Pub Date : 2023-02-01DOI: 10.1080/07474938.2023.2178089
Bhavna Rai
Abstract I study the problem of missing values in the outcome and endogenous covariates in linear models. I propose an estimator that improves efficiency relative to a complete cases 2SLS. Unlike traditional imputation, my estimator is consistent even if the model contains nonlinear functions – like squares and interactions – of the endogenous covariates. It can also be used to combine data sets with missing outcome, missing endogenous covariates, and no missing variables. It includes the well-known “Two-Sample 2SLS” as a special case under weaker assumptions than the corresponding literature.
{"title":"Efficient estimation with missing data and endogeneity","authors":"Bhavna Rai","doi":"10.1080/07474938.2023.2178089","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178089","url":null,"abstract":"Abstract I study the problem of missing values in the outcome and endogenous covariates in linear models. I propose an estimator that improves efficiency relative to a complete cases 2SLS. Unlike traditional imputation, my estimator is consistent even if the model contains nonlinear functions – like squares and interactions – of the endogenous covariates. It can also be used to combine data sets with missing outcome, missing endogenous covariates, and no missing variables. It includes the well-known “Two-Sample 2SLS” as a special case under weaker assumptions than the corresponding literature.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"220 - 239"},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48507805","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 : 2023-02-01DOI: 10.1080/07474938.2023.2178086
Andrés Aradillas-López
Abstract We consider a static entry game played between an incumbent and a collection of potential entrants. Entry decisions are made with incomplete information and beliefs are conditioned, at least partially, on a market characteristic that is unobserved by the econometrician. We describe conditions under which, even though the unobserved market characteristic cannot be identified, a subset of parameters of the model can still be identified, including all the strategic-interaction effects. We also characterize testable implications for strategic behavior by the incumbent when this player is able to shift the unobserved market characteristic to deter entry. We present results under Bayesian Nash equilibrium (BNE) and under the weaker behavioral model of iterated elimination of nonrationalizable strategies. Our empirical example analyzes geographic entry decisions in the Mexican internet service provider (ISP) industry. This industry has an incumbent, América Móvil (AMX), which established a widespread geographic presence as a monopolist following the privatization of Telmex in 1990. Our results show significant strategic interaction effects between AMX and its competitors, as well as evidence of strategic behavior by AMX to deter entry and maximize its market share.
{"title":"Inference in an incomplete information entry game with an incumbent and with beliefs conditioned on unobservable market characteristics","authors":"Andrés Aradillas-López","doi":"10.1080/07474938.2023.2178086","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178086","url":null,"abstract":"Abstract We consider a static entry game played between an incumbent and a collection of potential entrants. Entry decisions are made with incomplete information and beliefs are conditioned, at least partially, on a market characteristic that is unobserved by the econometrician. We describe conditions under which, even though the unobserved market characteristic cannot be identified, a subset of parameters of the model can still be identified, including all the strategic-interaction effects. We also characterize testable implications for strategic behavior by the incumbent when this player is able to shift the unobserved market characteristic to deter entry. We present results under Bayesian Nash equilibrium (BNE) and under the weaker behavioral model of iterated elimination of nonrationalizable strategies. Our empirical example analyzes geographic entry decisions in the Mexican internet service provider (ISP) industry. This industry has an incumbent, América Móvil (AMX), which established a widespread geographic presence as a monopolist following the privatization of Telmex in 1990. Our results show significant strategic interaction effects between AMX and its competitors, as well as evidence of strategic behavior by AMX to deter entry and maximize its market share.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"123 - 156"},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46729691","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 : 2023-01-02DOI: 10.1080/07474938.2022.2140982
Martin Burda, Remi Daviet
Abstract The practical use of nonparametric Bayesian methods requires the availability of efficient algorithms for posterior inference. The inherently serial nature of traditional Markov chain Monte Carlo (MCMC) methods imposes limitations on their efficiency and scalability. In recent years, there has been a surge of research activity devoted to developing alternative implementation methods that target parallel computing environments. Sequential Monte Carlo (SMC), also known as a particle filter, has been gaining popularity due to its desirable properties. SMC uses a genetic mutation-selection sampling approach with a set of particles representing the posterior distribution of a stochastic process. We propose to enhance the performance of SMC by utilizing Hamiltonian transition dynamics in the particle transition phase, in place of random walk used in the previous literature. We call the resulting procedure Hamiltonian Sequential Monte Carlo (HSMC). Hamiltonian transition dynamics have been shown to yield superior mixing and convergence properties relative to random walk transition dynamics in the context of MCMC procedures. The rationale behind HSMC is to translate such gains to the SMC environment. HSMC will facilitate practical estimation of models with complicated latent structures, such as nonparametric individual unobserved heterogeneity, that are otherwise difficult to implement. We demonstrate the behavior of HSMC in a challenging simulation study and contrast its favorable performance with SMC and other alternative approaches. We then apply HSMC to a panel discrete choice model with nonparametric consumer heterogeneity, allowing for multiple modes, asymmetries, and data-driven clustering, providing insights for consumer segmentation, individual level marketing, and price micromanagement.
