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Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression 非对称拉普拉斯回归:最大似然、最大熵和分位数回归
Journal of Econometric Methods Pub Date : 2015-01-01 DOI: 10.1515/jem-2014-0018
Anil K. Bera, A. Galvao, Gabriel Montes-Rojas, Sung Y. Park
{"title":"Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression","authors":"Anil K. Bera, A. Galvao, Gabriel Montes-Rojas, Sung Y. Park","doi":"10.1515/jem-2014-0018","DOIUrl":"https://doi.org/10.1515/jem-2014-0018","url":null,"abstract":"Abstract This paper studies the connections among the asymmetric Laplace probability density (ALPD), maximum likelihood, maximum entropy and quantile regression. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. The ALPD score functions lead to joint estimating equations that delivers estimates for the slope parameters together with a representative quantile. Asymptotic properties of the estimator are derived under the framework of the quasi maximum likelihood estimation. With a limited simulation experiment we evaluate the finite sample properties of our estimator. Finally, we illustrate the use of the estimator with an application to the US wage data to evaluate the effect of training on wages.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"5 1","pages":"101 - 79"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2014-0018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 48
Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform 失业持续时间的竞争风险联结模型:在德国哈茨改革中的应用
Journal of Econometric Methods Pub Date : 2015-01-01 DOI: 10.1515/jem-2015-0005
Simon M. S. Lo, G. Stephan, R. Wilke
{"title":"Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform","authors":"Simon M. S. Lo, G. Stephan, R. Wilke","doi":"10.1515/jem-2015-0005","DOIUrl":"https://doi.org/10.1515/jem-2015-0005","url":null,"abstract":"Abstract The copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate that the CGE imposes fewer restrictions on partial covariate effects than standard methods do. Differences are less evident when a more flexible difference-in-differences estimator is applied. It is also found that the MMPHM estimates react more strongly to the choice of the copula than the CGE in terms of the shape of the treatment effect function over time. Thus, the MMPHM produces less robust results in our application.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2015-0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
MULTIVARIATE FRACTIONAL REGRESSION ESTIMATION OF ECONOMETRIC SHARE MODELS. 计量经济份额模型的多元分数回归估计。
Journal of Econometric Methods Pub Date : 2015-01-01 Epub Date: 2014-03-22 DOI: 10.1515/jem-2012-0006
John Mullahy
{"title":"MULTIVARIATE FRACTIONAL REGRESSION ESTIMATION OF ECONOMETRIC SHARE MODELS.","authors":"John Mullahy","doi":"10.1515/jem-2012-0006","DOIUrl":"https://doi.org/10.1515/jem-2012-0006","url":null,"abstract":"<p><p>This paper describes and applies econometric strategies for estimating regression models of economic share data outcomes where the shares may take boundary values (zero and one) with nontrivial probability. The main focus of the paper is on the conditional mean structures of such data. The paper proposes an extension of the fractional regression methodology proposed by Papke and Wooldridge, 1996, 2008, in univariate cross-sectional and panel contexts. The paper discusses the stochastic aspects of share definition and measurement, and summarizes important features of the existing literature on econometric strategies for share model estimation. The paper then goes on to discuss the univariate fractional regression estimation strategies proposed by Papke and Wooldridge and to extend the fractional regression approach to estimation of and inference about regression models describing the multivariate share data. Some issues involving outcome aggregation/disaggregation are considered, as is a full likelihood estimation approach based on Dirichlet-multinomial models. The paper demonstrates the workings of these various empirical strategies by estimating models of financial asset portfolio shares using data from the 2001, 2004, and 2007 U.S. Surveys of Consumer Finances.</p>","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"4 1","pages":"71-100"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2012-0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36373871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 131
Bounding a Linear Causal Effect Using Relative Correlation Restrictions 用相对相关限制限定线性因果效应
Journal of Econometric Methods Pub Date : 2015-01-01 DOI: 10.1515/jem-2013-0013
Brian Krauth
