Journal of Econometrics最新文献_第8页

Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal 机器学习推动谁：学生助学金更新的现场实验中的因果vs预测目标

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105945

Susan Athey , Niall Keleher , Jann Spiess

{"title":"Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal","authors":"Susan Athey , Niall Keleher , Jann Spiess","doi":"10.1016/j.jeconom.2024.105945","DOIUrl":"10.1016/j.jeconom.2024.105945","url":null,"abstract":"<div><div>In many settings, interventions may be more effective for some individuals than for others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use “nudges” to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example when treatment effects are difficult to estimate. We propose hybrid approaches that incorporate the strengths of predictive approaches (accurate estimation) and causal approaches (correct criterion). We show that targeting <em>intermediate</em> baseline outcomes is most effective in our application, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37%, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105945"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068129","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

Unconditional quantile partial effects via conditional quantile regression 通过条件量子回归的无条件量子部分效应

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105678

Javier Alejo , Antonio F. Galvao , Julian Martinez-Iriarte , Gabriel Montes-Rojas

引用次数: 0

Cross-sectional dependence in idiosyncratic volatility 特质波动率的横截面依赖性

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2025.106003

Ilze Kalnina , Kokouvi Tewou

{"title":"Cross-sectional dependence in idiosyncratic volatility","authors":"Ilze Kalnina , Kokouvi Tewou","doi":"10.1016/j.jeconom.2025.106003","DOIUrl":"10.1016/j.jeconom.2025.106003","url":null,"abstract":"<div><div>This paper introduces an econometric framework for analyzing cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We first consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Naive estimators of these measures are biased due to the use of the error-laden estimates of idiosyncratic volatilities. We provide bias-corrected estimators and the relevant asymptotic theory. Next, we introduce an idiosyncratic volatility factor model, in which we decompose the variation in idiosyncratic volatilities into two parts: the variation related to the systematic factors such as the market volatility, and the residual variation. Again, naive estimators of the decomposition are biased, and we provide bias-corrected estimators. We also provide the asymptotic theory that allows us to test whether the residual (non-systematic) components of the idiosyncratic volatilities exhibit cross-sectional dependence. We apply our methodology to the S&P 100 index constituents, and document strong cross-sectional dependence in their idiosyncratic volatilities. We consider two different sets of idiosyncratic volatility factors, and find that neither can fully account for the cross-sectional dependence in idiosyncratic volatilities. For each model, we map out the network of dependencies in residual (non-systematic) idiosyncratic volatilities across all stocks.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 106003"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907645","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

Limit theory for local polynomial estimation of functional coefficient models with possibly integrated regressors 可能有积分回归的泛函系数模型的局部多项式估计的极限理论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2025.106007

Ying Wang , Peter C.B. Phillips

{"title":"Limit theory for local polynomial estimation of functional coefficient models with possibly integrated regressors","authors":"Ying Wang , Peter C.B. Phillips","doi":"10.1016/j.jeconom.2025.106007","DOIUrl":"10.1016/j.jeconom.2025.106007","url":null,"abstract":"<div><div>Limit theory for functional coefficient cointegrating regression was recently found to be considerably more complex than earlier understood. The issues were explained and correct limit theory derived for the kernel weighted local level estimator in Phillips and Wang (2023b). The present paper provides complete limit theory for the general kernel weighted local <span><math><mi>p</mi></math></span>th order polynomial estimators of the functional coefficient and the coefficient derivatives. Both stationary and nonstationary regressors are allowed. Implications for bandwidth selection are discussed. An adaptive procedure to select the fit order <span><math><mi>p</mi></math></span> is proposed and found to work well. A robust <span><math><mi>t</mi></math></span>-ratio is constructed following the new limit theory, which corrects and improves the usual <span><math><mi>t</mi></math></span>-ratio in the literature. The robust <span><math><mi>t</mi></math></span>-ratio is valid and works well regardless of the properties of the regressors, thereby providing a unified procedure to compute the <span><math><mi>t</mi></math></span>-ratio and facilitating practical inference. Testing constancy of the functional coefficient is also considered. Finite sample studies are provided that corroborate the new asymptotic theory.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 106007"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891719","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

Central bank communication on social media: What, to whom, and how? 央行在社交媒体上的沟通：什么，对谁，怎么做？

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105869

Yuriy Gorodnichenko , Tho Pham , Oleksandr Talavera

引用次数: 0

Paying over the odds at the end of the fiscal year. Evidence from Ukraine 在财政年度结束时支付过高的赔率。来自乌克兰的证据

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-05-01 DOI: 10.1016/j.jeconom.2024.105903

Margaryta Klymak , Stuart Baumann

引用次数: 0

Estimating coefficient-by-coefficient breaks in panel data models 估计面板数据模型中逐个系数的断点

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-04-24 DOI: 10.1016/j.jeconom.2025.106005

Yousef Kaddoura

引用次数: 0

Subjective expectations and demand for contraception 对避孕的主观期望和需求

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-04-17 DOI: 10.1016/j.jeconom.2025.105997

Grant Miller , Áureo de Paula , Christine Valente

引用次数: 0

Predictive quantile regressions with persistent and heteroskedastic predictors: A powerful 2SLS testing approach 具有持久和异方差预测因子的预测性分位数回归：一种强大的2SLS测试方法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-04-17 DOI: 10.1016/j.jeconom.2025.106002

Matei Demetrescu , Paulo M.M. Rodrigues , A.M. Robert Taylor

{"title":"Predictive quantile regressions with persistent and heteroskedastic predictors: A powerful 2SLS testing approach","authors":"Matei Demetrescu , Paulo M.M. Rodrigues , A.M. Robert Taylor","doi":"10.1016/j.jeconom.2025.106002","DOIUrl":"10.1016/j.jeconom.2025.106002","url":null,"abstract":"<div><div>We develop new tests for predictability at a given quantile, based on the Lagrange Multiplier [LM] principle, in the context of quantile regression [QR] models which allow for persistent and endogenous predictors driven by heteroskedastic errors. Of the extant predictive QR tests in the literature, only the moving blocks bootstrap implementation, due to Fan and Lee (2019) , of the Wald-type test of Lee (2016) can allow for conditionally heteroskedastic errors in the context of a QR model with persistent predictors. In common with all other tests in the literature these tests cannot, however, allow for unconditionally heteroskedastic behaviour in the errors. The LM-based approach we adopt in this paper is obtained from a simple auxiliary linear test regression which facilitates inference based on established instrumental variable methods. We demonstrate that, as a result, the tests we develop, based on either conventional or heteroskedasticity-consistent standard errors in the auxiliary regression, are robust under the null hypothesis of no predictability to conditional heteroskedasticity and to unconditional heteroskedasticity in the errors driving the predictors, with no need for bootstrap implementation. We also propose tests for joint predictability across a set of multiple distinct quantiles. Simulation results for both conditionally and unconditionally heteroskedastic errors highlight the superior finite sample properties of our proposed LM tests over the tests of Lee (2016) and Fan and Lee (2019) and the recent variable addition tests of Cai et al. (2023). An empirical application to the equity premium for the S&P 500 highlights the practical usefulness of our proposed tests, uncovering significant evidence of predictability in the left and right tails of the returns distribution for a number of predictors containing information on market or firm risk.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 106002"},"PeriodicalIF":9.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839606","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 for deprivation profiles in a binary setting 二元环境下的贫困状况推断

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-04-10 DOI: 10.1016/j.jeconom.2025.106000

Maria Grazia Pittau , Pier Luigi Conti , Roberto Zelli

引用次数: 0