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A note on sufficiency in binary panel models 关于二元面板模型的充分性的注记
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-04-04 DOI: 10.1111/ectj.12091
Koen Jochmans, Thierry Magnac
{"title":"A note on sufficiency in binary panel models","authors":"Koen Jochmans,&nbsp;Thierry Magnac","doi":"10.1111/ectj.12091","DOIUrl":"10.1111/ectj.12091","url":null,"abstract":"<div>\u0000 \u0000 <p>Consider estimating the slope coefficients of a fixed-effect binary-choice model from two-period panel data. Two approaches to semiparametric estimation at the regular parametric rate have been proposed: one is based on a sufficiency requirement, and the other is based on a conditional-median restriction. We show that, under standard assumptions, both conditions are equivalent.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357795","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
Least-squares estimation of GARCH(1,1) models with heavy-tailed errors 具有重尾误差的GARCH(1,1)模型的最小二乘估计
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-03-30 DOI: 10.1111/ectj.12089
Arie Preminger, Giuseppe Storti
{"title":"Least-squares estimation of GARCH(1,1) models with heavy-tailed errors","authors":"Arie Preminger,&nbsp;Giuseppe Storti","doi":"10.1111/ectj.12089","DOIUrl":"10.1111/ectj.12089","url":null,"abstract":"<div>\u0000 \u0000 <p>GARCH(1,1) models are widely used for modelling processes with time-varying volatility. These include financial time series, which can be particularly heavy tailed. In this paper, we propose a novel log-transform-based least-squares approach to the estimation of GARCH(1,1) models. Within this approach, the scale of the estimated volatility is dependent on an unknown tuning constant. By means of a backtesting exercise on both real and simulated data, we show that knowledge of the tuning constant is not crucial for Value at Risk prediction. However, this does not apply to many other applications where correct identification of the volatility scale is required. In order to overcome this difficulty, we propose two alternative two-stage least-squares estimators and we derive their asymptotic properties under very mild moment conditions for the errors. In particular, we establish the consistency and asymptotic normality at the standard convergence rate of for our estimators. Their finite sample properties are assessed by means of an extensive simulation study.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115058950","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: 5
Semi-linear mode regression 半线性模式回归
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-03-20 DOI: 10.1111/ectj.12088
Jerome M. Krief
{"title":"Semi-linear mode regression","authors":"Jerome M. Krief","doi":"10.1111/ectj.12088","DOIUrl":"10.1111/ectj.12088","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, I estimate the slope coefficient parameter β of the regression model , where the error term <i>e</i> satisfies almost surely and ϕ is an unknown function. It is possible to achieve -consistency for estimating β when ϕ is known up to a finite-dimensional parameter. I present a consistent and asymptotically normal estimator for β, which does not require prescribing a functional form for ϕ, let alone a parametrization. Furthermore, the rate of convergence in probability is equal to at least , and approaches if a certain density is sufficiently differentiable around the origin. This method allows both heteroscedasticity and skewness of the distribution of . Moreover, under suitable conditions, the proposed estimator exhibits an oracle property, namely the rate of convergence is identical to that when ϕ is known. A Monte Carlo study is conducted, and reveals the benefits of this estimator with fat-tailed and/or skewed data. Moreover, I apply the proposed estimator to measure the effect of primogeniture on economic achievement.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121823236","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: 15
A simple and robust estimator for linear regression models with strictly exogenous instruments 具有严格外生工具的线性回归模型的一个简单而稳健的估计
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-03-10 DOI: 10.1111/ectj.12087
Juan Carlos Escanciano
{"title":"A simple and robust estimator for linear regression models with strictly exogenous instruments","authors":"Juan Carlos Escanciano","doi":"10.1111/ectj.12087","DOIUrl":"https://doi.org/10.1111/ectj.12087","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, I investigate the estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. I introduce a uniformly (in the data-generating process) consistent estimator under nearly minimal identifying assumptions. The proposed estimator, called the integrated instrumental variables (IIV) estimator, is a simple weighted least-squares estimator. It does not require the choice of a bandwidth or tuning parameter, or the selection of a finite set of instruments. Thus, the estimator is extremely simple to implement. Monte Carlo evidence supports the theoretical claims and suggests that the IIV estimator is a robust complement to optimal instrumental variables in finite samples. In an application with quarterly UK data, the IIV estimator estimates a positive and significant elasticity of intertemporal substitution and an equally sensible estimate for its reciprocal, in sharp contrast to instrumental variables methods that fail to identify these parameters.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71957422","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: 16
Identification and estimation of semi-parametric censored dynamic panel data models of short time periods 短时间半参数截尾动态面板数据模型的辨识与估计
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-03-10 DOI: 10.1111/ectj.12086
Yingyao Hu, Ji-Liang Shiu
{"title":"Identification and estimation of semi-parametric censored dynamic panel data models of short time periods","authors":"Yingyao Hu,&nbsp;Ji-Liang Shiu","doi":"10.1111/ectj.12086","DOIUrl":"https://doi.org/10.1111/ectj.12086","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we present a semi-parametric identification and estimation method for censored dynamic panel data models of short time periods and their average partial effects with only two periods of data. The proposed method transforms the semi-parametric specification of censored dynamic panel data models into a parametric family of distribution functions of observables without specifying the distribution of the initial condition. Then the censored dynamic panel data models are globally identified under a standard maximum likelihood estimation framework. The identifying assumptions are related to the completeness of the families of known parametric distribution functions corresponding to censored dynamic panel data models. Dynamic tobit models and two-part dynamic regression models satisfy the key assumptions. We propose a sieve maximum likelihood estimator and we investigate the finite sample properties of these sieve-based estimators using Monte Carlo analysis. Our empirical application using the Medical Expenditure Panel Survey shows that individuals consume more health care when their incomes increase, after controlling for past health expenditures.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71957421","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