{"title":"Bounding a Linear Causal Effect Using Relative Correlation Restrictions","authors":"Brian Krauth","doi":"10.1515/jem-2013-0013","DOIUrl":"https://doi.org/10.1515/jem-2013-0013","url":null,"abstract":"Abstract This paper describes and implements a simple partial solution to the most common problem in applied microeconometrics: estimating a linear causal effect with a potentially endogenous explanatory variable and no suitable instrumental variables. Empirical researchers faced with this situation can either assume away the endogeneity or accept that the effect of interest is not identified. This paper describes a middle ground in which the researcher assumes plausible but nontrivial restrictions on the correlation between the variable of interest and relevant unobserved variables relative to the correlation between the variable of interest and observed control variables. Given such relative correlation restrictions, the researcher can then estimate informative bounds on the effect and assess the sensitivity of conventional estimates to plausible deviations from exogeneity. Two empirical applications demonstrate the potential usefulness of this method for both experimental and observational data.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"5 1","pages":"117 - 141"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2013-0013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 43
Spatial Errors in Count Data Regressions 计数数据回归中的空间误差
Journal of Econometric Methods Pub Date : 2014-08-01 DOI: 10.2139/ssrn.2406216
Marinho Bertanha, Petra Moser
{"title":"Spatial Errors in Count Data Regressions","authors":"Marinho Bertanha, Petra Moser","doi":"10.2139/ssrn.2406216","DOIUrl":"https://doi.org/10.2139/ssrn.2406216","url":null,"abstract":"Abstract Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum likelihood estimator (PCFE) and its sandwich variance estimator are consistent even if the data are not Poisson-distributed, or if the data are correlated over time. Analyses of counts may however also be affected by correlation in the cross-section. For example, patent counts or publications may increase across related research fields in response to common shocks. This paper shows that the PCFE and its sandwich variance estimator are consistent in the presence of such dependence in the cross-section – as long as spatial dependence is time-invariant. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"5 1","pages":"49 - 69"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68184511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 25
Broken or Fixed Effects? 破碎或固定的效果?
Journal of Econometric Methods Pub Date : 2014-07-01 DOI: 10.1515/jem-2017-0002
C. Gibbons, Juan Carlos Suárez Serrato, Michael B. Urbancic
{"title":"Broken or Fixed Effects?","authors":"C. Gibbons, Juan Carlos Suárez Serrato, Michael B. Urbancic","doi":"10.1515/jem-2017-0002","DOIUrl":"https://doi.org/10.1515/jem-2017-0002","url":null,"abstract":"Abstract We replicate eight influential papers to provide empirical evidence that, in the presence of heterogeneous treatment effects, OLS with fixed effects (FE) is generally not a consistent estimator of the average treatment effect (ATE). We propose two alternative estimators that recover the ATE in the presence of group-specific heterogeneity. We document that heterogeneous treatment effects are common and the ATE is often statistically and economically different from the FE estimate. In all but one of our replications, there is statistically significant treatment effect heterogeneity and, in six, the ATEs are either economically or statistically different from the FE estimates.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2017-0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 105
Dif-in-Dif Estimators of Multiplicative Treatment Effects 乘法处理效果的双中双估计量
Journal of Econometric Methods Pub Date : 2014-03-13 DOI: 10.2139/ssrn.2566252
Emanuele Ciani, Paul Fisher
{"title":"Dif-in-Dif Estimators of Multiplicative Treatment Effects","authors":"Emanuele Ciani, Paul Fisher","doi":"10.2139/ssrn.2566252","DOIUrl":"https://doi.org/10.2139/ssrn.2566252","url":null,"abstract":"Abstract We consider a difference-in-differences setting with a continuous outcome. The standard practice is to take its logarithm and then interpret the results as an approximation of the multiplicative treatment effect on the original outcome. We argue that a researcher should rather focus on the non-transformed outcome when discussing causal inference. The first step should be to decide whether the time trend is more likely to hold in multiplicative or level form. If the former, it is preferable to estimate an exponential model by Poisson Pseudo Maximum Likelihood, which does not require statistical independence of the error term. Running OLS on the log-linearised model might instead lead to confounding distributional and mean changes. We illustrate the argument with a simulation exercise.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2139/ssrn.2566252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68206393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 210
Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work? 检验计数数据模型中多项回归量的外生性:两阶段残差包含是否有效?
