Pub Date : 2022-05-30DOI: 10.1080/07474938.2022.2074188
Wen Xu
Abstract This paper proposes a test for structural changes in factor loadings in high-dimensional factor models under weak serial and cross-sectional dependence. The test is an aggregate statistic in the form of the maximum of the variable-specific statistics whose asymptotic null distribution and local power property are studied. Two approaches including extreme value theory and Bonferroni correction are adopted to compute the critical values of the aggregate test statistic. Monte Carlo simulations reveal the non-trivial power of the proposed test against various types of structural changes, including abrupt changes, nonrandom smooth changes, random-walk variations and stationary variations. Additionally, our test can be more powerful than some alternative tests in the considered scenarios. The usefulness of the test is illustrated by an empirical application to Stock and Watson’s U.S. data set.
{"title":"Testing for time-varying factor loadings in high-dimensional factor models","authors":"Wen Xu","doi":"10.1080/07474938.2022.2074188","DOIUrl":"https://doi.org/10.1080/07474938.2022.2074188","url":null,"abstract":"Abstract This paper proposes a test for structural changes in factor loadings in high-dimensional factor models under weak serial and cross-sectional dependence. The test is an aggregate statistic in the form of the maximum of the variable-specific statistics whose asymptotic null distribution and local power property are studied. Two approaches including extreme value theory and Bonferroni correction are adopted to compute the critical values of the aggregate test statistic. Monte Carlo simulations reveal the non-trivial power of the proposed test against various types of structural changes, including abrupt changes, nonrandom smooth changes, random-walk variations and stationary variations. Additionally, our test can be more powerful than some alternative tests in the considered scenarios. The usefulness of the test is illustrated by an empirical application to Stock and Watson’s U.S. data set.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"918 - 965"},"PeriodicalIF":1.2,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44874062","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 : 2022-05-28DOI: 10.1080/07474938.2022.2073743
B. Brune, W. Scherrer, E. Bura
Abstract We propose a new approach to reduced-rank regression that allows for time-variation in the regression coefficients. The Kalman filter based estimation allows for usage of standard methods and easy implementation of our procedure. The EM-algorithm ensures convergence to a local maximum of the likelihood. Our estimation approach in time-varying reduced-rank regression performs well in simulations, with amplified competitive advantage in time series that experience large structural changes. We illustrate the performance of our approach with a simulation study and two applications to stock index and Covid-19 case data.
{"title":"A state-space approach to time-varying reduced-rank regression","authors":"B. Brune, W. Scherrer, E. Bura","doi":"10.1080/07474938.2022.2073743","DOIUrl":"https://doi.org/10.1080/07474938.2022.2073743","url":null,"abstract":"Abstract We propose a new approach to reduced-rank regression that allows for time-variation in the regression coefficients. The Kalman filter based estimation allows for usage of standard methods and easy implementation of our procedure. The EM-algorithm ensures convergence to a local maximum of the likelihood. Our estimation approach in time-varying reduced-rank regression performs well in simulations, with amplified competitive advantage in time series that experience large structural changes. We illustrate the performance of our approach with a simulation study and two applications to stock index and Covid-19 case data.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"895 - 917"},"PeriodicalIF":1.2,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43783569","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 : 2022-05-14DOI: 10.1080/07474938.2022.2039494
Ye Yang
Abstract In this paper, a unified M-estimation method in Yang (2018) is extended to the matrix exponential spatial dynamic panel specification (MESDPS) with fixed effects in short panels. Similar to the STLE model which includes the spatial lag effect, the space-time effect and the spatial error effect in Yang (2018), the quasi-maximum likelihood (QML) estimation for MESDPS also has the initial condition specification problem. The initial-condition free M-estimator in this paper solves this problem and is proved to be consistent and asymptotically normal. An outer product of martingale difference (OPMD) estimator for the variance-covariance (VC) matrix of the M-estimator is also derived and proved to be consistent. The finite sample property of the M-estimator is studied through an extensive Monte Carlo study. The method is applied to US outward FDI data to show its validity.
