Journal of Econometrics最新文献_第3页

Large Bayesian SVARs with linear restrictions 具有线性限制的大型贝叶斯 SVAR

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105850

Chenghan Hou

引用次数: 0

Threshold spatial autoregressive model 阈值空间自回归模型

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105841

Kunpeng Li , Wei Lin

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引用次数: 0

Empirical risk minimization for time series: Nonparametric performance bounds for prediction 时间序列的经验风险最小化：预测的非参数性能界限

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105849

Christian Brownlees , Jordi Llorens-Terrazas

{"title":"Empirical risk minimization for time series: Nonparametric performance bounds for prediction","authors":"Christian Brownlees , Jordi Llorens-Terrazas","doi":"10.1016/j.jeconom.2024.105849","DOIUrl":"10.1016/j.jeconom.2024.105849","url":null,"abstract":"<div><p>Empirical risk minimization is a standard principle for choosing algorithms in learning theory. In this paper we study the properties of empirical risk minimization for time series. The analysis is carried out in a general framework that covers different types of forecasting applications encountered in the literature. We are concerned with 1-step-ahead prediction of a univariate time series belonging to a class of location-scale parameter-driven processes. A class of recursive algorithms is available to forecast the time series. The algorithms are recursive in the sense that the forecast produced in a given period is a function of the lagged values of the forecast and of the time series. The relationship between the generating mechanism of the time series and the class of algorithms is not specified. Our main result establishes that the algorithm chosen by empirical risk minimization achieves asymptotically the optimal predictive performance that is attainable within the class of algorithms.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105849"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001945/pdfft?md5=03c2f325f7afb91701d18b9a3b37813b&pid=1-s2.0-S0304407624001945-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification and estimation of unconditional policy effects of an endogenous binary treatment: An unconditional MTE approach 内生二元处理的无条件政策效应的识别和估计：无条件 MTE 方法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105858

Julian Martinez-Iriarte , Yixiao Sun

引用次数: 0

Identification in discrete choice models with imperfect information 不完全信息离散选择模型的识别

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105854

Cristina Gualdani , Shruti Sinha

引用次数: 0

A gentle introduction to matrix calculus 矩阵微积分入门

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105862

Jan R. Magnus

引用次数: 0

A method of moments approach to asymptotically unbiased Synthetic Controls 渐近无偏合成控制的矩方法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105846

Joseph Fry

{"title":"A method of moments approach to asymptotically unbiased Synthetic Controls","authors":"Joseph Fry","doi":"10.1016/j.jeconom.2024.105846","DOIUrl":"10.1016/j.jeconom.2024.105846","url":null,"abstract":"<div><p>A common approach to constructing a Synthetic Control unit is to fit on the outcome variable and covariates in pre-treatment time periods, but it has been shown by Ferman and Pinto (2021) that this approach does not provide asymptotic unbiasedness when the fit is imperfect and the number of controls is fixed. Many related panel methods have a similar limitation when the number of units is fixed. I introduce and evaluate a new method in which the Synthetic Control is constructed using a General Method of Moments approach where units not being included in the Synthetic Control are used as instruments. I show that a Synthetic Control Estimator of this form will be asymptotically unbiased as the number of pre-treatment time periods goes to infinity, even when pre-treatment fit is imperfect and the number of units is fixed. Furthermore, if both the number of pre-treatment and post-treatment time periods go to infinity, then averages of treatment effects can be consistently estimated. I provide a model selection procedure for deciding whether a unit should be used as an instrument or as a control. I also conduct simulations and an empirical application to compare the performance of this method with existing approaches in the literature.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105846"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Policy evaluation with multiple instrumental variables 利用多个工具变量进行政策评估

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-07-01 DOI: 10.1016/j.jeconom.2024.105718

{"title":"Policy evaluation with multiple instrumental variables","authors":"","doi":"10.1016/j.jeconom.2024.105718","DOIUrl":"10.1016/j.jeconom.2024.105718","url":null,"abstract":"<div><p>Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables<span>. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. This condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. We develop a new marginal treatment effect framework under a weaker, partial monotonicity condition. The partial monotonicity condition is implied by standard choice theory and allows for rich unobserved heterogeneity even in the presence of multiple instruments. The new framework can be viewed as having multiple different choice models for the same observed treatment variable, all of which must be consistent with the data and with each other. Using this framework, we develop a methodology for partial identification of clearly stated, policy-relevant target parameters while allowing for a wide variety of nonparametric shape restrictions and parametric functional form assumptions. We show how the methodology can be used to combine multiple instruments together to yield more informative empirical conclusions than one would obtain by using each instrument separately. The methodology provides a blueprint for extracting and aggregating information from multiple controlled or natural experiments while still allowing for rich unobserved heterogeneity in both treatment effects and choice behavior.</span></p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"243 1","pages":"Article 105718"},"PeriodicalIF":9.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sample selection models without exclusion restrictions: Parameter heterogeneity and partial identification 无排除限制的样本选择模型：参数异质性和部分识别

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-07-01 DOI: 10.1016/j.jeconom.2021.07.017

Bo E. Honoré , Luojia Hu

引用次数: 0

Dealing with imperfect randomization: Inference for the highscope perry preschool program 处理不完美的随机化：高范围佩里学前教育计划的推论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-07-01 DOI: 10.1016/j.jeconom.2024.105683

{"title":"Dealing with imperfect randomization: Inference for the highscope perry preschool program","authors":"","doi":"10.1016/j.jeconom.2024.105683","DOIUrl":"10.1016/j.jeconom.2024.105683","url":null,"abstract":"<div><p>This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of characteristics that may be observed or unobserved by the analyst. We develop a partial identification approach to this problem that makes use of information limiting the extent to which randomization is imperfect to show that it is still possible to make nontrivial inferences about the effects of the program in such settings. We consider a family of null hypotheses in which each null hypothesis specifies that the program has no effect on one of many outcomes of interest. Under weak assumptions, we construct a procedure for testing this family of null hypotheses in a way that controls the familywise error rate – the probability of even one false rejection – in finite samples. We develop our methodology in the context of a reanalysis of the HighScope Perry Preschool program. We find statistically significant effects of the program on a number of different outcomes of interest, including outcomes related to criminal activity for males and females, even after accounting for imperfections in the randomization and the multiplicity of null hypotheses.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"243 1","pages":"Article 105683"},"PeriodicalIF":9.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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