{"title":"Hamiltonian sequential Monte Carlo with application to consumer choice behavior","authors":"Martin Burda, Remi Daviet","doi":"10.1080/07474938.2022.2140982","DOIUrl":"https://doi.org/10.1080/07474938.2022.2140982","url":null,"abstract":"Abstract The practical use of nonparametric Bayesian methods requires the availability of efficient algorithms for posterior inference. The inherently serial nature of traditional Markov chain Monte Carlo (MCMC) methods imposes limitations on their efficiency and scalability. In recent years, there has been a surge of research activity devoted to developing alternative implementation methods that target parallel computing environments. Sequential Monte Carlo (SMC), also known as a particle filter, has been gaining popularity due to its desirable properties. SMC uses a genetic mutation-selection sampling approach with a set of particles representing the posterior distribution of a stochastic process. We propose to enhance the performance of SMC by utilizing Hamiltonian transition dynamics in the particle transition phase, in place of random walk used in the previous literature. We call the resulting procedure Hamiltonian Sequential Monte Carlo (HSMC). Hamiltonian transition dynamics have been shown to yield superior mixing and convergence properties relative to random walk transition dynamics in the context of MCMC procedures. The rationale behind HSMC is to translate such gains to the SMC environment. HSMC will facilitate practical estimation of models with complicated latent structures, such as nonparametric individual unobserved heterogeneity, that are otherwise difficult to implement. We demonstrate the behavior of HSMC in a challenging simulation study and contrast its favorable performance with SMC and other alternative approaches. We then apply HSMC to a panel discrete choice model with nonparametric consumer heterogeneity, allowing for multiple modes, asymmetries, and data-driven clustering, providing insights for consumer segmentation, individual level marketing, and price micromanagement.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"54 - 77"},"PeriodicalIF":1.2,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48679485","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}
Abstract When conducting regression analysis, econometricians often face the situation where some relevant regressors are unavailable in the data set at hand. This article shows how to construct a new class of nonparametric proxies by combining the original data set with one containing the missing regressors. Imputation of the missing values is done using a nonstandard kernel adapted to mixed data. We derive the asymptotic distribution of the resulting semiparametric two-sample estimator of the parameters of interest and show, using Monte Carlo simulations, that it dominates the solutions involving instrumental variables and other parametric alternatives. An application to the PSID and NLS data illustrates the importance of our estimation approach for empirical research.
{"title":"Yet another look at the omitted variable bias","authors":"Masayuki Hirukawa, Irina Murtazashvili, Artem Prokhorov","doi":"10.1080/07474938.2022.2157965","DOIUrl":"https://doi.org/10.1080/07474938.2022.2157965","url":null,"abstract":"Abstract When conducting regression analysis, econometricians often face the situation where some relevant regressors are unavailable in the data set at hand. This article shows how to construct a new class of nonparametric proxies by combining the original data set with one containing the missing regressors. Imputation of the missing values is done using a nonstandard kernel adapted to mixed data. We derive the asymptotic distribution of the resulting semiparametric two-sample estimator of the parameters of interest and show, using Monte Carlo simulations, that it dominates the solutions involving instrumental variables and other parametric alternatives. An application to the PSID and NLS data illustrates the importance of our estimation approach for empirical research.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"1 - 27"},"PeriodicalIF":1.2,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45392070","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 : 2022-12-21DOI: 10.1080/07474938.2022.2156740
Ş. Nazlıoğlu, Junsoo Lee, M. Tieslau, Cagin Karul, Yu You
Abstract This article suggests new panel unit root tests that allow for multiple structural breaks and control for cross-correlations in the panel. Breaks are modeled with a Fourier function, which allows for smooth or gradual change rather than abrupt breaks. Cross-correlations are corrected by using the PANIC procedure. The simulations show that our tests have good size and power properties and perform reasonably well when the nature of breaks or the factor structure is unknown. The new panel unit root tests support fresh evidence on the persistence of healthcare expenditures in OECD countries.
