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Instrumental variable estimation of a spatial dynamic panel model with endogenous spatial weights when T is small T较小时具有内生空间权值的空间动态面板模型的工具变量估计
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-07-13 DOI: 10.1111/ectj.12069
Xi Qu, Xiaoliang Wang, Lung-fei Lee
{"title":"Instrumental variable estimation of a spatial dynamic panel model with endogenous spatial weights when T is small","authors":"Xi Qu,&nbsp;Xiaoliang Wang,&nbsp;Lung-fei Lee","doi":"10.1111/ectj.12069","DOIUrl":"10.1111/ectj.12069","url":null,"abstract":"<div>\u0000 \u0000 <p>The spatial dynamic panel data (SDPD) model is a standard tool for analysing data with both spatial correlation and dynamic dependences among economic units. Conventional estimation methods rely on the key assumption that the spatial weight matrix is exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. In this paper, we propose an SDPD model with individual fixed effects in a short time dimension, where the spatial weights can be endogenous and time-varying. We establish the consistency and asymptotic normality of the two-stage instrumental variable (2SIV) estimator and we investigate its finite sample properties using a Monte Carlo simulation. When applying this model to study government expenditures in China, we find strong evidence of spatial correlation and time dependence in making spending decisions among China's provincial governments.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 3","pages":"261-290"},"PeriodicalIF":1.9,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129568108","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}
引用次数: 23
Using mixtures in econometric models: a brief review and some new results 在计量经济模型中使用混合物:简要回顾和一些新的结果
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-06-23 DOI: 10.1111/ectj.12068
Giovanni Compiani, Yuichi Kitamura
{"title":"Using mixtures in econometric models: a brief review and some new results","authors":"Giovanni Compiani,&nbsp;Yuichi Kitamura","doi":"10.1111/ectj.12068","DOIUrl":"10.1111/ectj.12068","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper is concerned with applications of mixture models in econometrics. Focused attention is given to semiparametric and nonparametric models that incorporate mixture distributions, where important issues about model specifications arise. For example, there is a significant difference between a finite mixture and a continuous mixture in terms of model identifiability. Likewise, the dimension of the latent mixing variables is a critical issue, in particular when a continuous mixture is used. We present applications of mixture models to address various problems in econometrics, such as unobserved heterogeneity and multiple equilibria. New nonparametric identification results are developed for finite mixture models with testable exclusion restrictions without relying on an identification-at-infinity assumption on covariates. The results apply to mixtures with both continuous and discrete covariates, delivering point identification under weak conditions.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 3","pages":"C95-C127"},"PeriodicalIF":1.9,"publicationDate":"2016-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134167339","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}
引用次数: 43
Royal Economic Society Annual Conference 2014 Special Issue on Large Dimensional Models 2014年皇家经济学会年会大维度模型特刊
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-05-09 DOI: 10.1111/ectj.12064
Andrew J. Patton, Richard J. Smith
{"title":"Royal Economic Society Annual Conference 2014 Special Issue on Large Dimensional Models","authors":"Andrew J. Patton,&nbsp;Richard J. Smith","doi":"10.1111/ectj.12064","DOIUrl":"10.1111/ectj.12064","url":null,"abstract":"","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 1","pages":"Ci-Cii"},"PeriodicalIF":1.9,"publicationDate":"2016-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62958527","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}
引用次数: 0
Estimating a nonparametric triangular model with binary endogenous regressors 用二元内生回归量估计非参数三角形模型
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-05-06 DOI: 10.1111/ectj.12066
Sung Jae Jun, Joris Pinkse, Haiqing Xu
{"title":"Estimating a nonparametric triangular model with binary endogenous regressors","authors":"Sung Jae Jun,&nbsp;Joris Pinkse,&nbsp;Haiqing Xu","doi":"10.1111/ectj.12066","DOIUrl":"10.1111/ectj.12066","url":null,"abstract":"<div>\u0000 \u0000 <p>We consider identification and estimation in a nonparametric triangular system with a binary endogenous regressor and nonseparable errors. For identification, we take a control function approach utilizing the Dynkin system idea. We articulate various trade-offs, including continuity, monotonicity and differentiability. For estimation, we use the idea of local instruments under smoothness assumptions, but we do not assume additive separability in latent variables. Our estimator uses nonparametric kernel regression techniques and its statistical properties are derived using the functional delta method. We establish that it is -consistent and has a limiting normal distribution. We apply the method to estimate the returns on a college education. Unlike existing work, we find that returns on a college education are consistently positive. Moreover, the returns curves we estimate are inconsistent with the shape restrictions imposed in those papers.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 2","pages":"113-149"},"PeriodicalIF":1.9,"publicationDate":"2016-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123347360","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}
引用次数: 2
Testing for error cross-sectional independence using pairwise augmented regressions 使用两两增强回归检验误差截面独立性
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-05-06 DOI: 10.1111/ectj.12067
Guangyu Mao
{"title":"Testing for error cross-sectional independence using pairwise augmented regressions","authors":"Guangyu Mao","doi":"10.1111/ectj.12067","DOIUrl":"10.1111/ectj.12067","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes two statistics for testing error cross-sectional independence in a static linear heterogeneous panel data model by virtue of pairwise augmented regressions. The tests based on the two statistics are extensions to the cross-sectional dependence test and the bias-adjusted Lagrange multiplier test. Unlike the two existing tests that are justified under sequential limits, the newly developed tests can be justified under simultaneous limits without any additional restriction imposed on the cross-sectional and time-series dimensions. Moreover, it is proved that the new tests can even be justified under high dimension, low sample size limits, provided that a homo-rank condition holds. Several simulation experiments are conducted to evaluate the performance of the newly introduced tests. The simulation results show that use of the tests can bring significant improvement, especially in cases of large cross-sectional dimension and small time-series dimension.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 3","pages":"237-260"},"PeriodicalIF":1.9,"publicationDate":"2016-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78094143","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}
