Pub Date : 2022-09-12DOI: 10.1080/07474938.2022.2114624
J. Kim, Byoung G. Park
Abstract This paper develops a test for the rank similarity condition of the nonseparable instrumental variable quantile regression model using the local average treatment effect model. When the instrument takes more than two values or multiple binary instruments are available, there exist multiple complier groups for which the marginal distributions of potential outcomes are identified. A testable implication is obtained by comparing the distributions of ranks across complier groups. We propose a test procedure in a semiparametric quantile regression specification. We establish the weak convergence of the test statistic and the validity of the bootstrap critical value. We illustrate the test with an empirical example of the effects of fertility on women’s labor supply.
{"title":"Testing rank similarity in the local average treatment effects model","authors":"J. Kim, Byoung G. Park","doi":"10.1080/07474938.2022.2114624","DOIUrl":"https://doi.org/10.1080/07474938.2022.2114624","url":null,"abstract":"Abstract This paper develops a test for the rank similarity condition of the nonseparable instrumental variable quantile regression model using the local average treatment effect model. When the instrument takes more than two values or multiple binary instruments are available, there exist multiple complier groups for which the marginal distributions of potential outcomes are identified. A testable implication is obtained by comparing the distributions of ranks across complier groups. We propose a test procedure in a semiparametric quantile regression specification. We establish the weak convergence of the test statistic and the validity of the bootstrap critical value. We illustrate the test with an empirical example of the effects of fertility on women’s labor supply.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1265 - 1286"},"PeriodicalIF":1.2,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43355583","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 : 2022-09-09DOI: 10.1080/07474938.2022.2114623
Maoshan Tian, Huw Dixon
Abstract This paper focuses on the link between non-parametric survival analysis and three distributions. The delta method is applied to derive the variances of the non-parametric estimators of three distributions: the distribution of durations (DD), the cross-sectional distribution of ages (CSA) and the cross-sectional distribution of (completed) durations (CSD). The non-parametric estimator of the the cross-sectional distribution of durations (CSD) has been defined and derived by Dixon (2012) and used in the generalized Taylor price model (GTE) by Dixon and Le Bihan (2012). The Monte Carlo method is applied to evaluate the variances of the estimators of DD and CSD and how their performance varies with sample size and the censoring of data. We apply those estimators to two data sets: the UK CPI micro-price data and waiting-time data from UK hospitals. Both the estimates of the distributions and their variances are calculated. Depending on the empirical results, the estimated variances indicate that the DD and CSD estimators are all significant.
{"title":"The variances of non-parametric estimates of the cross-sectional distribution of durations","authors":"Maoshan Tian, Huw Dixon","doi":"10.1080/07474938.2022.2114623","DOIUrl":"https://doi.org/10.1080/07474938.2022.2114623","url":null,"abstract":"Abstract This paper focuses on the link between non-parametric survival analysis and three distributions. The delta method is applied to derive the variances of the non-parametric estimators of three distributions: the distribution of durations (DD), the cross-sectional distribution of ages (CSA) and the cross-sectional distribution of (completed) durations (CSD). The non-parametric estimator of the the cross-sectional distribution of durations (CSD) has been defined and derived by Dixon (2012) and used in the generalized Taylor price model (GTE) by Dixon and Le Bihan (2012). The Monte Carlo method is applied to evaluate the variances of the estimators of DD and CSD and how their performance varies with sample size and the censoring of data. We apply those estimators to two data sets: the UK CPI micro-price data and waiting-time data from UK hospitals. Both the estimates of the distributions and their variances are calculated. Depending on the empirical results, the estimated variances indicate that the DD and CSD estimators are all significant.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1243 - 1264"},"PeriodicalIF":1.2,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46882605","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 : 2022-09-01DOI: 10.1080/07474938.2022.2091363
D. Drukker, Di Liu
Abstract High-dimensional models that include many covariates which might potentially affect an outcome are increasingly common. This paper begins by introducing a lasso-based approach and a stepwise-based approach to valid inference for a high-dimensional model. It then discusses several essential extensions to the literature that make the estimators more usable in practice. Finally, it presents Monte Carlo evidence to help applied researchers choose which of several available estimators should be used in practice. The Monte Carlo evidence shows that our extensions to the literature perform well. It also shows that a BIC-stepwise approach performs well for a data-generating process for which the lasso-based approaches and a testing-stepwise approach fail. The Monte Carlo evidence also indicates the BIC-based lasso and plugin-based lasso can produce better inferential results than the ubiquitous CV-based lasso. Easy-to-use Stata commands are available for all the methods that we discuss.
