Journal of Econometrics最新文献_第3页

Distributional counterfactual analysis in high-dimensional setup 高维设置中的分布式反事实分析

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105675

Ricardo Masini

{"title":"Distributional counterfactual analysis in high-dimensional setup","authors":"Ricardo Masini","doi":"10.1016/j.jeconom.2024.105675","DOIUrl":"10.1016/j.jeconom.2024.105675","url":null,"abstract":"<div><div>In the context of treatment effect estimation, this paper proposes a new methodology to recover the counterfactual distribution when there is a single (or a few) treated unit and possibly a high-dimensional number of potential controls observed in a panel structure. The methodology accommodates, <em>albeit</em><span> does not require, the number of units to be larger than the number of time periods (high-dimensional setup). As opposed to model only the conditional mean, we propose to model the entire conditional quantile<span> function (CQF) in the absence of intervention and estimate it using the pre-intervention period using a penalized regression. We derive non-asymptotic bounds for the estimated CQF valid uniformly over the quantiles, allowing the practitioner to re-construct the entire contractual distribution. Moreover, we bound the probability coverage of this estimated CQF which can be used to construct valid confidence intervals for the (possibly random) treatment effect for every post-intervention period or simultaneously. We also propose a new hypothesis test for the sharp null of no-effect based on the </span></span><span><math><msup><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msup></math></span> norm of deviation of the estimated CQF to the population one. Interestingly, the null distribution is quasi-pivotal in the sense that it only depends on the estimated CQF, <span><math><msup><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msup></math></span> norm, and the number of post-intervention periods, but not on the size of the post-intervention period. For that reason, critical values can then be easily simulated. We illustrate the methodology is by revisiting the empirical study in Acemoglu et al. (2016).</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105675"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677674","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

Bayesian neural networks for macroeconomic analysis 宏观经济分析的贝叶斯神经网络

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105843

Niko Hauzenberger , Florian Huber , Karin Klieber , Massimiliano Marcellino

引用次数: 0

Sequential quantile regression for stream data by least squares 用最小二乘法对流数据进行顺序分位数回归

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105791

Ye Fan , Nan Lin

引用次数: 0

Quantile control via random forest 随机森林分位数控制

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105789

Qiang Chen , Zhijie Xiao , Qingsong Yao

{"title":"Quantile control via random forest","authors":"Qiang Chen , Zhijie Xiao , Qingsong Yao","doi":"10.1016/j.jeconom.2024.105789","DOIUrl":"10.1016/j.jeconom.2024.105789","url":null,"abstract":"<div><div><span>This paper studies robust inference procedure for treatment effects in panel data with flexible relationship across units via the random forest method. The key contribution of this paper is twofold. First, we propose a direct construction of prediction intervals for the treatment effect by exploiting the information of the joint distribution of the cross-sectional units using random forest. In particular, we propose a Quantile Control Method (QCM) using the Quantile Random Forest (QRF) to accommodate flexible cross-sectional structure as well as high dimensionality. Second, we establish the asymptotic consistency of QRF under the panel/time series setup with high dimensionality, which is of theoretical interest on its own right. In addition, Monte Carlo simulations are conducted and show that prediction intervals via the QCM have excellent coverage probability for the treatment effects comparing to existing methods in the literature, and are robust to </span>heteroskedasticity<span>, autocorrelation<span>, and various types of model misspecifications. Finally, an empirical application to study the effect of the economic integration between Hong Kong<span> and mainland China on Hong Kong’s economy is conducted to highlight the potential of the proposed method.</span></span></span></div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105789"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071699","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

Inference on time series nonparametric conditional moment restrictions using nonlinear sieves 非线性筛子对时间序列非参数条件矩约束的推断

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105920

Xiaohong Chen , Yuan Liao , Weichen Wang

{"title":"Inference on time series nonparametric conditional moment restrictions using nonlinear sieves","authors":"Xiaohong Chen , Yuan Liao , Weichen Wang","doi":"10.1016/j.jeconom.2024.105920","DOIUrl":"10.1016/j.jeconom.2024.105920","url":null,"abstract":"<div><div>This paper studies estimation of and inference on dynamic nonparametric conditional moment restrictions of high dimensional variables for weakly dependent data, where the unknown functions of endogenous variables can be approximated via nonlinear sieves such as neural networks and Gaussian radial bases. The true unknown functions and their sieve approximations are allowed to be in general weighted function spaces with unbounded supports, which is important for time series data. Under some regularity conditions, the optimally weighted general nonlinear sieve quasi-likelihood ratio (GN-QLR) statistic for the expectation functional of unknown function is asymptotically Chi-square distributed regardless whether the functional could be estimated at a root-<span><math><mi>n</mi></math></span> rate or not, and the estimated expectation functional is asymptotically efficient if it is root-<span><math><mi>n</mi></math></span> estimable. Our general theories are applied to two important examples: (1) estimating the value function and the off-policy evaluation in reinforcement learning (RL); and (2) estimating the averaged partial mean and averaged partial derivative of dynamic nonparametric quantile instrumental variable (NPQIV) models. We demonstrate the finite sample performance of our optimal inference procedure on averaged partial derivative of a dynamic NPQIV model in simulation studies.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105920"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071700","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

Interval quantile correlations with applications to testing high-dimensional quantile effects 区间分位数与测试高维分位数效应的应用程序的相关性

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105922

Yaowu Zhang , Yeqing Zhou , Liping Zhu

引用次数: 0

Estimating time-varying networks for high-dimensional time series 高维时间序列时变网络的估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105941

Jia Chen , Degui Li , Yu-Ning Li , Oliver Linton

{"title":"Estimating time-varying networks for high-dimensional time series","authors":"Jia Chen , Degui Li , Yu-Ning Li , Oliver Linton","doi":"10.1016/j.jeconom.2024.105941","DOIUrl":"10.1016/j.jeconom.2024.105941","url":null,"abstract":"<div><div>We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method with time-varying weighted group LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrices. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates including the consistency and oracle properties. In addition, we extend the methodology and theory to cover highly-correlated large-scale time series, for which the sparsity assumption becomes invalid and we allow for common factors before estimating the factor-adjusted time-varying networks. We provide extensive simulation studies and an empirical application to a large U.S. macroeconomic dataset to illustrate the finite-sample performance of our methods.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105941"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068126","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

Inequality and the zero lower bound 不等式和下界为零

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105819

Jesús Fernández-Villaverde , Joël Marbet , Galo Nuño , Omar Rachedi

引用次数: 0

Machine Learning for Economic Policy 经济政策的机器学习

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2025.105970

Maryam Haghighi , Andreas Joseph , George Kapetanios , Christopher Kurz , Michele Lenza , Juri Marcucci

引用次数: 0

On superlevel sets of conditional densities and multivariate quantile regression 关于超水平条件密度集和多元量子回归

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105807

Annika Camehl, Dennis Fok, Kathrin Gruber

引用次数: 0