[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127807-en":3,"doc-seo-127807-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127807,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Essays in Econometrics and Machine Learning - dissertation for PhD","This PhD dissertation addresses econometric challenges by developing estimation and inference methods that leverage machine learning techniques across three chapters. It introduces computationally efficient batched gradient descent estimators for large-dimensional monotone index models, including semiparametric variants with kernel or sieve components. It then proposes a subsample-and-iteration procedure generalizing mini-batch gradient descent for extremely large samples with reduced computation while preserving statistical accuracy. Finally, it develops robust panel-data inference for treatment effects using quantile random forests and quantile control methods, supported by asymptotic results, Monte Carlo evidence, and an empirical application.","ESSAYS IN ECONOMETRICS AND MACHINE LEARNING  \nQingsong Yao  \nA dissertation (for PhD)  \nsubmitted to the Faculty of  \nthe department of Economics  \nin partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nBoston College  \nMorrissey College of Arts and Sciences Graduate School  \nMarch 2024  \n©Copyright 2024 Qingsong Yao  \nESSAYS IN ECONOMETRICS AND MACHINE  \nLEARNING  \nQingsong Yao  \nAdvisors: Shakeeb Khan, Ph.D.  \nZhijie Xiao, Ph.D.  \nArthur Lewbel, Ph.D.  \nAbstract  \nThis dissertation consists of three chapters demonstrating how the current econometric problems can be solved by using machine learning techniques. In the first chapter, I propose new approaches to estimating large dimensional monotone index models. This class of models has been popular in the applied and theoretical econometrics literatures as it includes discrete choice, nonparametric transformation, and duration models. A main advantage of my approach is computational. For instance, rank estimation procedures such as those proposed in Han (1987) and Cavanagh and Sherman (1998) that optimize a nonsmooth, non convex objective function are difficult to use with more than a few regressors and so limits their use in with economic data sets. For such monotone index models with increasing dimension, we propose to use a new class of estimators based on batched gradient descent (BGD) involving nonparametric methods such as kernel estimation or sieve estimation, and study their asymptotic properties. The BGD algorithm uses an iterative procedure where the key step exploits a strictly convex objective function, resulting in computational advantages. A contribution of my approach is that the model is large dimensional and semiparametric and so does not require the use of parametric distributional assumptions.  \nThe second chapter studies the estimation of semiparametric monotone index models when the sample size n is extremely large and conventional approaches fail to work due to devastating computational burdens. Motivated by the mini-batch gradient descent algorithm (MBGD) that is widely used as a stochastic optimization tool in the machine learning field, this chapter proposes a novel subsample-and iteration-based estimation procedure. In particular, starting from any initial guess  \nof the true parameter, the estimator is progressively updated using a sequence of subsamples randomly drawn from the data set whose sample size is much smaller than n. The update is based on the gradient of some well-chosen loss function, where the nonparametric component in the model is replaced with its Nadaraya-Watson kernel estimator that is also constructed based on the random subsamples. The proposed algorithm essentially generalizes MBGD algorithm to the semiparametric setup. Since the new method uses only a subsample to perform Nadaraya-Watson kernel estimation and conduct the update, compared with the full-sample-based iterative method, the new method reduces the computational time by roughly n times if the subsample size and the kernel function are chosen properly, so can be easily applied when the sample size n is large. Moreover, this chapter shows that if averages are further conducted across the estimators produced during iterations, the difference between the average estimator and full-sample-based estimator will be 1/ √n-trivial. Consequently, the averaged estimator is 1/ √n-consistent and asymptotically normally distributed. In other words, the new estimator substantially improves the computational speed, while at the sametime maintains the estimation accuracy. Finally, extensive Monte Carlo experiments and real data analysis illustrate the excellent performance of novel algorithm in terms of computational efficiency when the sample size is extremely large.  \nFinally, the third chapter studies robust inference procedure for treatment effects in panel data with flexible relationship across units via the random forest method. The key contribution","cbCaijJxBEP4CMRB","https://ap.wps.com/l/cbCaijJxBEP4CMRB","pdf",5050799,3,1,199,"English","en",105,"# Contents\n## 1 Estimating High Dimensional Monotone Index Models\n## 2 Stochastic Learning\n## 3 Robust Inference for Treatment Effects in Panel Data","[{\"question\":\"What is the main goal of this dissertation?\",\"answer\":\"To demonstrate how current econometric problems can be solved using machine learning techniques, with new estimation and inference procedures across three chapters.\"},{\"question\":\"How does the dissertation handle large-dimensional monotone index models?\",\"answer\":\"It proposes batched gradient descent estimators, including semiparametric versions that use kernel estimation or sieve estimation, and studies their computational and asymptotic properties.\"},{\"question\":\"What method is used for robust inference of treatment effects in panel data?\",\"answer\":\"It uses random forest–based quantile methods: a quantile control method built on quantile random forest to construct prediction intervals and to support asymptotic consistency, validated by simulations and an empirical study.\"}]","Essays in Econometrics and Machine Learning - 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