[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127495-en":3,"doc-seo-127495-105":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},127495,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Essays in Macroeconomics and Machine Learning - Doctoral Thesis","This PhD thesis investigates how machine learning and data science can address macroeconomic questions. It delivers theoretical results by using neural network learning in rational-expectations indeterminate models, yielding stable equilibria that reconcile indeterminacy with rationality and can imply permanent effects from transitory shocks. Empirically, it develops Bayesian Topic Regression for causal inference with text, improving out-of-sample prediction and enabling analysis of how media coverage and central bank communication shape investor attention, market volatility, and information focus.","Essays in Macroeconomics and Machine Learning  \nJulian Ashwin  \nNu􀀎eld College  \nUniversity of Oxford  \nA thesis submitted for the degree of Doctor of Philosophy in Economics Michaelmas 2021  \nAbstract  \nThe unifying theme of this thesis is the use of techniques from machine learning and data science to address questions in macroeconomics. It makes both theoretical contributions by applying neural networks as a learning algorithm in models that are indeterminate under rational expectations, and empirical contributions by developing and applying natural language processing methods to datasets including news media articles and central bank communication.  \nThe 􀀌rst Chapter, Resolving Indeterminacy with Neural Network Learning: Sinks become Sources, aims to make a theoretical contribution to the literature on indeterminacy in rational expectations models. Indeterminacy (i.e. non-uniqueness of equilibrium under rational expectations) is a pervasive and often neglected challenge for macroeconomists. Previous literature on learning in macroeconomics has shown that theequilibria in linear indeterminate models are almost always not learnable. This Chapter examines the equilibrium sophisticated learning agents converge to in models where there is indeterminacy, but it is bounded. This neural network learning converges to astable equilibrium, in which indeterminate regions are sources and determinate regions are sinks. Furthermore, this equilibrium is consistent with Rational Expectations. There are multiple steady states and agents have correct beliefs about transitions between them, which means that transitory shocks can have permanent e􀀋ects. This is demonstrated with an application to a well-known example of indeterminacy: a New Keynesian model with a Zero Lower Bound in interest rates.  \nThe resolution to the challenge of indeterminacy presented here is plausible, as it's based on learnability, and also appealing because the identi􀀌ed equilibrium is globally stable, can have multiple steady states with well-de􀀌ned transitions between them, and passes tests for rationality. It also demonstrates the value of using machine learning, as the neural network acts as a very 􀀍exible function approximator that is fast to train, allowing the use of learnability as an equilibrium selection device in non-linear models. Alternative learning algorithms like Recursive Least Squares yield qualitatively similar results, but are not su􀀎ciently 􀀍exible to pass tests of rationality.  \nChapter 2, Bayesian Topic Regression for Causal Inference has a more methodological focus, developing Bayesian Topic Regression, a model for causal inference with text data. This methodology is then applied in Chapter 3 to help identify a potentially causal e􀀋ect of media coverage on stock price volatility. The Bayesian Topic Regression  \nmodel jointly estimates topics in text documents and a regression using these topics and associated numerical data to predict a response variable. As well as showing that performing text feature extraction and prediction in separate stages can lead to incorrect inference, we benchmark our model on two real-world customer review datasets and show markedly improved out-of-sample prediction in comparison to competing approaches.  \nChapters 3 and 4 use text data to address to empirical questions relating to how agents in the economy acquire information and on what they focus their attention. In Chapter 3  \nFinancial news media and volatility: is there more to newspapers that news?  \nidenti􀀌es a co-movement media coverage in the Financial Times newspaper and a 􀀌rm's intra-day stock price volatility is identi􀀌ed. I argue that part of this co-movement is causal, relying on an identi􀀌cation strategy based on the publication time of the newspaper, controlling for persistence and anticipation e􀀋ects as well as the content of articles using the Bayesian Topic Regression introduced in Chapter 2 . These results are consistent with a salience-based vi","cbCaigmfcmAbNt0L","https://ap.wps.com/l/cbCaigmfcmAbNt0L","pdf",12643293,2,1,252,"English","en",105,"# Abstract\n## Chapter 1: Resolving Indeterminacy with Neural Network Learning: Sinks become Sources\n## Chapter 2: Bayesian Topic Regression for Causal Inference\n## Chapter 3: Financial news media and volatility: is there more to newspapers than news?\n## Chapter 4: The Shifting focus of Central Bankers\n## Acknowledgements","[{\"question\":\"What unifying theme does the thesis focus on?\",\"answer\":\"The thesis centers on applying machine learning and data science techniques to macroeconomics, combining theoretical equilibrium analysis with empirical text-based methods.\"},{\"question\":\"How does Chapter 1 address indeterminacy in rational expectations models?\",\"answer\":\"It studies neural-network learning by sophisticated agents in bounded indeterminacy settings, showing convergence to a stable equilibrium where indeterminate regions act as sources and determinate regions as sinks while remaining consistent with rational expectations.\"},{\"question\":\"What does Chapter 2 contribute methodologically?\",\"answer\":\"It develops Bayesian Topic Regression for causal inference with text data, and demonstrates that jointly estimating topics and regression components improves inference relative to staged approaches, with benchmarking on real-world datasets.\"}]","Essays in Macroeconomics and Machine Learning - 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