{"title":"Unified M-estimation of matrix exponential spatial dynamic panel specification","authors":"Ye Yang","doi":"10.1080/07474938.2022.2039494","DOIUrl":"https://doi.org/10.1080/07474938.2022.2039494","url":null,"abstract":"Abstract In this paper, a unified M-estimation method in Yang (2018) is extended to the matrix exponential spatial dynamic panel specification (MESDPS) with fixed effects in short panels. Similar to the STLE model which includes the spatial lag effect, the space-time effect and the spatial error effect in Yang (2018), the quasi-maximum likelihood (QML) estimation for MESDPS also has the initial condition specification problem. The initial-condition free M-estimator in this paper solves this problem and is proved to be consistent and asymptotically normal. An outer product of martingale difference (OPMD) estimator for the variance-covariance (VC) matrix of the M-estimator is also derived and proved to be consistent. The finite sample property of the M-estimator is studied through an extensive Monte Carlo study. The method is applied to US outward FDI data to show its validity.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"729 - 748"},"PeriodicalIF":1.2,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59415025","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 : 2022-05-13DOI: 10.1080/07474938.2022.2072321
Chuhui Li, D. Poskitt, F. Windmeijer, Xueyan Zhao
Abstract For a binary outcome Y, generated by a simple threshold crossing model with a single exogenous normally distributed explanatory variable X, the OLS estimator of the coefficient on X in a linear probability model is a consistent estimator of the average partial effect of X. Even in this very simple setting, we show that when allowing for X to be endogenously determined, the 2SLS estimator, using a normally distributed instrumental variable Z, does not identify the same causal parameter. It instead estimates the average partial effect of Z, scaled by the coefficient on Z in the linear first-stage model for X, denoted γ 1, or equivalently, it estimates the average partial effect of the population predicted value of X, These causal parameters can differ substantially as we show for the normal Probit model, which implies that care has to be taken when interpreting 2SLS estimation results in a linear probability model. Under joint normality of the error terms, IV Probit maximum likelihood estimation does identify the average partial effect of X. The two-step control function procedure of Rivers and Vuong can also estimate this causal parameter consistently, but a double averaging is needed, one over the distribution of the first-stage error V and one over the distribution of X. If instead a single averaging is performed over the joint distribution of X and V, then the same causal parameter is estimated as the one estimated by the 2SLS estimator in the linear probability model. The 2SLS estimator is a consistent estimator when the average partial effect is equal to 0, and the standard Wald test for this hypothesis has correct size under strong instrument asymptotics. We show that, in general, the standard weak instrument first-stage F-test interpretations do not apply in this setting.
{"title":"Binary outcomes, OLS, 2SLS and IV probit","authors":"Chuhui Li, D. Poskitt, F. Windmeijer, Xueyan Zhao","doi":"10.1080/07474938.2022.2072321","DOIUrl":"https://doi.org/10.1080/07474938.2022.2072321","url":null,"abstract":"Abstract For a binary outcome Y, generated by a simple threshold crossing model with a single exogenous normally distributed explanatory variable X, the OLS estimator of the coefficient on X in a linear probability model is a consistent estimator of the average partial effect of X. Even in this very simple setting, we show that when allowing for X to be endogenously determined, the 2SLS estimator, using a normally distributed instrumental variable Z, does not identify the same causal parameter. It instead estimates the average partial effect of Z, scaled by the coefficient on Z in the linear first-stage model for X, denoted γ 1, or equivalently, it estimates the average partial effect of the population predicted value of X, These causal parameters can differ substantially as we show for the normal Probit model, which implies that care has to be taken when interpreting 2SLS estimation results in a linear probability model. Under joint normality of the error terms, IV Probit maximum likelihood estimation does identify the average partial effect of X. The two-step control function procedure of Rivers and Vuong can also estimate this causal parameter consistently, but a double averaging is needed, one over the distribution of the first-stage error V and one over the distribution of X. If instead a single averaging is performed over the joint distribution of X and V, then the same causal parameter is estimated as the one estimated by the 2SLS estimator in the linear probability model. The 2SLS estimator is a consistent estimator when the average partial effect is equal to 0, and the standard Wald test for this hypothesis has correct size under strong instrument asymptotics. We show that, in general, the standard weak instrument first-stage F-test interpretations do not apply in this setting.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"859 - 876"},"PeriodicalIF":1.2,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49510563","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 : 2022-05-13DOI: 10.1080/07474938.2022.2072319
Aubrey Poon, Dan Zhu
Abstract We develop a flexible Bayesian time-varying parameter model with a Leamer correction to measure contagion and interdependence. Our proposed framework facilitates a model-based identification mechanism for static and dynamic interdependence. We also allow for fat-tails stochastic volatility within the model, which enables us to capture volatility clustering and outliers in high-frequency financial data. We apply our new proposed framework to two empirical applications: the Chilean foreign exchange market during the Argentine crisis of 2001 and the recent Covid-19 pandemic in the United Kingdom. We find no evidence of contagion effects from Argentina or Brazil to Chile and three additional key insights compared to Ciccarelli and Rebucci 2006 study. For the Covid-19 pandemic application, our results convey that the United Kingdom government was largely ineffective in preventing the importation of Covid-19 cases from European countries during the second wave of the pandemic.