{"title":"Finite-sample results for lasso and stepwise Neyman-orthogonal Poisson estimators","authors":"D. Drukker, Di Liu","doi":"10.1080/07474938.2022.2091363","DOIUrl":"https://doi.org/10.1080/07474938.2022.2091363","url":null,"abstract":"Abstract High-dimensional models that include many covariates which might potentially affect an outcome are increasingly common. This paper begins by introducing a lasso-based approach and a stepwise-based approach to valid inference for a high-dimensional model. It then discusses several essential extensions to the literature that make the estimators more usable in practice. Finally, it presents Monte Carlo evidence to help applied researchers choose which of several available estimators should be used in practice. The Monte Carlo evidence shows that our extensions to the literature perform well. It also shows that a BIC-stepwise approach performs well for a data-generating process for which the lasso-based approaches and a testing-stepwise approach fail. The Monte Carlo evidence also indicates the BIC-based lasso and plugin-based lasso can produce better inferential results than the ubiquitous CV-based lasso. Easy-to-use Stata commands are available for all the methods that we discuss.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1047 - 1076"},"PeriodicalIF":1.2,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46193070","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 : 2022-08-02DOI: 10.1080/07474938.2022.2082169
Yu‐Chin Hsu, Jen-Che Liao, Eric S. Lin
Abstract This paper studies three semiparametric models that are useful and frequently encountered in applied econometric work—a linear and two partially linear specifications with generated regressors, i.e., the regressors that are unobserved, but can be nonparametrically estimated from the data. Our framework allows for generated regressors to appear in linear or nonlinear components of partially linear models. We propose two-step series estimators for the finite-dimensional parameters, establish their -consistency (with sample size n) and asymptotic normality, and provide the asymptotic variance formulae that take into account the estimation error of generated regressors. Moreover, we develop a nonparametric specification test for the models considered. Numerical performances of the proposed estimators and test via simulation experiments and an empirical application illustrate the utility of our approach.
{"title":"Two-step series estimation and specification testing of (partially) linear models with generated regressors","authors":"Yu‐Chin Hsu, Jen-Che Liao, Eric S. Lin","doi":"10.1080/07474938.2022.2082169","DOIUrl":"https://doi.org/10.1080/07474938.2022.2082169","url":null,"abstract":"Abstract This paper studies three semiparametric models that are useful and frequently encountered in applied econometric work—a linear and two partially linear specifications with generated regressors, i.e., the regressors that are unobserved, but can be nonparametrically estimated from the data. Our framework allows for generated regressors to appear in linear or nonlinear components of partially linear models. We propose two-step series estimators for the finite-dimensional parameters, establish their -consistency (with sample size n) and asymptotic normality, and provide the asymptotic variance formulae that take into account the estimation error of generated regressors. Moreover, we develop a nonparametric specification test for the models considered. Numerical performances of the proposed estimators and test via simulation experiments and an empirical application illustrate the utility of our approach.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"985 - 1007"},"PeriodicalIF":1.2,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49652542","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 : 2022-07-25DOI: 10.1080/07474938.2022.2091360
Shunan Zhao, Yiguo Sun, S. Kumbhakar
Abstract We examine heterogeneous nonlinear effects of income on democracy using country-level data from 1960 to 2000. Existing studies mainly focused on a linear relationship or restricted nonlinear ones and find mixed findings about the effects of income on democracy. The strong positive cross-country correlation between income and democracy is often found to disappear after controlling country specific fixed effects, although the result varies with different estimation methods and samples. In contrast to previous studies, we apply a flexible semiparametric additive partially linear dynamic panel data model to explore the heterogeneous effects of income on democracy. We assume income is endogenous and it enters in the regression model nonparametrically. Our model specification also allows for different democracy equilibria and adjustment speeds toward equilibria. We propose a nonlinearity test for our model and a penalized sieve minimum distance estimator to solve the ill-posed inverse problem in the semiparametric instrumental variable estimator. The finite sample performance of the proposed test and estimator are evaluated by simulations. In the empirical model, we find that the relationship between income and democracy is nonlinear and it is more complex than a simple inverted U-shape. Specifically, depending on the choice of the democracy measure, income may have positive effects on democracy for low-income countries, negative effects for middle-income countries, and no effects for high-income countries.
