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Essays On Artificial Intelligence Reinforcement Learning And Structural Econometrics
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Book Synopsis Essays on Artificial Intelligence, Reinforcement Learning, and Structural Econometrics by : Weipeng Zhang
Download or read book Essays on Artificial Intelligence, Reinforcement Learning, and Structural Econometrics written by Weipeng Zhang and published by . This book was released on 2022 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the second chapter, I propose a distributed randomized policy iteration algorithm for infinite horizon dynamic programming problems for which the control at each stage is m-dimensional. The traditional policy iteration algorithm involves performing a minimization over an m-dimensional constraint set and has a computational complexity that increases exponentially in m, resulting in an intractable combinatorial search problem. In each iteration, our algorithm performs a series of sequential minimizations followed by policy evaluation and policy improvement using the policy that attains the minimum cost over the sequential minimizations. The algorithm is well-suited for parallel computation, has a complexity that increases linearly in m, and converges to an agent-by-agent optimal policy. I characterize sufficient conditions for which our algorithm generates a globally optimal policy that coincides with that obtained from standard policy iteration.
Book Synopsis Essays on Applied Economics with Machine Learning Approach by : Tzai-Shuen Chen
Download or read book Essays on Applied Economics with Machine Learning Approach written by Tzai-Shuen Chen and published by . This book was released on 2018 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation concentrates on applying machine learning methods to economic policy analysis. When talking about using machine learning or other non-behavioral model to conduct policy analysis, the first question raised by economists is the Lucas critique. A policy intervention would affect the incentive that people face and thus changes the underlying decision-making problem. A predictive model without the component of optimizing behavior might not capture people's reactions to the policy intervention to give a reliable prediction. Even if the quantitative effect of the Lucas critique is not significant, the machine learning method might have no advantage over a well-performed standard econometric model in terms of prediction or time efficiency. The first chapter presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. To evaluate the benefits of this approach, I use the most common machine learning algorithms, CART, C4.5, LASSO, random forest, and adaboost, to construct prediction models for a cash transfer experiment conducted by the Progresa program in Mexico, and I compare the prediction results with those of a previous structural econometric study. Two prediction tasks are performed in this paper: the out-of-sample forecast and the long-term within-sample simulation. For the out-of-sample forecast, both the mean absolute error and the root mean square error of the school attendance rates found by all machine learning models are smaller than those found by the structural model. Random forest and adaboost have the highest accuracy for the individual outcomes of all subgroups. For the long-term within-sample simulation, the structural model has better performance than do all of the machine learning models. The poor within-sample fitness of the machine learning model results from the inaccuracy of the income and pregnancy prediction models. The result shows that the machine learning model performs better than does the structural model when there are many data to learn; however, when the data are limited, the structural model offers a more sensible prediction. In addition to prediction outcome, machine learning models are more time-efficient than the structural model. The most complicated model, random forest, takes less than half an hour to build and less than one minute to predict. The findings show promise for adopting machine learning in economic policy analyses in the era of big data. The second chapter exploits the predictive power of machine learning algorithms to conduct covariate adjustment for estimating average treatment effects and the log-odds ratio. Previous semi-parametric approaches have proven that baseline covariate adjustment can increase the estimator efficiency and statistical power, compared to an unadjusted estimator. I use random forest model to select predictive covariates and conduct a Monte Carlo simulation to compare the efficiency and statistical power of unadjusted, OLS-based, and random-forest-based approaches in different parameter settings. The simulation result indicates that the random-forest-based estimator is more efficient and has higher statistical power than the other two methods. In addition, I apply this approach to the Zomba Cash Transfer Experiment in Malawi to study the difference in policy effect between conditional and unconditional cash transfers. The third chapter investigates the possibility of using machine learning models to conduct the counterfactual analysis for conditional policies. Conditional Cash Transfer has become a popular tool to alleviate intergenerational poverty in many developing countries due to the success of the Progresa program in Mexico. There are some experiments focused on the implementation details to explore the efficient practice of the policy implementation. The policy analysis, however, still heavily relies on the counterfactual prediction because of the budget and time constraints. Recently, machine learning has been proved successful in many prediction applications. Adopting machine learning model into economic policy analysis might help to increase the prediction performance and hence offer another approach of counterfactual analysis. While it is straightforward to apply machine learning algorithms to conduct counterfactual prediction for the unconditional policy, there is no direct prediction for the conditional policy due to the lack of behavioral description. This chapter uses the Zomba Cash Transfer Experiment in Malawi to examine the error of using an unconditional machine learning approach to prediction the outcome of the conditional policy. The result shows that the error from the conditional-unconditional difference is a minor source of prediction errors, which provides support of exploiting the predictive power of machine learning algorithms to offer policy suggestions for the conditional policy.
