Journal of Econometrics最新文献_第5页

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

Asymptotic theory for two-way clustering 双向聚类的渐近理论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-04-10 DOI: 10.1016/j.jeconom.2025.106001

Luther Yap

引用次数: 0

Regret analysis in threshold policy design 阈值策略设计中的后悔分析

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-04-07 DOI: 10.1016/j.jeconom.2025.105998

Federico Crippa

引用次数: 0

Quantile prediction with factor-augmented regression: Structural instability and model uncertainty 因子增强回归的分位数预测：结构不稳定性和模型不确定性

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-03-25 DOI: 10.1016/j.jeconom.2025.105999

Yundong Tu , Siwei Wang

引用次数: 0

Quantile Granger causality in the presence of instability 存在不稳定性的量子格兰杰因果关系

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-03-23 DOI: 10.1016/j.jeconom.2025.105992

Alexander Mayer , Dominik Wied , Victor Troster

引用次数: 0

Model averaging prediction for possibly nonstationary autoregressions 可能非平稳自回归的模型平均预测

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-03-22 DOI: 10.1016/j.jeconom.2025.105994

Tzu-Chi Lin , Chu-An Liu

引用次数: 0

Supervised factor modeling for high-dimensional linear time series 高维线性时间序列的监督因子建模

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-03-19 DOI: 10.1016/j.jeconom.2025.105995

Feiqing Huang , Kexin Lu , Yao Zheng , Guodong Li

引用次数: 0

Huber Principal Component Analysis for large-dimensional factor models 大维度因子模型的Huber主成分分析

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-03-18 DOI: 10.1016/j.jeconom.2025.105993

Yong He , Lingxiao Li , Dong Liu , Wen-Xin Zhou

{"title":"Huber Principal Component Analysis for large-dimensional factor models","authors":"Yong He , Lingxiao Li , Dong Liu , Wen-Xin Zhou","doi":"10.1016/j.jeconom.2025.105993","DOIUrl":"10.1016/j.jeconom.2025.105993","url":null,"abstract":"<div><div>Factor models have been widely used in economics and finance. However, the heavy-tailed nature of macroeconomic and financial data is often neglected in statistical analysis. To address this issue, we propose a robust approach to estimate factor loadings and scores by minimizing the Huber loss function, which is motivated by the equivalence between conventional Principal Component Analysis (PCA) and the constrained least squares method in the factor model. We provide two algorithms that use different penalty forms. The first algorithm involves an element-wise-type Huber loss minimization, solved by an iterative Huber regression algorithm. The second algorithm, which we refer to as Huber PCA, minimizes the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-norm-type Huber loss and performs PCA on the weighted sample covariance matrix. We examine the theoretical minimizer of the element-wise Huber loss function and demonstrate that it has the same convergence rate as conventional PCA when the idiosyncratic errors have bounded second moments. We also derive their asymptotic distributions under mild conditions. Moreover, we suggest a consistent model selection criterion that relies on rank minimization to estimate the number of factors robustly. We showcase the benefits of the proposed two algorithms through extensive numerical experiments and a real macroeconomic data example. An <span>R</span> package named “<span>HDRFA</span>” <span><span><sup>1</sup></span></span> has been developed to conduct the proposed robust factor analysis.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105993"},"PeriodicalIF":9.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642831","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 and inference in non-cointegrated functional coefficient regression 非协整泛函系数回归的极限理论与推论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2025-03-17 DOI: 10.1016/j.jeconom.2025.105996

Ying Wang , Peter C.B. Phillips , Yundong Tu

{"title":"Limit theory and inference in non-cointegrated functional coefficient regression","authors":"Ying Wang , Peter C.B. Phillips , Yundong Tu","doi":"10.1016/j.jeconom.2025.105996","DOIUrl":"10.1016/j.jeconom.2025.105996","url":null,"abstract":"<div><div>Functional coefficient (FC) cointegrating regressions offer empirical investigators flexibility in modeling economic relationships by introducing covariates that influence the direction and intensity of comovement among nonstationary time series. FC regression models are also useful when formal cointegration is absent, in the sense that the equation errors may themselves be nonstationary, but where the nonstationary series display well-defined FC linkages that can be meaningfully interpreted as correlation measures involving the covariates. The present paper proposes new nonparametric estimators for such FC regression models where the nonstationary series display linkages that enable consistent estimation of the correlation measures between them. Specifically, we develop <span><math><msqrt><mrow><mi>n</mi></mrow></msqrt></math></span>-consistent estimators for the functional coefficient and establish their asymptotic distributions, which involve mixed normal limits that facilitate inference. Two novel features that appear in the limit theory are (i) the need for non-diagonal matrix normalization due to the presence of stationary and nonstationary components in the regression; and (ii) random bias elements that appear in the asymptotic distribution of the kernel estimators, again resulting from the nonstationary regression components. Numerical studies reveal that the proposed estimators achieve significant efficiency improvements compared to the estimators suggested in earlier work by Sun et al. (2011). Easily implementable specification tests with standard chi-square asymptotics are suggested to check for constancy of the functional coefficient. These tests are shown to have faster divergence rate under local alternatives and enjoy superior performance in simulations than tests proposed in Gan et al. (2014). An empirical application based on the quantity theory of money is included, illustrating the practical use of correlated but non-cointegrated regression relations.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105996"},"PeriodicalIF":9.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642830","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