{"title":"Income and democracy: a semiparametric approach","authors":"Shunan Zhao, Yiguo Sun, S. Kumbhakar","doi":"10.1080/07474938.2022.2091360","DOIUrl":"https://doi.org/10.1080/07474938.2022.2091360","url":null,"abstract":"Abstract We examine heterogeneous nonlinear effects of income on democracy using country-level data from 1960 to 2000. Existing studies mainly focused on a linear relationship or restricted nonlinear ones and find mixed findings about the effects of income on democracy. The strong positive cross-country correlation between income and democracy is often found to disappear after controlling country specific fixed effects, although the result varies with different estimation methods and samples. In contrast to previous studies, we apply a flexible semiparametric additive partially linear dynamic panel data model to explore the heterogeneous effects of income on democracy. We assume income is endogenous and it enters in the regression model nonparametrically. Our model specification also allows for different democracy equilibria and adjustment speeds toward equilibria. We propose a nonlinearity test for our model and a penalized sieve minimum distance estimator to solve the ill-posed inverse problem in the semiparametric instrumental variable estimator. The finite sample performance of the proposed test and estimator are evaluated by simulations. In the empirical model, we find that the relationship between income and democracy is nonlinear and it is more complex than a simple inverted U-shape. Specifically, depending on the choice of the democracy measure, income may have positive effects on democracy for low-income countries, negative effects for middle-income countries, and no effects for high-income countries.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1113 - 1140"},"PeriodicalIF":1.2,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46139200","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 : 2022-07-23DOI: 10.1080/07474938.2022.2094539
Jingjie Xiang, Gangzheng Guo, Jiaolong Li
Abstract This paper estimates the number of factors in constrained and partially constrained factor models (Tsai and Tsay, 2010) based on constrained Bayesian information criterion (CBIC). Following Bai and Ng (2002), the estimation of the number of factors depends on the tradeoff between good fit and parsimony, so we first derive the convergence rate of constrained factor estimates under the framework of large cross-sections (N) and large time dimensions (T). Furthermore, we demonstrate that the penalty for overfitting can be a function of N alone, so the BIC form, which does not work in the case of (unconstrained) approximate factor models, consistently estimates the number of factors in constrained factor models. We then conduct Monte Carlo simulations to show that our proposed CBIC has good finite sample performance and outperforms competing methods.
摘要本文基于约束贝叶斯信息准则(CBIC)对约束因子模型和部分约束因子模型(Tsai and Tsay, 2010)中的因子数量进行估计。继Bai和Ng(2002)之后，因子数量的估计取决于良好拟合和简约性之间的权衡，因此我们首先推导了大横截面(N)和大时间维度(T)框架下约束因子估计的收敛率。此外，我们证明了过拟合的惩罚可以是N的函数，因此BIC形式在(无约束)近似因子模型的情况下不起作用，始终如一地估计约束因素模型中的因素数量。然后，我们进行蒙特卡罗模拟，表明我们提出的CBIC具有良好的有限样本性能，并且优于竞争方法。
{"title":"Determining the number of factors in constrained factor models via Bayesian information criterion","authors":"Jingjie Xiang, Gangzheng Guo, Jiaolong Li","doi":"10.1080/07474938.2022.2094539","DOIUrl":"https://doi.org/10.1080/07474938.2022.2094539","url":null,"abstract":"Abstract This paper estimates the number of factors in constrained and partially constrained factor models (Tsai and Tsay, 2010) based on constrained Bayesian information criterion (CBIC). Following Bai and Ng (2002), the estimation of the number of factors depends on the tradeoff between good fit and parsimony, so we first derive the convergence rate of constrained factor estimates under the framework of large cross-sections (N) and large time dimensions (T). Furthermore, we demonstrate that the penalty for overfitting can be a function of N alone, so the BIC form, which does not work in the case of (unconstrained) approximate factor models, consistently estimates the number of factors in constrained factor models. We then conduct Monte Carlo simulations to show that our proposed CBIC has good finite sample performance and outperforms competing methods.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"98 - 122"},"PeriodicalIF":1.2,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47049253","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 : 2022-07-21DOI: 10.1080/07474938.2022.2091713