{"title":"A new Bayesian model for contagion and interdependence","authors":"Aubrey Poon, Dan Zhu","doi":"10.1080/07474938.2022.2072319","DOIUrl":"https://doi.org/10.1080/07474938.2022.2072319","url":null,"abstract":"Abstract We develop a flexible Bayesian time-varying parameter model with a Leamer correction to measure contagion and interdependence. Our proposed framework facilitates a model-based identification mechanism for static and dynamic interdependence. We also allow for fat-tails stochastic volatility within the model, which enables us to capture volatility clustering and outliers in high-frequency financial data. We apply our new proposed framework to two empirical applications: the Chilean foreign exchange market during the Argentine crisis of 2001 and the recent Covid-19 pandemic in the United Kingdom. We find no evidence of contagion effects from Argentina or Brazil to Chile and three additional key insights compared to Ciccarelli and Rebucci 2006 study. For the Covid-19 pandemic application, our results convey that the United Kingdom government was largely ineffective in preventing the importation of Covid-19 cases from European countries during the second wave of the pandemic.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"806 - 826"},"PeriodicalIF":1.2,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43616625","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 : 2022-04-18DOI: 10.1080/07474938.2022.2039493
Shuo Li, Liuhua Peng, Y. Tu
Abstract Although the exogeneity condition is usually used in many econometric models to identify parameters, the stronger restriction that the error term is independent of a vector of exogenous variables might lead to theoretical benefits. In this paper, we develop a unified methodology for testing the independence assumption. Our methodology can deal with a wide class of parametric models and allows for endogeneity and instrumental variables. In the first-step development, we construct tests that are continuous functionals of the estimated difference of the joint distribution and the product marginal distributions. Next, to remedy the dimensionality issue that arises when the dimension of the exogenous random vector is large, we propose a multiple testing approach which combines marginal p-values obtained by employing the original tests to test independence between the error term and each exogenous variable, while taking full account of the multiplicity nature of the testing problem. We obtain null limiting distributions of our tests, establish the testing consistency, and justify the sensitivity to -local alternatives, with n the sample size. The multiplier bootstrap is employed to estimate the critical values. Our methodology is illustrated in the linear regression, the instrumental variables regression, and the nonlinear quantile regression. Our tests are found to perform well in simulations and are demonstrated via an empirical example.
