ERN: Other Econometrics: Econometric Model Construction最新文献

Rank Determination in Tensor Factor Model 张量因子模型的等级确定

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2020-11-13 DOI: 10.1214/22-EJS1991

Yuefeng Han, Cun-Hui Zhang, Rong Chen

{"title":"Rank Determination in Tensor Factor Model","authors":"Yuefeng Han, Cun-Hui Zhang, Rong Chen","doi":"10.1214/22-EJS1991","DOIUrl":"https://doi.org/10.1214/22-EJS1991","url":null,"abstract":"Factor model is an appealing and effective analytic tool for high-dimensional time series, with a wide range of applications in economics, finance and statistics. One of the fundamental issues in using factor model for time series in practice is the determination of the number of factors to use. This paper develops two criteria for such a task for tensor factor models where the signal part of an observed time series in tensor form assumes a Tucker decomposition with the core tensor as the factor tensor. The task is to determine the dimensions of the core tensor. One of the proposed criteria is similar to information based criteria of model selection, and the other is an extension of the approaches based on the ratios of consecutive eigenvalues often used in factor analysis for panel time series. The new criteria are designed to locate the gap between the true smallest non-zero eigenvalue and the zero eigenvalues of a functional of the population version of the auto-cross-covariances of the tensor time series using their sample versions. As sample size and tensor dimension increase, such a gap increases under regularity conditions, resulting in consistency of the rank estimator. The criteria are built upon the existing non-iterative and iterative estimation procedures of tensor factor model, yielding different performances. We provide sufficient conditions and convergence rate for the consistency of the criteria as the sample size $T$ and the dimensions of the observed tensor time series go to infinity. The results include the vector factor models as special cases, with an additional convergence rates. The results also include the cases when there exist factors with different signal strength. In addition, the convergence rates of the eigenvalue estimators are established. Simulation studies provide promising finite sample performance for the two criteria.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

A Toolkit for Robust Risk Assessment Using F-Divergences 基于f -散度的稳健风险评估工具

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2020-08-25 DOI: 10.2139/ssrn.3680475

T. Kruse, Judith C. Schneider, Nikolaus Schweizer

引用次数: 1

One-factor Hull-White Model Calibration for CVA - Part I: Instrument Selection With a Kink CVA的单因素Hull-White模型校准-第一部分:带扭结的仪器选择

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2020-07-09 DOI: 10.2139/ssrn.3659430

Christoph M. Puetter, Stefano Renzitti

引用次数: 0

High-Dimensional Granger Causality Tests with an Application to VIX and News 基于VIX和News的高维Granger因果检验

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2020-05-29 DOI: 10.2139/ssrn.3615718

Andrii Babii, Eric Ghysels, Jonas Striaukas

引用次数: 14

Selective Linear Segmentation For Detecting Relevant Parameter Changes 选择性线性分割检测相关参数的变化

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2019-08-20 DOI: 10.2139/ssrn.3461554

A. Dufays, Elysée Aristide Houndetoungan, Alain Coen

引用次数: 1

When Simplicity Offers a Benefit, Not a Cost: Closed-Form Estimation of the GARCH(1,1) Model that Enhances the Efficiency of Quasi-Maximum Likelihood 当简单带来好处而不是代价:提高拟极大似然效率的GARCH(1,1)模型的封闭估计

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2019-02-15 DOI: 10.17016/FEDS.2019.030

Todd Prono

{"title":"When Simplicity Offers a Benefit, Not a Cost: Closed-Form Estimation of the GARCH(1,1) Model that Enhances the Efficiency of Quasi-Maximum Likelihood","authors":"Todd Prono","doi":"10.17016/FEDS.2019.030","DOIUrl":"https://doi.org/10.17016/FEDS.2019.030","url":null,"abstract":"Simple, multi-step estimators are developed for the popular GARCH(1,1) model, where these estimators are either available entirely in closed form or dependent upon a preliminary estimate from, for example, quasi-maximum likelihood. Identification sources to asymmetry in the model's innovations, casting skewness as an instrument in a linear, two-stage least squares estimator. Properties of regular variation coupled with point process theory establish the distributional limits of these estimators as stable, though highly non-Gaussian, with slow convergence rates relative to the ??n-case. Moment existence criteria necessary for these results are consistent with the heavy-tailed features of many financial returns. In light-tailed cases that support asymptotic normality for these simple estimators, conditions are discovered where the simple estimators can enhance the asymptotic efficiency of quasi-maximum likelihood estimation. In small samples, extensive Monte Carlo experime nts reveal these efficiency enhancements to be available for (very) heavy tailed cases. Consequently, the proposed simple estimators are members of the class of multi-step estimators aimed at improving the efficiency of the quasi-maximum likelihood estimator.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130486982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Currency Unions and Trade: A PPML Re‐Assessment with High‐Dimensional Fixed Effects 货币联盟与贸易:具有高维固定效应的PPML再评估

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2018-11-29 DOI: 10.1111/obes.12283

Mario Larch, Joschka Wanner, Y. Yotov, Thomas Zylkin

{"title":"Currency Unions and Trade: A PPML Re‐Assessment with High‐Dimensional Fixed Effects","authors":"Mario Larch, Joschka Wanner, Y. Yotov, Thomas Zylkin","doi":"10.1111/obes.12283","DOIUrl":"https://doi.org/10.1111/obes.12283","url":null,"abstract":"Recent work on the effects of currency unions (CUs) on trade stresses the importance of using many countries and years in order to obtain reliable estimates. However, for large samples, computational issues associated with the three‐way (exporter‐time, importer‐time, and country pair) fixed effects currently recommended in the gravity literature have heretofore limited the choice of estimator, leaving an important methodological gap. To address this gap, we introduce an iterative poisson pseudo‐maximum likelihood (PPML) estimation procedure that facilitates the inclusion of these fixed effects for large data sets and also allows for correlated errors across countries and time. When applied to a comprehensive sample with more than 200 countries trading over 65 years, these innovations flip the conclusions of an otherwise rigorously specified linear model. Most importantly, our estimates for both the overall CU effect and the Euro effect specifically are economically small and statistically insignificant. We also document that linear and PPML estimates of the Euro effect increasingly diverge as the sample size grows.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123123195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 142

Empirical Validation of Agent-Based Models 基于agent的模型的实证验证

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2018-11-12 DOI: 10.1016/BS.HESCOM.2018.02.003

T. Lux, Remco C. J. Zwinkels

引用次数: 72

Predictive Regressions Under Asymmetric Loss: Factor Augmentation and Model Selection 非对称损失下的预测回归:因子增强与模型选择

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2018-05-11 DOI: 10.2139/ssrn.3180690

M. Demetrescu, Sinem Hacioglu Hoke

{"title":"Predictive Regressions Under Asymmetric Loss: Factor Augmentation and Model Selection","authors":"M. Demetrescu, Sinem Hacioglu Hoke","doi":"10.2139/ssrn.3180690","DOIUrl":"https://doi.org/10.2139/ssrn.3180690","url":null,"abstract":"This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.","PeriodicalId":106740,"journal":{"name":"ERN: Other Econometrics: Econometric Model Construction","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126586669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Asymptotic Post-Selection Inference for Akaike's Information Criterion Akaike信息准则的渐近后选择推理

ERN: Other Econometrics: Econometric Model Construction Pub Date : 2018-02-01 DOI: 10.2139/ssrn.3167253

Ali Charkhi, G. Claeskens

引用次数: 2