引用次数: 4
Finite mixture models with one exclusion restriction 具有一个排除限制的有限混合模型
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-04-29 DOI: 10.1111/ectj.12065
Christopher P. Adams
{"title":"Finite mixture models with one exclusion restriction","authors":"Christopher P. Adams","doi":"10.1111/ectj.12065","DOIUrl":"10.1111/ectj.12065","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper characterizes the identified set for finite mixture models with one exclusion restriction on the data-generating process. It provides necessary and sufficient conditions on the observed data for point identification and for when the identified set has measure zero. The results are illustrated in a simulation study and with data from a randomized controlled trial on chemotherapy for colon cancer as well as with data from an observational study used to estimate returns to schooling.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 2","pages":"150-165"},"PeriodicalIF":1.9,"publicationDate":"2016-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123369890","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}
引用次数: 21
Model averaging in predictive regressions 预测回归中的模型平均
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-04-01 DOI: 10.1111/ectj.12063
Chu-An Liu, Biing-Shen Kuo
{"title":"Model averaging in predictive regressions","authors":"Chu-An Liu,&nbsp;Biing-Shen Kuo","doi":"10.1111/ectj.12063","DOIUrl":"10.1111/ectj.12063","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we consider forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models, given a set of potentially relevant predictors. We derive the asymptotic risk of least-squares averaging estimators in a local asymptotic framework. We then develop a frequentist model averaging criterion, an asymptotically unbiased estimator of the asymptotic risk, to select forecast weights. Monte Carlo simulations show that our averaging estimator compares favourably with alternative methods, such as weighted AIC, weighted BIC, Mallows model averaging and jackknife model averaging. The proposed method is applied to stock return predictions.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 2","pages":"203-231"},"PeriodicalIF":1.9,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531486","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}
引用次数: 22
Nonlinear panel data estimation via quantile regressions 基于分位数回归的非线性面板数据估计
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-03-18 DOI: 10.1111/ectj.12062
Manuel Arellano, Stéphane Bonhomme
{"title":"Nonlinear panel data estimation via quantile regressions","authors":"Manuel Arellano,&nbsp;Stéphane Bonhomme","doi":"10.1111/ectj.12062","DOIUrl":"10.1111/ectj.12062","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors and models with multiple individual effects. We use quantile regression as a flexible tool to model the relationships between outcomes, covariates and heterogeneity. We develop an iterative simulation-based approach for estimation, which exploits the computational simplicity of ordinary quantile regression in each iteration step. Finally, an application to measure the effect of smoking during pregnancy on birthweight completes the paper.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 3","pages":"C61-C94"},"PeriodicalIF":1.9,"publicationDate":"2016-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80523612","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}
引用次数: 68
An overview of the estimation of large covariance and precision matrices 大协方差和精度矩阵的估计概述
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-02-18 DOI: 10.1111/ectj.12061
Jianqing Fan, Yuan Liao, Han Liu
{"title":"An overview of the estimation of large covariance and precision matrices","authors":"Jianqing Fan,&nbsp;Yuan Liao,&nbsp;Han Liu","doi":"10.1111/ectj.12061","DOIUrl":"10.1111/ectj.12061","url":null,"abstract":"<div>\u0000 \u0000 <p>The estimation of large covariance and precision matrices is fundamental in modern multivariate analysis. However, problems arise from the statistical analysis of large panel economic and financial data. The covariance matrix reveals marginal correlations between variables, while the precision matrix encodes conditional correlations between pairs of variables given the remaining variables. In this paper, we provide a selective review of several recent developments on the estimation of large covariance and precision matrices. We focus on two general approaches: a rank-based method and a factor-model-based method. Theories and applications of both approaches are presented. These methods are expected to be widely applicable to the analysis of economic and financial data.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 1","pages":"C1-C32"},"PeriodicalIF":1.9,"publicationDate":"2016-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126736603","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}
引用次数: 311
Asymptotic refinements of nonparametric bootstrap for quasi-likelihood ratio tests for classes of extremum estimators 一类极值估计的拟似然比检验的非参数自举的渐近改进
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2016-02-04 DOI: 10.1111/ectj.12060
Lorenzo Camponovo
{"title":"Asymptotic refinements of nonparametric bootstrap for quasi-likelihood ratio tests for classes of extremum estimators","authors":"Lorenzo Camponovo","doi":"10.1111/ectj.12060","DOIUrl":"10.1111/ectj.12060","url":null,"abstract":"<div>\u0000 \u0000 <p>We study the asymptotic refinements of nonparametric bootstrap for quasi-likelihood ratio type tests of nonlinear restrictions. The bootstrap method applies to extremum estimators, such as quasi-maximum likelihood and generalized method of moments estimators, among others. Unlike existing parametric bootstrap procedures for quasi-likelihood ratio type tests, this bootstrap approach does not require any specific parametric assumption on the data distribution, and constructs the bootstrap samples in a fully nonparametric way. We derive the higher-order improvements of the nonparametric bootstrap compared to procedures based on standard first-order asymptotic theory. We show that the magnitude of these improvements is the same as those of parametric bootstrap procedures currently proposed in the literature. Monte Carlo simulations confirm the reliability and accuracy of the nonparametric bootstrap.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"19 1","pages":"33-54"},"PeriodicalIF":1.9,"publicationDate":"2016-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85420653","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}
引用次数: 1
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