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It compares pooled OLS, fixed effects, Anderson–Hsiao, and Arellano–Bond with Ridge, LASSO, Elastic Net, empirical Bayes maximum likelihood, and Kwon’s unbiased risk estimation. Monte Carlo results show substantial out-of-sample gains from shrinkage methods. An application using Compustat firm-level leverage dynamics further confirms improved corporate finance forecasting while preserving the structural framework.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-and-shrinkage-in-dynamic-panel-forecasting-research-paper/290502/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-and-shrinkage-in-dynamic-panel-forecasting-research-paper/290502.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Which forecasting estimators does the paper compare?","Question",{"text":113,"@type":114},"It compares pooled OLS, fixed effects, Anderson–Hsiao, and Arellano–Bond against shrinkage and regularization methods such as Ridge, LASSO, Elastic Net, empirical Bayes maximum likelihood, and unbiased risk estimation.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What do the Monte Carlo results show?",{"text":118,"@type":114},"Monte Carlo evidence indicates that shrinkage methods substantially improve out-of-sample forecasting accuracy compared with conventional estimators.",{"name":120,"@type":111,"acceptedAnswer":121},"How is the empirical application conducted and what does it support?",{"text":122,"@type":114},"The paper applies the approach to firm-level leverage dynamics using Compustat data, confirming that the forecasting gains found in simulations are relevant for corporate finance while keeping the structural framework intact.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290502,1789641850,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","MACHINE LEARNING AND SHRINKAGE IN DYNAMIC PANEL FORECASTING  \nMagdalena Cornejo (Universidad Torcuato Di Tella-CONICET) Walter Sosa-Escudero (Universidad de San Andrés-CONICET)  \nPrimera versión: Mayo 2026  \nEsta versión: Mayo 2026  \nDocumento de Trabajo Nº 183 Departamento de Economía Universidad de San Andrés  \nVito Dumas 284, B1644BID, Victoria, San Fernando, Buenos Aires, Argentina Teléfono +54 11 7078 0400  \nEmail: [economia@udesa.edu.ar](economia@udesa.edu.ar)  \nMachine Learning and Shrinkage in Dynamic Panel  \nForecasting  \nMagdalena Cornejo∗ Walter Sosa-Escudero†  \nUniversidad Torcuato Di Tella-CONICET Universidad de San Andr´es-CONICET  \nMay 2026  \nAbstract  \nThis paper studies forecasting in dynamic panel data models with fixed effects. We compare the forecasting accuracy of conventional estimators— pooled OLS, fixed effects, Anderson–Hsiao, and Arellano–Bond—against shrinkage and regularization methods such as Ridge, LASSO, Elastic Net, empirical Bayes maximum likelihood and the recent unbiased risk estimation of Kwon (2026) . Monte Carlo evidence shows that shrinkage methods substantially improve out-of-sample accuracy. An empirical application to firm-level leverage dynamics using Compustat data confirms the relevance of these findings for forecasting in corporate finance. Machine learning regularization can improve forecasting performance in dynamic panel settings while preserving the structural framework.  \nKeywords: Forecasting, Dynamic panel data, Machine learning, Regularization, Corporate finance.  \nJEL Codes: C53, C58 .  \n∗ Corresponding author: School of Business, Universidad Torcuato Di Tella and CONICET, email: [mcornejo@utdt.edu](mcornejo@utdt.edu. 7350 Figueroa Alcorta Av)[. 7350 Figueroa Alcorta Av](mcornejo@utdt.edu. 7350 Figueroa Alcorta Av)., Buenos Aires, Argentina. +54 11 5169 7156.  \nReplication files and code are available at [https://github.com/magdalenacornejo/](https://github.com/magdalenacornejo/)[ ](https://github.com/magdalenacornejo/)[forecasting-dynamic-panels](forecasting-dynamic-panels)  \n†Department of Economics, Universidad de San Andr´es and CONICET. Email: [wsosa@udesa.edu.ar](wsosa@udesa.edu.ar)  \n1 Introduction  \nFixed effects occupy a central place in the analysis of panel data. In static specifications, controlling for fixed effects eliminates biases from unobserved, time-invariant heterogeneity, which explains their ubiquity in empirical research. However, in dynamic settings the presence of lagged dependent variables renders conventional strategies, such as within or first-difference transformations, inconsistent, a result formally established by Nickell (1981) . This creates a fundamental tension: while fixed effects are essential to capture persistent heterogeneity, their estimation becomes problematic in dynamic environments, especially when the time dimension is limited. The econometric literature has proposed several solutions to this problem. Anderson and Hsiao (1981) introduced the use of valid instruments that arise naturally under firstdifferencing, an approach subsequently exploited in the GMM framework by Arellano and Bond (1991) and extended by Ahn and Schmidt (1995), Blundell and Bond (1998), and Kiviet (1995); Baltagi (2021) provide a recent, comprehensive overview of these ideas.  \nWhile these estimators address consistency, their small-sample performance has been the subject of considerable scrutiny, for two reasons. First, the so-called Nickell bias decreases as the time dimension T grows, but remains empirically relevant for realistic sample sizes. Second, the large-sample properties of instrumental variable estimators often fail to materialize in small samples, and in some cases may even reverse. To the point, the well-known simulation study of Judson and Owen (1999) reports substantial biases persisting even for T = 30, and that, in small samples, strategies that use few instruments (like the Anderson-Hsiao estimator) perform better than those that exploi","cbCairN44Ig6IXaS","https://ap.wps.com/l/cbCairN44Ig6IXaS","pdf",523638,29,"English","# Abstract\n# Keywords\n# Introduction\n## Fixed effects in static vs dynamic settings\n## Bias–variance trade-off for prediction\n## Regularization and shrinkage approaches\n## Scope and evaluation strategy","[{\"question\":\"Which forecasting estimators does the paper compare?\",\"answer\":\"It compares pooled OLS, fixed effects, Anderson–Hsiao, and Arellano–Bond against shrinkage and regularization methods such as Ridge, LASSO, Elastic Net, empirical Bayes maximum likelihood, and unbiased risk estimation.\"},{\"question\":\"What do the Monte Carlo results show?\",\"answer\":\"Monte Carlo evidence indicates that shrinkage methods substantially improve out-of-sample forecasting accuracy compared with conventional estimators.\"},{\"question\":\"How is the empirical application conducted and what does it support?\",\"answer\":\"The paper applies the approach to firm-level leverage dynamics using Compustat data, confirming that the forecasting gains found in simulations are relevant for corporate finance while keeping the structural framework intact.\"}]","Machine Learning and Shrinkage in Dynamic Panel Forecasting - Research Paper | PDF",73]