{"title":"Testing independence between exogenous variables and unobserved errors","authors":"Shuo Li, Liuhua Peng, Y. Tu","doi":"10.1080/07474938.2022.2039493","DOIUrl":"https://doi.org/10.1080/07474938.2022.2039493","url":null,"abstract":"Abstract Although the exogeneity condition is usually used in many econometric models to identify parameters, the stronger restriction that the error term is independent of a vector of exogenous variables might lead to theoretical benefits. In this paper, we develop a unified methodology for testing the independence assumption. Our methodology can deal with a wide class of parametric models and allows for endogeneity and instrumental variables. In the first-step development, we construct tests that are continuous functionals of the estimated difference of the joint distribution and the product marginal distributions. Next, to remedy the dimensionality issue that arises when the dimension of the exogenous random vector is large, we propose a multiple testing approach which combines marginal p-values obtained by employing the original tests to test independence between the error term and each exogenous variable, while taking full account of the multiplicity nature of the testing problem. We obtain null limiting distributions of our tests, establish the testing consistency, and justify the sensitivity to -local alternatives, with n the sample size. The multiplier bootstrap is employed to estimate the critical values. Our methodology is illustrated in the linear regression, the instrumental variables regression, and the nonlinear quantile regression. Our tests are found to perform well in simulations and are demonstrated via an empirical example.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"697 - 728"},"PeriodicalIF":1.2,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45017714","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 : 2022-03-21DOI: 10.1080/07474938.2022.2047507
Ye Yang, Osman Doğan, Suleyman Taspinar
Abstract In this paper, we focus on a model specification problem in spatial econometric models when an empiricist needs to choose from a pool of candidates for the spatial weights matrix. We propose a model selection (MS) procedure for the matrix exponential spatial specification (MESS), when the true spatial weights matrix may not be in the set of candidate spatial weights matrices. We show that the selection estimator is asymptotically optimal in the sense that asymptotically it is as efficient as the infeasible estimator that uses the best candidate spatial weights matrix. The proposed selection procedure is also consistent in the sense that when the data generating process involves spatial effects, it chooses the true spatial weights matrix with probability approaching one in large samples. We also propose a model averaging (MA) estimator that compromises across a set of candidate models. We show that it is asymptotically optimal. We further flesh out how to extend the proposed selection and averaging schemes to higher order specifications and to the MESS with heteroscedasticity. Our Monte Carlo simulation results indicate that the MS and MA estimators perform well in finite samples. We also illustrate the usefulness of the proposed MS and MA schemes in a spatially augmented economic growth model.
{"title":"Model selection and model averaging for matrix exponential spatial models","authors":"Ye Yang, Osman Doğan, Suleyman Taspinar","doi":"10.1080/07474938.2022.2047507","DOIUrl":"https://doi.org/10.1080/07474938.2022.2047507","url":null,"abstract":"Abstract In this paper, we focus on a model specification problem in spatial econometric models when an empiricist needs to choose from a pool of candidates for the spatial weights matrix. We propose a model selection (MS) procedure for the matrix exponential spatial specification (MESS), when the true spatial weights matrix may not be in the set of candidate spatial weights matrices. We show that the selection estimator is asymptotically optimal in the sense that asymptotically it is as efficient as the infeasible estimator that uses the best candidate spatial weights matrix. The proposed selection procedure is also consistent in the sense that when the data generating process involves spatial effects, it chooses the true spatial weights matrix with probability approaching one in large samples. We also propose a model averaging (MA) estimator that compromises across a set of candidate models. We show that it is asymptotically optimal. We further flesh out how to extend the proposed selection and averaging schemes to higher order specifications and to the MESS with heteroscedasticity. Our Monte Carlo simulation results indicate that the MS and MA estimators perform well in finite samples. We also illustrate the usefulness of the proposed MS and MA schemes in a spatially augmented economic growth model.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"827 - 858"},"PeriodicalIF":1.2,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45976576","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 : 2022-03-15DOI: 10.1080/07474938.2022.2047508
Jiahui Zou, Wendung Wang, Xinyu Zhang, Guohua Zou
Abstract This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback–Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
{"title":"Optimal model averaging for divergent-dimensional Poisson regressions","authors":"Jiahui Zou, Wendung Wang, Xinyu Zhang, Guohua Zou","doi":"10.1080/07474938.2022.2047508","DOIUrl":"https://doi.org/10.1080/07474938.2022.2047508","url":null,"abstract":"Abstract This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback–Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"775 - 805"},"PeriodicalIF":1.2,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42537524","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 : 2022-02-05DOI: 10.1080/07474938.2023.2222633
H. Boswijk, Giuseppe Cavaliere, L. Angelis, A. Taylor
Abstract Standard methods, such as sequential procedures based on Johansen’s (pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank of a vector autoregressive (VAR) system of variables integrated of order one can be significantly affected, even asymptotically, by unconditional heteroskedasticity (non-stationary volatility) in the data. Known solutions to this problem include wild bootstrap implementations of the PLR test or the use of an information criterion, such as the BIC, to select the co-integration rank. Although asymptotically valid in the presence of heteroskedasticity, these methods can display very low finite sample power under some patterns of non-stationary volatility. In particular, they do not exploit potential efficiency gains that could be realized in the presence of non-stationary volatility by using adaptive inference methods. Under the assumption of a known autoregressive lag length, Boswijk and Zu develop adaptive PLR test based methods using a non-parametric estimate of the covariance matrix process. It is well-known, however, that selecting an incorrect lag length can significantly impact on the efficacy of both information criteria and bootstrap PLR tests to determine co-integration rank in finite samples. We show that adaptive information criteria-based approaches can be used to estimate the autoregressive lag order to use in connection with bootstrap adaptive PLR tests, or to jointly determine the co-integration rank and the VAR lag length and that in both cases they are weakly consistent for these parameters in the presence of non-stationary volatility provided standard conditions hold on the penalty term. Monte Carlo simulations are used to demonstrate the potential gains from using adaptive methods and an empirical application to the U.S. term structure is provided.
