Econometrics Journal最新文献

{"title":"Royal Economic Society Annual Conference 2015 Special Issue on Econometrics of Matching","authors":"Richard J. Smith","doi":"10.1111/ectj.12094","DOIUrl":"10.1111/ectj.12094","url":null,"abstract":"","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47488106","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}

{"title":"Identification of peer effects through social networks using variance restrictions","authors":"Christiern D. Rose","doi":"10.1111/ectj.12101","DOIUrl":"10.1111/ectj.12101","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a novel method to identify peer effects through exploiting fluctuations in the variances and covariances of outcomes over a social network. Endogenous and contextual peer effects can be disentangled by contrasting the covariances of the outcomes of peers with the covariances of the outcomes of peers of peers. Correlated effects are incorporated by allowing for unrestricted network level heterogeneity. The method is applied to the Project STAR data to study peer effects in academic attainment of first-year kindergarten students. Evidence is found of positive peer effects, which are statistically significant and consistent with the existing literature.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122846370","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}

{"title":"Adaptive wild bootstrap tests for a unit root with non-stationary volatility","authors":"H. Peter Boswijk,&nbsp;Yang Zu","doi":"10.1111/ectj.12100","DOIUrl":"https://doi.org/10.1111/ectj.12100","url":null,"abstract":"<p>Recent research has emphasized that permanent changes in the innovation variance (caused by structural shifts or an integrated volatility process) lead to size distortions in conventional unit root tests. It has been shown how these size distortions can be resolved using the wild bootstrap. In this paper, we first derive the asymptotic power envelope for the unit root testing problem when the non-stationary volatility process is known. Next, we show that under suitable conditions, adaptation with respect to the volatility process is possible, in the sense that non-parametric estimation of the volatility process leads to the same asymptotic power envelope. Implementation of the resulting test involves cross-validation and the wild bootstrap. A Monte Carlo experiment shows that the asymptotic results are reflected in finite sample properties, and an empirical analysis of real exchange rates illustrates the applicability of the proposed procedures.</p>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71913332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simpler bootstrap estimation of the asymptotic variance of U-statistic-based estimators","authors":"Bo E. Honoré,&nbsp;Luojia Hu","doi":"10.1111/ectj.12099","DOIUrl":"https://doi.org/10.1111/ectj.12099","url":null,"abstract":"The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honore and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. The contribution here is that rather than repeated re-calculating the U-statistic-based estimator, we can recalculate a related estimator based on single-sums. A simulation study suggests that the approach leads to a good approximation to the standard bootstrap, and that if this is the goal, then our approach is superior to numerical derivative methods.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71963303","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}

{"title":"Double/debiased machine learning for treatment and structural parameters","authors":"Victor Chernozhukov,&nbsp;Denis Chetverikov,&nbsp;Mert Demirer,&nbsp;Esther Duflo,&nbsp;Christian Hansen,&nbsp;Whitney Newey,&nbsp;James Robins","doi":"10.1111/ectj.12097","DOIUrl":"https://doi.org/10.1111/ectj.12097","url":null,"abstract":"<div>\u0000 \u0000 <p>We revisit the classic semi-parametric problem of inference on a low-dimensional parameter θ₀ in the presence of high-dimensional nuisance parameters η₀. We depart from the classical setting by allowing for η₀ to be so high-dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η₀, we consider the use of statistical or machine learning (ML) methods, which are particularly well suited to estimation in modern, very high-dimensional cases. ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice. However, both regularization bias and overfitting in estimating η₀ cause a heavy bias in estimators of θ₀ that are obtained by naively plugging ML estimators of η₀ into estimating equations for θ₀. This bias results in the naive estimator failing to be consistent, where <i>N</i> is the sample size. We show that the impact of regularization bias and overfitting on estimation of the parameter of interest θ₀ can be removed by using two simple, yet critical, ingredients: (1) using Neyman-orthogonal moments/scores that have reduced sensitivity with respect to nuisance parameters to estimate θ₀; (2) making use of cross-fitting, which provides an efficient form of data-splitting. We call the resulting set of methods double or debiased ML (DML). We verify that DML delivers point estimators that concentrate in an -neighbourhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements. The generic statistical theory of DML is elementary and simultaneously relies on only weak theoretical requirements, which will admit the use of a broad array of modern ML methods for estimating the nuisance parameters, such as random forests, lasso, ridge, deep neural nets, boosted trees, and various hybrids and ensembles of these methods. We illustrate the general theory by applying it to provide theoretical properties of the following: DML applied to learn the main regression parameter in a partially linear regression model; DML applied to learn the coefficient on an endogenous variable in a partially linear instrumental variables model; DML applied to learn the average treatment effect and the average treatment effect on the treated under unconfoundedness; DML applied to learn the local average treatment effect in an instrumental variables setting. In addition to these theoretical applications, we also illustrate the use of DML in three empirical examples.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71978566","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}