{"title":"Smooth structural changes and common factors in nonstationary panel data: an analysis of healthcare expenditures†","authors":"Ş. Nazlıoğlu, Junsoo Lee, M. Tieslau, Cagin Karul, Yu You","doi":"10.1080/07474938.2022.2156740","DOIUrl":"https://doi.org/10.1080/07474938.2022.2156740","url":null,"abstract":"Abstract This article suggests new panel unit root tests that allow for multiple structural breaks and control for cross-correlations in the panel. Breaks are modeled with a Fourier function, which allows for smooth or gradual change rather than abrupt breaks. Cross-correlations are corrected by using the PANIC procedure. The simulations show that our tests have good size and power properties and perform reasonably well when the nature of breaks or the factor structure is unknown. The new panel unit root tests support fresh evidence on the persistence of healthcare expenditures in OECD countries.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"78 - 97"},"PeriodicalIF":1.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47186267","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 : 2022-11-28DOI: 10.1080/07474938.2022.2135495
H. Herwartz, Simone Maxand, Yabibal M. Walle
Abstract The variances of most economic time series display marked fluctuations over time. Panel unit root tests of the so-called first and second generation are not robust in such cases. In response to this problem, a few heteroskedasticity-robust panel unit root tests have been proposed. An important limitation of these tests is, however, that they become invalid if the data are trending. As a prominent means of drift adjustment under the panel unit root hypothesis, the (unweighted) forward detrending scheme of Breitung suffers from nuisance parameters if the data feature time-varying variances. In this article, we propose a weighted forward-detrending scheme. Unlike its unweighted counterpart, the new detrending scheme restores the pivotalness of the heteroskedasticity-robust panel unit root tests suggested by Demetrescu and Hanck and Herwartz et al. when applied to trending panels with heteroskedastic variances. As an empirical illustration, we provide evidence in favor of non-stationarity of health care expenditures as shares of GDP in a panel of OECD economies.
{"title":"Forward detrending for heteroskedasticity-robust panel unit root testing","authors":"H. Herwartz, Simone Maxand, Yabibal M. Walle","doi":"10.1080/07474938.2022.2135495","DOIUrl":"https://doi.org/10.1080/07474938.2022.2135495","url":null,"abstract":"Abstract The variances of most economic time series display marked fluctuations over time. Panel unit root tests of the so-called first and second generation are not robust in such cases. In response to this problem, a few heteroskedasticity-robust panel unit root tests have been proposed. An important limitation of these tests is, however, that they become invalid if the data are trending. As a prominent means of drift adjustment under the panel unit root hypothesis, the (unweighted) forward detrending scheme of Breitung suffers from nuisance parameters if the data feature time-varying variances. In this article, we propose a weighted forward-detrending scheme. Unlike its unweighted counterpart, the new detrending scheme restores the pivotalness of the heteroskedasticity-robust panel unit root tests suggested by Demetrescu and Hanck and Herwartz et al. when applied to trending panels with heteroskedastic variances. As an empirical illustration, we provide evidence in favor of non-stationarity of health care expenditures as shares of GDP in a panel of OECD economies.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"28 - 53"},"PeriodicalIF":1.2,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46315353","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 : 2022-10-10DOI: 10.1080/07474938.2022.2127076
Hao Dong, Taisuke Otsu, L. Taylor
Abstract In the estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement error is present in a covariate. This paper proposes a two-stage estimator for such models. In the first stage, to adapt to the additive structure, we use a series approximation together with a ridge approach to deal with the ill-posedness brought by mismeasurement. We derive the uniform convergence rate of this first-stage estimator and characterize how the measurement error slows down the convergence rate for ordinary/super smooth cases. To establish the limiting distribution, we construct a second-stage estimator via one-step backfitting with a deconvolution kernel using the first-stage estimator. The asymptotic normality of the second-stage estimator is established for ordinary/super smooth measurement error cases. Finally, a Monte Carlo study and an empirical application highlight the applicability of the estimator.
