[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118337-en":3,"doc-seo-118337-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118337,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Double Machine Learning for Static Panel Models with Fixed Effects - Paper","Double Machine Learning for Static Panel Models with Fixed Effects develops new double machine learning (DML) procedures for panel data, using machine learning to approximate high-dimensional and nonlinear nuisance functions with unknown forms. The work extends correlated random effects, within-group, and first-difference estimators to nonlinear panel settings based on a fixed-effects version of Robinson’s partially linear regression model. Simulations compare multiple ML algorithms, and an application re-estimates the effect of minimum wage on UK voting behavior, recommending first-differencing and ensemble learning.","Manuscript accepted at The Econometrics Journal, pp. 1–19.  \nDouble Machine Learning for Static Panel Models  \nwith Fixed Effects  \nPaul S. Clarke† and Annalivia Polselli‡  \n†Institute for Social and Economic Research, University of Essex, Colchester CO4 3SQ, UK.  \nE-mail: [pclarke@essex.ac.uk](pclarke@essex.ac.uk)  \n‡Institute for Analytics and Data Science, University of Essex, Colchester CO4 3ZL, UK.  \nE-mail: [annalivia.polselli@essex.ac.uk](annalivia.polselli@essex.ac.uk)  \nSummary Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning (DML) procedures for panel data in which these algorithms are used to approximate high-dimensional and nonlinear nuisance functions of the covariates. Our new procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators from linear to nonlinear panel models, specifically, Robinson (1988)’s partially linear regression model with fixed effects and unspecified nonlinear confounding. Our simulation study assesses the performance of these procedures using different machine learning algorithms. We use our procedures to re-estimate the impact of minimum wage on voting behaviour in the UK. From our results, we recommend the use of first-differencing because it imposes the fewest constraints on the distribution of the fixed effects, and an ensemble learning strategy to ensure optimum estimator accuracy.  \nKeywords: CART, homogeneous treatment effect, hyperparameter tuning, LASSO, random forest.  \n1. INTRODUCTION  \nRecent advances in the econometric literature on Machine Learning (ML) use the power of ML algorithms, widely used in data science for solving prediction problems, to enhance existing estimation procedures for treatment and other kinds of causal effect. Notable developments include novel ML algorithms for causal analysis such as Causal Trees by Athey and Imbens (2016), Causal Forests by Wager and Athey (2018) and Generalised Random Forests by Athey et al. (2019) . However, the key development, as far as this paper is concerned, is Double/Debiased Machine Learning (DML) by Chernozhukov et al.(2018) wherein ML is used to learn nuisance functions with ex ante unknown functional forms, and the predicted values of these functions used to construct (orthogonalized) scores for the interest parameters from which consistent and asymptotically normal estimators can be obtained. DML is a very general estimation framework but there are limited examples of its application to panel data, notable examples of which include Chang (2020), Klosin and Vilgalys (2023), and Semenova et al. (2023) .  \nIn this paper, we develop and assess novel DML procedures for estimating treatment (or causal) effects from panel data with fixed effects. The procedures we propose are extensions of the correlated random effects (CRE), within-group (WG) and first-difference (FD) estimators commonly used for linear models to scenarios where the underlying model is non-linear. Specifically, these are based on an extension of the partially linear regression (PLR) model proposed by Robinson (1988) to panel data through the inclusion of time-varying predictors and unobserved individual heterogeneity (i.e. individual fixed effects) .  \n2 P. S. Clarke and A. Polselli  \nOur methodological contribution is twofold and complementary to the recent work on causal panel data estimation using DML by Chang (2020) on difference-in-differences, Klosin and Vilgalys (2023) and Semenova et al. (2023) on high-dimensional treatment heterogeneity in model with fixed effects.  \nA first contribution is that the procedures we develop are not based on ex ante taking the nuisance functions or fixed effects to be accurately approximated by high-dimensional sparse functions. Thus, we do not focus solely on the Least Absolute Shrinkage and Selection Operator (LASSO)","cbCaio5eQ0BD3KmX","https://ap.wps.com/l/cbCaio5eQ0BD3KmX","pdf",443527,1,19,"English","en",105,"# Introduction\n## Methodological contribution\n### ML nuisance approximation and estimator accuracy\n### Block-k-fold cross-fitting for panel dependence","[{\"question\":\"What is the main goal of the proposed DML procedures?\",\"answer\":\"To estimate treatment (causal) effects in panel data with fixed effects by learning unknown nuisance functions using machine learning, then constructing orthogonalized scores for valid inference.\"},{\"question\":\"How do the new methods extend existing estimators for panel models?\",\"answer\":\"They extend correlated random effects, within-group, and first-difference estimators from linear models to nonlinear panel settings by building on a fixed-effects adaptation of Robinson’s partially linear regression model.\"},{\"question\":\"What do simulations and the empirical application suggest about implementation choices?\",\"answer\":\"The results recommend first-differencing because it imposes fewer constraints on the fixed effects distribution, and an ensemble learning strategy to improve estimator accuracy across datasets.\"}]","Double Machine Learning for Static Panel Models with Fixed Effects - 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