Book Synopsis Essays in Honor of Cheng Hsiao by : Dek Terrell
Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
Book Synopsis Essays in Applied Panel Data Econometrics and Machine Learning by : Ghalib Absar Ahmed Minhas
Download or read book Essays in Applied Panel Data Econometrics and Machine Learning written by Ghalib Absar Ahmed Minhas and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Recent Advances in Reinforcement Learning by : Leslie Pack Kaelbling
Download or read book Recent Advances in Reinforcement Learning written by Leslie Pack Kaelbling and published by Springer. This book was released on 2007-08-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Book Synopsis Artificial Intelligence, Learning and Computation in Economics and Finance by : Ragupathy Venkatachalam
Download or read book Artificial Intelligence, Learning and Computation in Economics and Finance written by Ragupathy Venkatachalam and published by Springer Nature. This book was released on 2023-02-15 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded. Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools. The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis.
Book Synopsis Reinforcement Learning Algorithms: Analysis and Applications by : Boris Belousov
Download or read book Reinforcement Learning Algorithms: Analysis and Applications written by Boris Belousov and published by Springer Nature. This book was released on 2021-01-02 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
Book Synopsis Machine Learning and AI in Finance by : German Creamer
Download or read book Machine Learning and AI in Finance written by German Creamer and published by Routledge. This book was released on 2021-04-06 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
Book Synopsis Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects by : Tohid Atashbar
Download or read book Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects written by Tohid Atashbar and published by International Monetary Fund. This book was released on 2022-12-16 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.
Book Synopsis Machine Learning in the Analysis and Forecasting of Financial Time Series by : Jaydip Sen
Download or read book Machine Learning in the Analysis and Forecasting of Financial Time Series written by Jaydip Sen and published by . This book was released on 2023-04-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of real-world cases, illustrating how to handle challenging and volatile financial time series data for a better understanding of their past behavior and robust forecasting of their future movement. It demonstrates how the concepts and techniques of statistical, econometric, machine learning, and deep learning are applied to build robust predictive models, and the ways in which these models can be used for constructing profitable portfolios of investments. All the concepts and methods used here have been implemented using R and Python languages on TensorFlow and Keras frameworks. The book will be particularly useful for advanced postgraduate and doctoral students of finance, economics, econometrics, statistics, data science, computer science, and information technology.
Book Synopsis Reinforcement Learning with TensorFlow by : Sayon Dutta
Download or read book Reinforcement Learning with TensorFlow written by Sayon Dutta and published by Packt Publishing Ltd. This book was released on 2018-04-24 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.
Book Synopsis Essays in Economics and Machine Learning by : Friedrich Christian Geiecke
Download or read book Essays in Economics and Machine Learning written by Friedrich Christian Geiecke and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fundamentals of Reinforcement Learning by : Rafael Ris-Ala
Download or read book Fundamentals of Reinforcement Learning written by Rafael Ris-Ala and published by Springer Nature. This book was released on 2023-08-14 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions
Book Synopsis Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by : El Bachir Boukherouaa
Download or read book Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Book Synopsis Essays on Probabilistic Machine Learning for Economics by : Nikolas Kuhlen
Download or read book Essays on Probabilistic Machine Learning for Economics written by Nikolas Kuhlen and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Reinforcement Learning by : Mohit Sewak
Download or read book Deep Reinforcement Learning written by Mohit Sewak and published by Springer. This book was released on 2019-06-27 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.
Book Synopsis Essays in Applied Machine Learning and Economics by : Garima Singal
Download or read book Essays in Applied Machine Learning and Economics written by Garima Singal and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we contribute to two strands of economic literature- applied economics and game theory. The contribution to applied economics is developing a prediction model for PM2.5, a key indicator of air pollution. The PM2.5 predictions from this model enable an analysis of economic and environmental policies that were previously infeasible due to a lack of PM2.5 measurements, especially in developing regions. We also demonstrate the unsuitability, for Delhi specifically, of the predominant benchmark estimates for PM2.5, in the applied economics literature supplied by van Donkelaar et al. Additionally, we were able to introduce and demonstrably improve upon a frontier technique from the deep learning literature to the applied economics literature. The contribution to the game theory literature takes the form of assessing the optimality of contests as a mechanism in the context of a standard Myersonian mechanism design environment, a previously unexplored setting. We find that despite extensive usage of contests as a mechanism in the real world, it is not without loss in revenue to use optimal contests. This dissertation's primary contribution is developing a modeling pipeline with lower data requirements and better predictive performance than the existing state-of-the-art estimates in the applied economics literature.
