{"title":"Tractable bayesian estimation of smooth transition vector autoregressive models","authors":"Martin Bruns, Michele Piffer","doi":"10.1093/ectj/utae009","DOIUrl":"https://doi.org/10.1093/ectj/utae009","url":null,"abstract":"\u0000 We develop a tractable way of estimating the parameters ruling the nonlinearity in the popular Smooth Transition VAR model, and identify structural shocks using external instruments. This jointly offers an alternative to the option of identifying shocks recursively and calibrating key parameters. In an illustration, we show that monetary policy shocks generate larger effects on economic activity during economic expansions compared to economic recessions. We then document that calibrating rather than estimating the parameters ruling the nonlinearity of the model can lead to values for which the key results are lost. This suggests caution in the calibration of these parameters.","PeriodicalId":514887,"journal":{"name":"The Econometrics Journal","volume":"27 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966664","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}
{"title":"Correction to: Causal inference and data fusion in econometrics","authors":"","doi":"10.1093/ectj/utae008","DOIUrl":"https://doi.org/10.1093/ectj/utae008","url":null,"abstract":"","PeriodicalId":514887,"journal":{"name":"The Econometrics Journal","volume":"143 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746517","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}
{"title":"Using a Satisficing Model of Experimenter Decision-Making to Guide Finite-Sample Inference for Compromised Experiments.","authors":"James J Heckman, Ganesh Karapakula","doi":"10.1093/ectj/utab009","DOIUrl":"https://doi.org/10.1093/ectj/utab009","url":null,"abstract":"<p><p>This paper presents a simple decision-theoretic economic approach for analyzing social experiments with compromised random assignment protocols that are only partially documented. We model administratively constrained experimenters who satisfice in seeking covariate balance. We develop design-based small-sample hypothesis tests that use worst-case (least favorable) randomization null distributions. Our approach accommodates a variety of compromised experiments, including imperfectly documented re-randomization designs. To make our analysis concrete, we focus much of our discussion on the influential Perry Preschool Project. We reexamine previous estimates of program effectiveness using our methods. The choice of how to model reassignment vitally affects inference.</p>","PeriodicalId":514887,"journal":{"name":"The Econometrics Journal","volume":"24 2","pages":"C1-C39"},"PeriodicalIF":1.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478285/pdf/nihms-1690015.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39474567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Econometrics JournalPub Date : 2020-09-29eCollection Date: 2021-01-01DOI: 10.1093/ectj/utaa030
Rong Zhu, Xinyu Zhang, Yanyuan Ma, Guohua Zou
{"title":"Model averaging estimation for high-dimensional covariance matrices with a network structure.","authors":"Rong Zhu, Xinyu Zhang, Yanyuan Ma, Guohua Zou","doi":"10.1093/ectj/utaa030","DOIUrl":"https://doi.org/10.1093/ectj/utaa030","url":null,"abstract":"<p><p>In this paper, we develop a model averaging method to estimate a high-dimensional covariance matrix, where the candidate models are constructed by different orders of polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove the asymptotic optimality of the resulting model average covariance estimators. Finally, we conduct numerical simulations and a case study on Chinese airport network structure data to demonstrate the usefulness of the proposed approaches.</p>","PeriodicalId":514887,"journal":{"name":"The Econometrics Journal","volume":"24 1","pages":"177-197"},"PeriodicalIF":1.9,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25500527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}