Dakyung Seong, J. Cho, T. Teräsvirta
Abstract This article examines the null limit distribution of the quasi-likelihood ratio (QLR) statistic for testing linearity condition against the smooth transition autoregressive (STAR) model. We explicitly show that the QLR test statistic weakly converges to a functional of a multivariate Gaussian process under the null of linearity, which is done by resolving the issue of identification problem arises in two different ways under the null. In contrast with the Lagrange multiplier test that is widely employed for testing the linearity condition, the proposed QLR statistic has an omnibus power, and thus, it complements the existing testing procedure. We show the empirical relevance of our test by testing the neglected nonlinearity of the US fiscal multipliers and growth rates of US unemployment. These empirical examples demonstrate that the QLR test is useful for detecting the nonlinear structure among economic variables.
{"title":"Comprehensively testing linearity hypothesis using the smooth transition autoregressive model","authors":"Dakyung Seong, J. Cho, T. Teräsvirta","doi":"10.1080/07474938.2022.2091713","DOIUrl":"https://doi.org/10.1080/07474938.2022.2091713","url":null,"abstract":"Abstract This article examines the null limit distribution of the quasi-likelihood ratio (QLR) statistic for testing linearity condition against the smooth transition autoregressive (STAR) model. We explicitly show that the QLR test statistic weakly converges to a functional of a multivariate Gaussian process under the null of linearity, which is done by resolving the issue of identification problem arises in two different ways under the null. In contrast with the Lagrange multiplier test that is widely employed for testing the linearity condition, the proposed QLR statistic has an omnibus power, and thus, it complements the existing testing procedure. We show the empirical relevance of our test by testing the neglected nonlinearity of the US fiscal multipliers and growth rates of US unemployment. These empirical examples demonstrate that the QLR test is useful for detecting the nonlinear structure among economic variables.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"966 - 984"},"PeriodicalIF":1.2,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41794518","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 : 2022-07-15DOI: 10.1080/07474938.2022.2091362
B. Chen
Abstract We consider a new nonparametric test for serial correlation of unknown form in the estimated residuals of a panel regression model, where individual and time effects can be fixed or random, and the panel data can be balanced or unbalanced. Our test is robust against potential weak error cross-sectional dependence and error serial dependence in higher-order moments. This is in contrast to existing tests for serial correlation in panel data models, which assume error components to be cross-sectionally and serially independent. Our test has an asymptotic N(0, 1) distribution under the null hypothesis and is consistent against serial correlation of unknown form. No common alternative is assumed and hence our test allows for substantial inhomogeneity in serial correlation across individuals. A simulation study highlights the merits of the proposed test relative to a variety of existing tests in the literature. We apply the new test to the empirical study of Wolfers on the relationship between unilateral divorce laws and divorce rates and find strong evidence against serial uncorrelatedness even controlling for the fixed effect.
