution of this statistic depends on both the endogeneity correlation parameter and the local-to-unity parameter characterizing the predictor. A method for obtaining asymptotic critical values is discussed and response surfaces are provided. We compare the asymptotic power properties of the feasible augmented test with those of a (non augmented) t-test recently considered in Harvey et al. and show that the augmented test is more powerful in the strongly persistent predictor case. We then propose using a weighted combination of the augmented statistic and the t-statistic of Harvey et al., where the weights are obtained using the p-values from a unit root test on the predictor. We find this can further improve asymptotic power in cases where the predictor has persistence at or close to that of a unit root process. Our final hybrid testing procedure then embeds the weighted statistic within a switching-based procedure which makes use of a standard predictive regression t-test, compared with standard normal critical values, when there is evidence for the predictor being weakly persistent. Monte Carlo simulations suggest that overall our new hybrid test displays superior finite sample performance to comparable extant tests.
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