{"title":"Nonparametric estimation of additive models with errors-in-variables","authors":"Hao Dong, Taisuke Otsu, L. Taylor","doi":"10.1080/07474938.2022.2127076","DOIUrl":"https://doi.org/10.1080/07474938.2022.2127076","url":null,"abstract":"Abstract In the estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement error is present in a covariate. This paper proposes a two-stage estimator for such models. In the first stage, to adapt to the additive structure, we use a series approximation together with a ridge approach to deal with the ill-posedness brought by mismeasurement. We derive the uniform convergence rate of this first-stage estimator and characterize how the measurement error slows down the convergence rate for ordinary/super smooth cases. To establish the limiting distribution, we construct a second-stage estimator via one-step backfitting with a deconvolution kernel using the first-stage estimator. The asymptotic normality of the second-stage estimator is established for ordinary/super smooth measurement error cases. Finally, a Monte Carlo study and an empirical application highlight the applicability of the estimator.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1164 - 1204"},"PeriodicalIF":1.2,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46612546","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}
Abstract We propose exact exogeneity tests and weak-instruments-robust tests on factor loadings for a system of regressions with possibly non-Gaussian disturbances. Our methodology is valid in finite samples and accounts for common cross-sectional factors. Analytical invariance results are derived, with companion simulation studies. Finally, a total-effect parameter is introduced that embeds the unobservable endogeneity factor. Proposed tests are applied to assess whether Catastrophe bond mutual funds co-move with financial markets. Significant risk premiums are detected globally and over time, although they are less pervasive from a domestic currency perspective. Findings underscore the importance of instrumenting and assessing direct and total effects.
{"title":"Finite sample inference in multivariate instrumental regressions with an application to Catastrophe bonds*","authors":"Marie-Claude Beaulieu, Lynda Khalaf, Maral Kichian, Olena Melin","doi":"10.1080/07474938.2022.2114625","DOIUrl":"https://doi.org/10.1080/07474938.2022.2114625","url":null,"abstract":"Abstract We propose exact exogeneity tests and weak-instruments-robust tests on factor loadings for a system of regressions with possibly non-Gaussian disturbances. Our methodology is valid in finite samples and accounts for common cross-sectional factors. Analytical invariance results are derived, with companion simulation studies. Finally, a total-effect parameter is introduced that embeds the unobservable endogeneity factor. Proposed tests are applied to assess whether Catastrophe bond mutual funds co-move with financial markets. Significant risk premiums are detected globally and over time, although they are less pervasive from a domestic currency perspective. Findings underscore the importance of instrumenting and assessing direct and total effects.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1205 - 1242"},"PeriodicalIF":1.2,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41920747","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 : 2022-09-14DOI: 10.1080/07474938.2022.2072323
Dimitra Kyriakopoulou, C. Hafner
Abstract One of the implications of the intertemporal capital asset pricing model (ICAPM) is a positive and linear relationship between the conditional mean and conditional variance of returns to the market portfolio. Empirically, however, it is often observed that there is a negative skewness in equity returns. This article shows that a negative skewness is only compatible with a positive risk premium if the innovation distribution is asymmetric with a negative skewness. We extend recent work using the EGARCH-in-Mean specification to allow for asymmetric innovations, and give results for the unconditional skewness of returns. We apply the model to the prediction of Value-at-Risk of the largest stock market indices, and demonstrate its good performance. Keywords: Exponential GARCH, in-mean, risk premium, ICAPM, unconditional skewness, asymmetric distribution, portfolio selection, Value-at-Risk.
{"title":"Reconciling negative return skewness with positive time-varying risk premia","authors":"Dimitra Kyriakopoulou, C. Hafner","doi":"10.1080/07474938.2022.2072323","DOIUrl":"https://doi.org/10.1080/07474938.2022.2072323","url":null,"abstract":"Abstract One of the implications of the intertemporal capital asset pricing model (ICAPM) is a positive and linear relationship between the conditional mean and conditional variance of returns to the market portfolio. Empirically, however, it is often observed that there is a negative skewness in equity returns. This article shows that a negative skewness is only compatible with a positive risk premium if the innovation distribution is asymmetric with a negative skewness. We extend recent work using the EGARCH-in-Mean specification to allow for asymmetric innovations, and give results for the unconditional skewness of returns. We apply the model to the prediction of Value-at-Risk of the largest stock market indices, and demonstrate its good performance. Keywords: Exponential GARCH, in-mean, risk premium, ICAPM, unconditional skewness, asymmetric distribution, portfolio selection, Value-at-Risk.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"877 - 894"},"PeriodicalIF":1.2,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43166130","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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