Journal of Econometric Methods Pub Date : 2014-01-23 DOI: 10.1515/jem-2014-0019
A. Geraci, D. Fabbri, C. Monfardini
{"title":"Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?","authors":"A. Geraci, D. Fabbri, C. Monfardini","doi":"10.1515/jem-2014-0019","DOIUrl":"https://doi.org/10.1515/jem-2014-0019","url":null,"abstract":"Abstract We study a simple exogeneity test in count data models with possibly endogenous multinomial treatment. The test is based on Two Stage Residual Inclusion (2SRI), an estimation method which has been proved to be consistent for a general class of nonlinear parametric models. Results from a broad set of simulation experiments provide novel evidence on important features of this approach. We find differences in the finite sample performance of various likelihood-based tests, analyze their robustness to misspecification arising from neglected over-dispersion or from incorrect specification of the first stage model, and uncover that standardizing the variance of the first stage residuals leads to better results. An original application to testing the endogeneity status of insurance in a model of healthcare demand corroborates our Monte Carlo findings.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2014-0019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 27
Non-Standard Tests through a Composite Null and Alternative in Point-Identified Parameters 通过点识别参数中的复合空值和可选值进行非标准测试
Journal of Econometric Methods Pub Date : 2014-01-01 DOI: 10.1515/jem-2014-0006
J. Hahn, G. Ridder
{"title":"Non-Standard Tests through a Composite Null and Alternative in Point-Identified Parameters","authors":"J. Hahn, G. Ridder","doi":"10.1515/jem-2014-0006","DOIUrl":"https://doi.org/10.1515/jem-2014-0006","url":null,"abstract":"Abstract We propose a new approach to statistical inference on parameters that depend on population parameters in a non-standard way. As examples we consider a parameter that is interval identified and a parameter that is the maximum (or minimum) of population parameters. In both examples we transform the inference problem into a test of a composite null against a composite alternative hypothesis involving point identified population parameters. We use standard tools in this testing problem. This setup substantially simplifies the conceptual basis of the inference problem. By inverting the Likelihood Ratio test statistic for the composite null and composite alternative inference problem, we obtain a closed form expression for the confidence interval that does not require any tuning parameter and is uniformly valid. We use our method to derive a confidence interval for a regression coefficient in a multiple linear regression with an interval censored dependent variable.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"4 1","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2014-0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Bivariate Non-Normality in the Sample Selection Model 样本选择模型中的二元非正态性
Journal of Econometric Methods Pub Date : 2014-01-01 DOI: 10.1515/jem-2013-0008
Claudia Pigini
{"title":"Bivariate Non-Normality in the Sample Selection Model","authors":"Claudia Pigini","doi":"10.1515/jem-2013-0008","DOIUrl":"https://doi.org/10.1515/jem-2013-0008","url":null,"abstract":"Abstract Since the seminal paper by [Heckman, J. J. 1974. “Shadow Prices, Market Wages, and Labor Supply.” Econometrica 42: 679–694], the sample selection model has been an essential tool for applied economists and arguably the most sensitive to sources of misspecification among the standard microeconometric models involving limited dependent variables. The need for alternative methods to get consistent estimators has led to a number of estimation proposals for the sample selection model under non-normality. There is a marked dichotomy in the literature that has developed in two conceptually different directions: the bivariate normality assumption can be either replaced, by using copulae, or relaxed/removed, relying on semi- and non-parametric estimators. This paper surveys the more recent proposals on the estimation of the sample selection model that deal with distributional misspecification giving the practitioner a unified framework of both parametric and semi/non-parametric options.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"4 1","pages":"123 - 144"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jem-2013-0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66939620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
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