{"title":"A robust test for serial correlation in panel data models","authors":"B. Chen","doi":"10.1080/07474938.2022.2091362","DOIUrl":"https://doi.org/10.1080/07474938.2022.2091362","url":null,"abstract":"Abstract We consider a new nonparametric test for serial correlation of unknown form in the estimated residuals of a panel regression model, where individual and time effects can be fixed or random, and the panel data can be balanced or unbalanced. Our test is robust against potential weak error cross-sectional dependence and error serial dependence in higher-order moments. This is in contrast to existing tests for serial correlation in panel data models, which assume error components to be cross-sectionally and serially independent. Our test has an asymptotic N(0, 1) distribution under the null hypothesis and is consistent against serial correlation of unknown form. No common alternative is assumed and hence our test allows for substantial inhomogeneity in serial correlation across individuals. A simulation study highlights the merits of the proposed test relative to a variety of existing tests in the literature. We apply the new test to the empirical study of Wolfers on the relationship between unilateral divorce laws and divorce rates and find strong evidence against serial uncorrelatedness even controlling for the fixed effect.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1095 - 1112"},"PeriodicalIF":1.2,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44391063","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 : 2022-07-12DOI: 10.1080/07474938.2022.2091361
S. Feng, Yingyao Hu, Jiandong Sun
Abstract We develop a general misclassification model to explain the so-called “Rotation Group Bias (RGB)” problem in the Current Population Surveys, where different rotation groups report different labor force statistics. The key insight is that responses to repeated questions in surveys can depend not only on unobserved true values, but also on previous responses to the same questions. Our method provides a framework to understand why unemployment rates in rotation group one are higher than those in other rotation groups in the CPS, without imposing any a priori assumptions on the existence and direction of RGB. Using our method, we provide new estimates of the U.S. unemployment rates, which are much higher than the official series, but lower than previous estimates that ignored persistence in misclassification.
{"title":"Rotation group bias and the persistence of misclassification errors in the Current Population Surveys","authors":"S. Feng, Yingyao Hu, Jiandong Sun","doi":"10.1080/07474938.2022.2091361","DOIUrl":"https://doi.org/10.1080/07474938.2022.2091361","url":null,"abstract":"Abstract We develop a general misclassification model to explain the so-called “Rotation Group Bias (RGB)” problem in the Current Population Surveys, where different rotation groups report different labor force statistics. The key insight is that responses to repeated questions in surveys can depend not only on unobserved true values, but also on previous responses to the same questions. Our method provides a framework to understand why unemployment rates in rotation group one are higher than those in other rotation groups in the CPS, without imposing any a priori assumptions on the existence and direction of RGB. Using our method, we provide new estimates of the U.S. unemployment rates, which are much higher than the official series, but lower than previous estimates that ignored persistence in misclassification.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1077 - 1094"},"PeriodicalIF":1.2,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43058915","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 : 2022-05-30DOI: 10.1080/07474938.2022.2074187
Xin Geng, K. Sun
Abstract This article studies a partially linear seemingly unrelated regressions (SUR) model to estimate a translog cost system that consists of a partially linear translog cost function and input share equations. The parametric component is estimated via a simple two-step feasible SUR estimation procedure. We show that the resulting estimator achieves root-n convergence and is asymptotically normal. The nonparametric component is estimated with a nonparametric SUR estimator based on the Cholesky decomposition. We show that this estimator is consistent, asymptotically normal, and more efficient relative to the ones that ignore cross-equation correlation. We emphasize the importance and implication of the choice of square root of the covariance matrix by comparing the Cholesky and Spectral decompositions. A model specification test for parametric functional form is proposed. An Italian banking data set is used to estimate the translog cost system. Results show that marginal effects of risks on cost of production are heterogeneous but increase with risk levels.
{"title":"Estimation of a partially linear seemingly unrelated regressions model: application to a translog cost system","authors":"Xin Geng, K. Sun","doi":"10.1080/07474938.2022.2074187","DOIUrl":"https://doi.org/10.1080/07474938.2022.2074187","url":null,"abstract":"Abstract This article studies a partially linear seemingly unrelated regressions (SUR) model to estimate a translog cost system that consists of a partially linear translog cost function and input share equations. The parametric component is estimated via a simple two-step feasible SUR estimation procedure. We show that the resulting estimator achieves root-n convergence and is asymptotically normal. The nonparametric component is estimated with a nonparametric SUR estimator based on the Cholesky decomposition. We show that this estimator is consistent, asymptotically normal, and more efficient relative to the ones that ignore cross-equation correlation. We emphasize the importance and implication of the choice of square root of the covariance matrix by comparing the Cholesky and Spectral decompositions. A model specification test for parametric functional form is proposed. An Italian banking data set is used to estimate the translog cost system. Results show that marginal effects of risks on cost of production are heterogeneous but increase with risk levels.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"41 1","pages":"1008 - 1046"},"PeriodicalIF":1.2,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44945374","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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