{"title":"My friend far, far away: a random field approach to exponential random graph models","authors":"Vincent Boucher,&nbsp;Ismael Mourifié","doi":"10.1111/ectj.12096","DOIUrl":"10.1111/ectj.12096","url":null,"abstract":"<div>\u0000 \u0000 <p>We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a set of parameters from the individuals' utility functions using the observation of a single, but large, social network. We show that, under some conditions, a simple logit-based estimator is coherent, consistent and asymptotically normally distributed under a weak version of homophily. The approach is compelling as the computing time is minimal and the estimator can be easily implemented using pre-programmed estimators available in most statistical packages. We provide an application of our method using the Add Health database.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37171779","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}

{"title":"Non-parametric inference on (conditional) quantile differences and interquantile ranges, using L-statistics","authors":"Matt Goldman,&nbsp;David M. Kaplan","doi":"10.1111/ectj.12095","DOIUrl":"https://doi.org/10.1111/ectj.12095","url":null,"abstract":"<div>\u0000 \u0000 <p>We provide novel, high-order accurate methods for non-parametric inference on quantile differences between two populations in both unconditional and conditional settings. These quantile differences correspond to (conditional) quantile treatment effects under (conditional) independence of a binary treatment and potential outcomes. Our methods use the probability integral transform and a Dirichlet (rather than Gaussian) reference distribution to pick appropriate <i>L</i>-statistics as confidence interval endpoints, achieving high-order accuracy. Using a similar approach, we also propose confidence intervals/sets for vectors of quantiles, interquantile ranges and differences of linear combinations of quantiles. In the conditional setting, when smoothing over continuous covariates, optimal bandwidth and coverage probability rates are derived for all methods. Simulations show that the new confidence intervals have a favourable combination of robust accuracy and short length compared with existing approaches. Detailed steps for confidence interval construction are provided in online Appendix E as supporting information, and code for all methods, simulations and empirical examples is provided.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71959954","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}

{"title":"Multiple fixed effects in binary response panel data models","authors":"Karyne B. Charbonneau","doi":"10.1111/ectj.12093","DOIUrl":"10.1111/ectj.12093","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper considers the adaptability of estimation methods for binary response panel data models to multiple fixed effects. It is motivated by the gravity equation used in international trade, where important papers use binary response models with fixed effects for both importing and exporting countries. Econometric theory has mostly focused on the estimation of single fixed effects models. This paper investigates whether existing methods can be modified to eliminate multiple fixed effects for two specific models in which the incidental parameter problem has already been solved in the presence of a single fixed effect. We find that it is possible to generalize the conditional maximum likelihood approach to include two fixed effects for the logit. Monte Carlo simulations show that the conditional logit estimator presented in this paper is less biased than other logit estimators without sacrificing on precision. This superiority is emphasized in small samples. An application to trade data using the logit estimator further highlights the importance of properly accounting for two fixed effects.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131045864","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}

{"title":"Oracle and adaptive false discovery rate controlling methods for one-sided testing: theory and application in treatment effect evaluation","authors":"Jiaying Gu,&nbsp;Shu Shen","doi":"10.1111/ectj.12092","DOIUrl":"https://doi.org/10.1111/ectj.12092","url":null,"abstract":"<div>\u0000 \u0000 <p>Economists are often interested in identifying effective policies or treatments together with subpopulations of individuals who respond positively (or with a sign that is expected) to these treatment interventions. In this paper, we propose an optimal false discovery rate controlling method that is especially useful for such one-sided testing problems. The proposed procedure is optimal in the sense of minimizing the false non-discovery rate while controlling the false discovery rate at a pre-specified level; it uses a deconvolution method based on non-parametric maximum likelihood estimation, which allows for a broader class of treatment effect distributions than existing methods do. The proposed test demonstrates good small-sample performance in Monte Carlo simulations and it is applied to study the effect of attending a more selective high school in Romania. The application reveals strong evidence of treatment effect heterogeneity, in that students who marginally gain access to higher-ranked schools are more likely to benefit if the higher-ranked school has a relatively high admission score cut-off – or, in other words, is more selective.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71937034","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}

{"title":"Trading networks","authors":"Lada Adamic,&nbsp;Celso Brunetti,&nbsp;Jeffrey H. Harris,&nbsp;Andrei Kirilenko","doi":"10.1111/ectj.12090","DOIUrl":"10.1111/ectj.12090","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we analyse the time series of 12,000+ networks of traders in the E-mini S&amp;P 500 stock index futures contract and we empirically link network variables with financial variables more commonly used to describe market conditions. We show that network variables lead trading volume, intertrade duration, effective spreads, trade imbalances and other market liquidity measures. Network variables reflect information, information asymmetry and market liquidity and significantly presage future market conditions prior to volume or liquidity measures. We also find two-way Granger-causality between network variables and both returns and volatility, highlighting strong feedback between market conditions and trading behaviour.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127907198","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}