Journal of Econometrics最新文献

From LATE to ATE: A Bayesian approach

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105895

Isaac M. Opper

引用次数: 0

Estimating and testing for smooth structural changes in moment condition models 估计和测试矩条件模型中的平稳结构变化

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105896

Haiqi Li , Jin Zhou , Yongmiao Hong

{"title":"Estimating and testing for smooth structural changes in moment condition models","authors":"Haiqi Li , Jin Zhou , Yongmiao Hong","doi":"10.1016/j.jeconom.2024.105896","DOIUrl":"10.1016/j.jeconom.2024.105896","url":null,"abstract":"<div><div>Numerous studies have been devoted to estimating and testing for moment condition models. Most existing studies assume that structural parameters are either fixed or change abruptly over time. This study considers estimating and testing for smooth structural changes in moment condition models where the data-generating process is locally stationary. A novel local generalized method of moments estimator and its boundary-corrected counterpart are proposed to estimate the smoothly changing parameters. Consistency and asymptotic normality are established, and an optimal weighting matrix and its consistent estimator are obtained. Moreover, we propose a consistent test to detect both smooth changes and abrupt breaks, as well as a consistent test for a parametric functional form of time-varying parameters. The tests are asymptotically pivotal and do not require prior information about the alternatives. Monte Carlo simulation studies show that the proposed estimators and tests have superior finite-sample performance. In an empirical application, we document the time-varying features of the risk aversion parameter in an asset pricing model, indicating that investors’ risk aversion is counter-cyclical.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105896"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701256","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

Multivariate spatiotemporal models with low rank coefficient matrix 低秩系数矩阵多变量时空模型

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105897

Dan Pu , Kuangnan Fang , Wei Lan , Jihai Yu , Qingzhao Zhang

{"title":"Multivariate spatiotemporal models with low rank coefficient matrix","authors":"Dan Pu , Kuangnan Fang , Wei Lan , Jihai Yu , Qingzhao Zhang","doi":"10.1016/j.jeconom.2024.105897","DOIUrl":"10.1016/j.jeconom.2024.105897","url":null,"abstract":"<div><div>Multivariate spatiotemporal data arise frequently in practical applications, often involving complex dependencies across cross-sectional units, time points and multivariate variables. In the literature, few studies jointly model the dependence in three dimensions. To simultaneously model the cross-sectional, dynamic and cross-variable dependence, we propose a multivariate reduced-rank spatiotemporal model. By imposing the low-rank assumption on the spatial influence matrix, the proposed model achieves substantial dimension reduction and has a nice interpretation, especially for financial data. Due to the innate endogeneity, we propose the quasi-maximum likelihood estimator (QMLE) to estimate the unknown parameters. A ridge-type ratio estimator is also developed to determine the rank of the spatial influence matrix. We establish the asymptotic distribution of the QMLE and the rank selection consistency of the ridge-type ratio estimator. The proposed methodology is further illustrated via extensive simulation studies and two applications to a stock market dataset and an air pollution dataset.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105897"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656946","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

Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models 半参数时间序列模型的伪方差准极大似然估计

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105894

Mirko Armillotta , Paolo Gorgi

{"title":"Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models","authors":"Mirko Armillotta , Paolo Gorgi","doi":"10.1016/j.jeconom.2024.105894","DOIUrl":"10.1016/j.jeconom.2024.105894","url":null,"abstract":"<div><div>We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain parametric restrictions with respect to the conditional expectation. The specification of the pseudo-variance and the parametric restrictions follow naturally in observation-driven models with bounds in the support of the observable process, such as count processes and double-bounded time series. We derive the asymptotic properties of the estimators and a validity test for the parameter restrictions. We show that the results remain valid irrespective of the correct specification of the pseudo-variance. The key advantage of the restricted estimators is that they can achieve higher efficiency compared to alternative quasi-likelihood methods that are available in the literature. Furthermore, the testing approach can be used to build specification tests for parametric time series models. We illustrate the practical use of the methodology in a simulation study and two empirical applications featuring integer-valued autoregressive processes, where assumptions on the dispersion of the thinning operator are formally tested, and autoregressions for double-bounded data with application to a realized correlation time series.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105894"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723000","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

Validating approximate slope homogeneity in large panels 验证大型面板中的近似斜率同质性

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105898

Tim Kutta , Holger Dette

引用次数: 0

Variable selection in high dimensional linear regressions with parameter instability

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105900

Alexander Chudik , M. Hashem Pesaran , Mahrad Sharifvaghefi

引用次数: 0

Consistent causal inference for high-dimensional time series 高维时间序列的一致因果推理

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105902

Francesco Cordoni, Alessio Sancetta

引用次数: 0

GLS under monotone heteroskedasticity 单调异方差下的 GLS

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-11-01 DOI: 10.1016/j.jeconom.2024.105899

Yoichi Arai , Taisuke Otsu , Mengshan Xu

引用次数: 0

Inference in predictive quantile regressions 预测性量化回归的推论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-10-01 DOI: 10.1016/j.jeconom.2024.105875

Alex Maynard , Katsumi Shimotsu , Nina Kuriyama

{"title":"Inference in predictive quantile regressions","authors":"Alex Maynard , Katsumi Shimotsu , Nina Kuriyama","doi":"10.1016/j.jeconom.2024.105875","DOIUrl":"10.1016/j.jeconom.2024.105875","url":null,"abstract":"<div><div>This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) <span><math><mi>t</mi></math></span>-statistic in terms of functionals of Ornstein–Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors – the dividend price ratio, earnings price ratio, and book-to-market ratio – to predict the median, shoulders, and tails of the stock return distribution.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"245 1","pages":"Article 105875"},"PeriodicalIF":9.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651238","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

On the spectral density of fractional Ornstein–Uhlenbeck processes 论分数奥恩斯坦-乌伦贝克过程的谱密度

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-10-01 DOI: 10.1016/j.jeconom.2024.105872

Shuping Shi , Jun Yu , Chen Zhang

{"title":"On the spectral density of fractional Ornstein–Uhlenbeck processes","authors":"Shuping Shi , Jun Yu , Chen Zhang","doi":"10.1016/j.jeconom.2024.105872","DOIUrl":"10.1016/j.jeconom.2024.105872","url":null,"abstract":"<div><div>This paper introduces a novel and easy-to-implement method for accurately approximating the spectral density of discretely sampled fractional Ornstein–Uhlenbeck (fOU) processes. The method offers a substantial reduction in approximation error, particularly within the rough region of the fractional parameter <span><math><mrow><mi>H</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>.</mo><mn>5</mn><mo>)</mo></mrow></mrow></math></span>. This approximate spectral density has the potential to enhance the performance of estimation methods and hypothesis testing that make use of spectral densities. We introduce the approximate Whittle maximum likelihood (AWML) method for discretely sampled fOU processes, utilizing the approximate spectral density, and demonstrate that the AWML estimator exhibits properties of consistency and asymptotic normality when <span><math><mrow><mi>H</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>, akin to the conventional Whittle maximum likelihood method. Through extensive simulation studies, we show that AWML outperforms existing methods in terms of estimation accuracy in finite samples. We then apply the AWML method to the trading volume of 40 financial assets. Our empirical findings reveal that the estimated Hurst parameters for these assets fall within the range of 0.10 to 0.21, indicating a rough dynamic.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"245 1","pages":"Article 105872"},"PeriodicalIF":9.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528271","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