发布求助
文献互助 智能选刊 最新文献

Journal of Econometrics最新文献

筛选
英文 中文
Shrinkage estimators for periodic autoregressions 周期自回归的收缩估计量
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105937
Richard Paap, Philip Hans Franses
{"title":"Shrinkage estimators for periodic autoregressions","authors":"Richard Paap,&nbsp;Philip Hans Franses","doi":"10.1016/j.jeconom.2024.105937","DOIUrl":"10.1016/j.jeconom.2024.105937","url":null,"abstract":"<div><div>A periodic autoregression [PAR] is a seasonal time series model where the autoregressive parameters vary over the seasons. A drawback of PAR models is that the number of parameters increases dramatically when the number of seasons gets large. Hence, one needs many periods with intra-seasonal data to be able to get reliable parameter estimates. Therefore, these models are rarely applied for weekly or daily observations. In this paper we propose shrinkage estimators which shrink the periodic autoregressive parameters to a common value determined by the data. We derive the asymptotic properties of these estimators in case of a quadratic penalty and we illustrate the bias–variance trade-off. Empirical illustrations show that shrinkage improves forecasting with PAR models.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105937"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181087","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
Individual welfare analysis: Random quasilinear utility, independence, and confidence bounds 个人福利分析：随机拟线性效用、独立性和置信限
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105927
Junlong Feng , Sokbae Lee
{"title":"Individual welfare analysis: Random quasilinear utility, independence, and confidence bounds","authors":"Junlong Feng ,&nbsp;Sokbae Lee","doi":"10.1016/j.jeconom.2024.105927","DOIUrl":"10.1016/j.jeconom.2024.105927","url":null,"abstract":"<div><div>We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a novel confidence set under an independence restriction. This confidence set is computationally simple and robust to weak instruments, nonlinearity, and partial identification. The validity of the confidence set is guaranteed by our new results on the joint limiting distribution of the independence test by Chatterjee (2021). These results together with the confidence set may have applications beyond welfare analysis. Monte Carlo simulations and two empirical applications on gasoline and food demand demonstrate the effectiveness of our method.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105927"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181082","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
Natural disasters as macroeconomic tail risks 自然灾害作为宏观经济尾部风险
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105914
Sulkhan Chavleishvili , Emanuel Moench
{"title":"Natural disasters as macroeconomic tail risks","authors":"Sulkhan Chavleishvili ,&nbsp;Emanuel Moench","doi":"10.1016/j.jeconom.2024.105914","DOIUrl":"10.1016/j.jeconom.2024.105914","url":null,"abstract":"<div><div>We introduce quantile and moment impulse response functions for structural quantile vector autoregressive models. We use them to study how climate-related natural disasters affect the predictive distribution of output growth and inflation. Disasters strongly shift the forecast distribution particularly in the tails. They result in an initial sharp increase of the downside risk for growth, followed by a temporary rebound. Upside risk to inflation increases markedly for a few months and then subsides. As a result, natural disasters have a persistent impact on the conditional variance and skewness of macroeconomic aggregates which standard linear models estimating conditional mean dynamics fail to match. We perform a scenario analysis to evaluate the hypothetical effects of more frequent large disasters on the macroeconomy due to increased atmospheric carbon concentration. Our results indicate a substantially higher conditional volatility of growth and inflation as well as increased upside risk to inflation particularly in a scenario where only currently pledged climate policies are implemented.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105914"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181089","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
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 ,&nbsp;Jin Zhou ,&nbsp;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
From LATE to ATE: A Bayesian approach 从LATE到ATE：贝叶斯方法
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
{"title":"From LATE to ATE: A Bayesian approach","authors":"Isaac M. Opper","doi":"10.1016/j.jeconom.2024.105895","DOIUrl":"10.1016/j.jeconom.2024.105895","url":null,"abstract":"<div><div>We develop a Bayesian model that produces a posterior distribution of the marginal treatment effect (MTE) function. The method provides researchers with a principled way to extrapolate from the observed moments using flexible assumptions, thereby allowing researchers to generate plausible ranges of important and potentially policy-relevant quantities of interest. We then use the model to propose a natural decomposition of the posterior variance into “statistical uncertainty,” i.e., variance that stems from the imprecise estimation of the observed moments, and “extrapolation uncertainty,” i.e., variance that stems from uncertainty in how to extrapolate away from the observed moments. We conclude by showing that under our preferred priors, even in an experiment as large as the Oregon Health Insurance Experiment, the main source of uncertainty in the ATE comes from uncertainty in the true values of the observed moments.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105895"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748725","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
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 ,&nbsp;Kuangnan Fang ,&nbsp;Wei Lan ,&nbsp;Jihai Yu ,&nbsp;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 ,&nbsp;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
{"title":"Validating approximate slope homogeneity in large panels","authors":"Tim Kutta ,&nbsp;Holger Dette","doi":"10.1016/j.jeconom.2024.105898","DOIUrl":"10.1016/j.jeconom.2024.105898","url":null,"abstract":"<div><div>In this paper, we introduce new inference methods for slope homogeneity in large regression panels. While most existing tests are developed for the hypothesis of <em>slope homogeneity</em> (equality of all individual slopes), we propose to test the more realistic relaxation of <em>approximate slope homogeneity</em> (similarity of all slopes). We present new test statistics for dense and sparse alternatives to approximate homogeneity. In the dense setting, the main focus of this paper, we develop statistics that converge to pivotal limits even under simultaneous temporal and intersectional dependence. We also demonstrate uniform consistency of these statistics against large classes of local alternatives. As a complementary diagnostic tool, we propose tests against sparse alternatives that are sensitive to excessive heterogeneity in a minority of slopes. Such tests can play an important role in the analysis of populations with diverse but small subgroups. A simulation study and a data example underline the usefulness of our approach.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105898"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701257","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
Variable selection in high dimensional linear regressions with parameter instability 具有参数不稳定性的高维线性回归的变量选择
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
{"title":"Variable selection in high dimensional linear regressions with parameter instability","authors":"Alexander Chudik ,&nbsp;M. Hashem Pesaran ,&nbsp;Mahrad Sharifvaghefi","doi":"10.1016/j.jeconom.2024.105900","DOIUrl":"10.1016/j.jeconom.2024.105900","url":null,"abstract":"<div><div>This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also investigated, and in-sample fit of the selected regression is shown to have the oracle property. The theoretical results support the use of unweighted observations at the selection stage of OCMT, whilst applying down-weighting of observations only at the forecasting stage. Monte Carlo and empirical applications show that OCMT without down-weighting at the selection stage yields smaller mean squared forecast errors compared to Lasso, Adaptive Lasso, and boosting.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105900"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748724","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
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
{"title":"Consistent causal inference for high-dimensional time series","authors":"Francesco Cordoni,&nbsp;Alessio Sancetta","doi":"10.1016/j.jeconom.2024.105902","DOIUrl":"10.1016/j.jeconom.2024.105902","url":null,"abstract":"<div><div>A methodology for high-dimensional causal inference in a time series context is introduced. Time series dynamics are captured by a Gaussian copula, and estimation of the marginal distribution of the data is not required. The procedure can consistently identify the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high-dimensional variables, under sparsity conditions. Identification of the causal relations is in the form of a directed acyclic graph, which is equivalent to identifying the structural VAR model for the transformed variables. As illustrative applications, we consider the impact of supply-side oil shocks on the economy and the causal relations between aggregated variables constructed from the limit order book for four stock constituents of the S&amp;P500.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"246 1","pages":"Article 105902"},"PeriodicalIF":9.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723001","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
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信