Debiased machine learning of conditional average treatment effects and other causal functions 条件平均处理效果和其他因果函数的去偏机器学习
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-02-21 DOI: 10.1093/ectj/utaa027
V. Semenova, V. Chernozhukov
{"title":"Debiased machine learning of conditional average treatment effects and other causal functions","authors":"V. Semenova, V. Chernozhukov","doi":"10.1093/ectj/utaa027","DOIUrl":"https://doi.org/10.1093/ectj/utaa027","url":null,"abstract":"This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularization bias. We then project the signal onto a set of basis functions, growing with sample size, which gives us the best linear predictor of the structural function. We derive a complete set of results for estimation and simultaneous inference on all parameters of the best linear predictor, conducting inference by Gaussian bootstrap. When the structural function is smooth and the basis is sufficiently rich, our estimation and inference result automatically targets this function. When basis functions are group indicators, the best linear predictor reduces to group average treatment/structural effect, and our inference automatically targets these parameters. We demonstrate our method by estimating uniform confidence bands for the average price elasticity of gasoline demand conditional on income.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44754410","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: 114
A sequential test for the specification of predictive densities 预测密度规格的顺序测试
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-02-16 DOI: 10.1111/ectj.12085
Juan Lin, Ximing Wu
{"title":"A sequential test for the specification of predictive densities","authors":"Juan Lin,&nbsp;Ximing Wu","doi":"10.1111/ectj.12085","DOIUrl":"10.1111/ectj.12085","url":null,"abstract":"<div>\u0000 \u0000 <p>We develop a specification test of predictive densities, based on the fact that the generalized residuals of correctly specified predictive density models are independent and identically distributed uniform. The proposed sequential test examines the hypotheses of serial independence and uniformity in two stages, wherein the first-stage test of serial independence is robust to violation of uniformity. The approach of the data-driven smooth test is employed to construct the test statistics. The asymptotic independence between the two stages facilitates proper control of the overall type I error of the sequential test. We derive the asymptotic null distribution of the test, which is free of nuisance parameters, and we establish its consistency. Monte Carlo simulations demonstrate excellent finite sample performance of the test. We apply this test to evaluate some commonly used models of stock returns.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81086905","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: 3
Indirect inference in spatial autoregression 空间自回归的间接推理
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-02-11 DOI: 10.1111/ectj.12084
Maria Kyriacou, Peter C. B. Phillips, Francesca Rossi
{"title":"Indirect inference in spatial autoregression","authors":"Maria Kyriacou,&nbsp;Peter C. B. Phillips,&nbsp;Francesca Rossi","doi":"10.1111/ectj.12084","DOIUrl":"10.1111/ectj.12084","url":null,"abstract":"<div>\u0000 \u0000 <p>Ordinary least-squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi-maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the II estimator displays little bias even in very small samples and gives overall performance that is comparable to the QML while raising variance in some cases; (b) II applied to QML also enjoys good finite sample properties; and (c) II shows robust performance in the presence of heavy-tailed error distributions.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116562928","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: 20
A survey of some recent applications of optimal transport methods to econometrics 最优运输方法在计量经济学中的最新应用综述
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-01-11 DOI: 10.1111/ectj.12083
Alfred Galichon
{"title":"A survey of some recent applications of optimal transport methods to econometrics","authors":"Alfred Galichon","doi":"10.1111/ectj.12083","DOIUrl":"10.1111/ectj.12083","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper surveys recent applications of methods from the theory of optimal transport to econometric problems.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127027290","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: 28
Nonparametric regression with nearly integrated regressors under long-run dependence 长期依赖条件下近积分回归量的非参数回归
IF 1.9 4区 经济学
Econometrics Journal Pub Date : 2017-01-10 DOI: 10.1111/ectj.12082
Zongwu Cai, Bingyi Jing, Xinbing Kong, Zhi Liu
{"title":"Nonparametric regression with nearly integrated regressors under long-run dependence","authors":"Zongwu Cai,&nbsp;Bingyi Jing,&nbsp;Xinbing Kong,&nbsp;Zhi Liu","doi":"10.1111/ectj.12082","DOIUrl":"10.1111/ectj.12082","url":null,"abstract":"<div>\u0000 \u0000 <p>We study the nonparametric estimation of a regression function with nonstationary (integrated or nearly integrated) covariates and the error series of the regressor process following a fractional integrated autoregressive moving average model. A local linear estimation method is developed to estimate the unknown regression function. The asymptotic results of the resulting estimator at both interior points and boundaries are obtained. The asymptotic distribution is mixed normal, associated with the local time of an Ornstein–Uhlenbeck fractional Brownian motion. Furthermore, we study the Nadaraya–Watson estimator and we examine its asymptotic results. As a result, it shares exactly the same asymptotic results as those for the local linear estimator for the zero energy situation. However, for the non-zero energy case, the local linear estimator is superior to the Nadaraya–Watson estimator in terms of optimal convergence rate. We also present a comparison of our results with the conventional results for stationary covariates. Finally, we conduct a Monte Carlo simulation to illustrate the finite sample performance of the proposed estimator.</p></div>","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2017-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/ectj.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84713637","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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