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Simulation error and numerical instability in estimating random coefficient logit demand models 随机系数logit需求模型估计中的仿真误差和数值不稳定性
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2025.105953
Daniel Brunner , Florian Heiss , André Romahn , Constantin Weiser
{"title":"Simulation error and numerical instability in estimating random coefficient logit demand models","authors":"Daniel Brunner ,&nbsp;Florian Heiss ,&nbsp;André Romahn ,&nbsp;Constantin Weiser","doi":"10.1016/j.jeconom.2025.105953","DOIUrl":"10.1016/j.jeconom.2025.105953","url":null,"abstract":"<div><div>The nonlinear GMM-IV estimator of Berry, Levinsohn and Pakes (1995) can suffer from numerical instability resulting in a wide range of parameter estimates and economic implications. This has been reported to depend on technical details such as the choice of the optimization algorithm, starting values, and convergence criteria. We show that numerical approximation errors in the estimator’s moment function are the main driver of this instability. With accurate approximation, the estimation approach is well-behaved. We provide a simple method to determine the required number of simulation draws.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105953"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181085","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 testing for spatial or social network dependence in panel data allowing for network variability 在允许网络变异性的面板数据中测试空间或社会网络依赖性
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105925
Xiaodong Liu , Ingmar R. Prucha
{"title":"On testing for spatial or social network dependence in panel data allowing for network variability","authors":"Xiaodong Liu ,&nbsp;Ingmar R. Prucha","doi":"10.1016/j.jeconom.2024.105925","DOIUrl":"10.1016/j.jeconom.2024.105925","url":null,"abstract":"<div><div>The paper introduces robust generalized Moran <span><math><mi>I</mi></math></span> tests for network-generated cross-sectional dependence in a panel data setting where unit-specific effects can be random or fixed. Network dependence may originate from endogenous variables, exogenous variables, and/or disturbances, and the network dependence is allowed to vary over time. The formulation of the test statistics also aims at accommodating situations where the researcher is unsure about the exact nature of the network. Unit-specific effects are eliminated using the Helmert transformation, which is well known to yield time-orthogonality for linear forms of transformed disturbances. Given the specification of our test statistics, these orthogonality properties also extend to the quadratic forms that underlie our test statistics. This greatly simplifies the expressions for the asymptotic variances of our test statistics and their estimation. Monte Carlo simulations suggest that the generalized Moran <span><math><mi>I</mi></math></span> tests introduced in this paper have the proper size and can provide substantial improvement in robustness when the researcher faces uncertainty about the specification of the network topology.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105925"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181083","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
Modelling large dimensional datasets with Markov switching factor models 用马尔可夫转换因子模型建模大维度数据集
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105919
Matteo Barigozzi , Daniele Massacci
{"title":"Modelling large dimensional datasets with Markov switching factor models","authors":"Matteo Barigozzi ,&nbsp;Daniele Massacci","doi":"10.1016/j.jeconom.2024.105919","DOIUrl":"10.1016/j.jeconom.2024.105919","url":null,"abstract":"<div><div>We study a novel large dimensional approximate factor model with regime changes in the loadings driven by a latent first order Markov process. By exploiting the equivalent linear representation of the model, we first recover the latent factors by means of Principal Component Analysis. We then cast the model in state–space form, and we estimate loadings and transition probabilities through an EM algorithm based on a modified version of the Baum–Lindgren–Hamilton–Kim filter and smoother that makes use of the factors previously estimated. Our approach is appealing as it provides closed form expressions for all estimators. More importantly, it does not require knowledge of the true number of factors. We derive the theoretical properties of the proposed estimation procedure, and we show their good finite sample performance through a comprehensive set of Monte Carlo experiments. The empirical usefulness of our approach is illustrated through three applications to large U.S. datasets of stock returns, macroeconomic variables, and inflation indexes.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105919"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181084","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
Bootstrapping out-of-sample predictability tests with real-time data 用实时数据引导样本外可预测性测试
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105916
Sílvia Gonçalves , Michael W. McCracken , Yongxu Yao
{"title":"Bootstrapping out-of-sample predictability tests with real-time data","authors":"Sílvia Gonçalves ,&nbsp;Michael W. McCracken ,&nbsp;Yongxu Yao","doi":"10.1016/j.jeconom.2024.105916","DOIUrl":"10.1016/j.jeconom.2024.105916","url":null,"abstract":"<div><div>In this paper we develop a block bootstrap approach to out-of-sample inference when real-time data are used to produce forecasts. In particular, we establish its first-order asymptotic validity for West-type (1996) tests of predictive ability in the presence of regular data revisions. This allows the user to conduct asymptotically valid inference without having to estimate the asymptotic variances derived in Clark and McCracken’s (2009) extension of West (1996) when data are subject to revision. Monte Carlo experiments indicate that the bootstrap can provide satisfactory finite sample size and power even in modest sample sizes. We conclude with an application to inflation forecasting that revisits the results in Ang et al. (2007) in the presence of real-time data.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105916"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181090","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
Uniform inference for cointegrated vector autoregressive processes 协整向量自回归过程的一致推理
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105944
Christian Holberg, Susanne Ditlevsen
{"title":"Uniform inference for cointegrated vector autoregressive processes","authors":"Christian Holberg,&nbsp;Susanne Ditlevsen","doi":"10.1016/j.jeconom.2024.105944","DOIUrl":"10.1016/j.jeconom.2024.105944","url":null,"abstract":"<div><div>Uniformly valid inference for cointegrated vector autoregressive processes has so far proven difficult due to certain discontinuities arising in the asymptotic distribution of the least squares estimator. We extend asymptotic results from the univariate case to multiple dimensions and show how inference can be based on these results. Furthermore, we show that lag augmentation and a recent instrumental variable procedure can also yield uniformly valid tests and confidence regions. We verify the theoretical findings and investigate finite sample properties in simulation experiments for two specific examples.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105944"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182077","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
Inference on dynamic systemic risk measures 关于动态系统性风险测度的推论
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2025-01-01 DOI: 10.1016/j.jeconom.2024.105936
Christian Francq, Jean-Michel Zakoïan
{"title":"Inference on dynamic systemic risk measures","authors":"Christian Francq,&nbsp;Jean-Michel Zakoïan","doi":"10.1016/j.jeconom.2024.105936","DOIUrl":"10.1016/j.jeconom.2024.105936","url":null,"abstract":"<div><div>Systemic risk measures (SRM) quantify the risk of a system induced by the possible distress of any of its components. Applications in economics and finance are numerous. We define a general dynamic framework for the risk factors, allowing us to obtain explicit expressions of the corresponding dynamic SRM. We deduce an easy-to-implement statistical approach which, based on semi-parametric assumptions, reduces to estimating univariate location-scale models and to computing (static) quantiles of the residuals. We derive a sound asymptotic theory (including confidence intervals, tests, validity of a residual bootstrap) for major SRM, namely the Conditional VaR (CoVaR) and Delta-CoVaR. Our theoretical results are illustrated via Monte-Carlo experiments and real financial and macroeconomic time series.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"247 ","pages":"Article 105936"},"PeriodicalIF":9.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182078","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
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
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