{"title":"Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models","authors":"H. Boswijk, Giuseppe Cavaliere, L. Angelis, A. Taylor","doi":"10.1080/07474938.2023.2222633","DOIUrl":"https://doi.org/10.1080/07474938.2023.2222633","url":null,"abstract":"Abstract Standard methods, such as sequential procedures based on Johansen’s (pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank of a vector autoregressive (VAR) system of variables integrated of order one can be significantly affected, even asymptotically, by unconditional heteroskedasticity (non-stationary volatility) in the data. Known solutions to this problem include wild bootstrap implementations of the PLR test or the use of an information criterion, such as the BIC, to select the co-integration rank. Although asymptotically valid in the presence of heteroskedasticity, these methods can display very low finite sample power under some patterns of non-stationary volatility. In particular, they do not exploit potential efficiency gains that could be realized in the presence of non-stationary volatility by using adaptive inference methods. Under the assumption of a known autoregressive lag length, Boswijk and Zu develop adaptive PLR test based methods using a non-parametric estimate of the covariance matrix process. It is well-known, however, that selecting an incorrect lag length can significantly impact on the efficacy of both information criteria and bootstrap PLR tests to determine co-integration rank in finite samples. We show that adaptive information criteria-based approaches can be used to estimate the autoregressive lag order to use in connection with bootstrap adaptive PLR tests, or to jointly determine the co-integration rank and the VAR lag length and that in both cases they are weakly consistent for these parameters in the presence of non-stationary volatility provided standard conditions hold on the penalty term. Monte Carlo simulations are used to demonstrate the potential gains from using adaptive methods and an empirical application to the U.S. term structure is provided.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"725 - 757"},"PeriodicalIF":1.2,"publicationDate":"2022-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44999231","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 : 2022-02-02DOI: 10.1080/07474938.2021.1983327
Siyang Peng, Shaojun Guo, Yonghong Long
Abstract In this paper, we propose a mixed-frequency dynamic factor model (MFDFM) taking into account the high-frequency variation and low-frequency variation at the same time. The factor loadings in our model are affected by the past quadratic variation of factor returns, while the process of the factor quadratic variation is under a mixed-frequency framework (DCC-RV). By combing the variations from the high-frequency and low-frequency domain, our approach exhibits a better estimation and forecast of the assets covariance matrix. Our empirical study compares our MFDFM model with the sample realized covariance matrix and the traditional factor model with intraday returns or daily returns. The results of the empirical study indicate that our proposed model indeed outperforms other models in the sense that the Markowitz’s portfolios based on the MFDFM have a better performance.
{"title":"Large dimensional portfolio allocation based on a mixed frequency dynamic factor model","authors":"Siyang Peng, Shaojun Guo, Yonghong Long","doi":"10.1080/07474938.2021.1983327","DOIUrl":"https://doi.org/10.1080/07474938.2021.1983327","url":null,"abstract":"Abstract In this paper, we propose a mixed-frequency dynamic factor model (MFDFM) taking into account the high-frequency variation and low-frequency variation at the same time. The factor loadings in our model are affected by the past quadratic variation of factor returns, while the process of the factor quadratic variation is under a mixed-frequency framework (DCC-RV). By combing the variations from the high-frequency and low-frequency domain, our approach exhibits a better estimation and forecast of the assets covariance matrix. Our empirical study compares our MFDFM model with the sample realized covariance matrix and the traditional factor model with intraday returns or daily returns. The results of the empirical study indicate that our proposed model indeed outperforms other models in the sense that the Markowitz’s portfolios based on the MFDFM have a better performance.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"539 - 563"},"PeriodicalIF":1.2,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48805048","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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