Isaiah Hull

Machine Learning for Economics and Finance in TensorFlow 2

Deep Learning Models for Research and Industry

1st ed.
Isaiah Hull
Nacka, Sweden
ISBN 978-1-4842-6372-3e-ISBN 978-1-4842-6373-0
https://doi.org/10.1007/978-1-4842-6373-0
Apress standard
© Isaiah Hull 2021
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For my wife, Jamie; my son, Moses; and my parents, James and Gale

Table of Contents
Chapter 1:​ TensorFlow 2 1
Installing TensorFlow 1
Changes in TensorFlow 2 2
TensorFlow for Economics and Finance 12
Machine Learning 14
Theoretical Models 19
Introduction to Tensors 20
Linear Algebra and Calculus in TensorFlow 22
Constants and Variables 22
Linear Algebra 24
Differential Calculus 34
Loading Data for Use in TensorFlow 55
Summary 58
Bibliography 59
Chapter 2:​ Machine Learning and Economics 61
“Big Data:​ New Tricks for Econometrics” (Varian 2014) 62
“Prediction Policy Problems” (Kleinberg et al.​ 2015) 64
“Machine Learning:​ An Applied Econometric Approach” (Mullainathan and Spiess 2017) 66
“The Impact of Machine Learning on Economics” (Athey 2019) 69
Machine Learning and Traditional Econometric Methods 69
Off-the-Shelf ML Routines 72
Policy Analysis 73
Active Research and Predictions 74
“Machine Learning Methods Economists Should Know About” (Athey and Imbens 2019) 75
“Text as Data” (Gentzkow et al.​ 2019) 77
Representing Text As Data 77
Statistical Methods 78
Applications 80
“How is Machine Learning Useful for Macroeconomic Forecasting” (Coulombe et al.​ 2019) 80
Summary 83
Bibliography 84
Chapter 3:​ Regression 87
Linear Regression 87
Overview 88
Ordinary Least Squares (OLS) 91
Least Absolute Deviations (LAD) 94
Other Loss Functions 101
Partially Linear Models 102
Non-linear Regression 108
Logistic Regression 114
Loss Functions 115
Discrete Dependent Variables 116
Continuous Dependent Variables 117
Optimizers 119
Stochastic Gradient Descent (SGD) 119
Modern Optimizers 122
Summary 124
Bibliography 125
Chapter 4:​ Trees 127
Decision Trees 127
Overview 128
Feature Engineering 131
Training 132
Regression Trees 134
Random Forests 136
Gradient Boosted Trees 138
Classification Trees 140
Regression Trees 143
Model Tuning 144
Summary 146
Bibliography 147
Chapter 5:​ Image Classification 149
Image Data 150
Neural Networks 155
Keras 158
The Sequential API 159
The Functional API 168
Estimators 171
Convolutional Neural Networks 174
The Convolutional Layer 174
Training a Convolutional Neural Network 177
Pretrained Models 181
Feature Extraction 181
Model Fine-Tuning 184
Summary 185
Bibliography 186
Chapter 6:​ Text Data 189
Data Cleaning and Preparation 190
Collecting the Data 192
Text Data Notation 195
Data Preparation 196
The Bag-of-Words Model 203
Dictionary-Based Methods 209
Word Embeddings 215
Topic Modeling 217
Text Regression 226
Text Classification 243
Summary 245
Bibliography 246
Chapter 7:​ Time Series 249
Sequential Models of Machine Learning 250
Dense Neural Networks 250
Recurrent Neural Networks 257
Long Short-Term Memory (LSTM) 264
Intermediate Hidden States 268
Multivariate Forecasts 271
LSTM 272
Gradient Boosted Trees 275
Summary 278
Bibliography 278
Chapter 8:​ Dimensionality Reduction 281
Dimensionality Reduction in Economics 282
Principal Component Analysis 283
Partial Least Squares 293
The Autoencoder Model 296
Summary 305
Bibliography 306
Chapter 9:​ Generative Models 307
Variational Autoencoders 309
Generative Adversarial Networks 318
Applications in Economics and Finance 327
Summary 329
Bibliography 330
Chapter 10:​ Theoretical Models 331
Solving Theoretical Models 332
The Cake-Eating Problem 332
The Neoclassical Business Cycle Model 341
Deep Reinforcement Learning 348
Summary 355
Bibliography 355
Index 357
About the Author
Isaiah Hull
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is a senior economist at the Research Division of Sweden’s Central Bank. He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, macro-finance, and fintech. He also teaches courses on the DataCamp platform, including Introduction to TensorFlow in Python, and is working on an interdisciplinary research project to introduce quantum computing and quantum money to the economics discipline.

 
About the Technical Reviewer
Vishwesh Ravi Shrimali

graduated from BITS Pilani in 2018, where he studied mechanical engineering. Since then, he has worked with Big Vision LLC on deep learning and computer vision and was involved in creating official OpenCV AI courses. Currently, he is working at Mercedes Benz Research and Development India Pvt. Ltd. He has a keen interest in programming and AI and has applied that interest in mechanical engineering projects. He has also written multiple blogs about OpenCV and deep learning on Learn OpenCV, a leading blog on computer vision. He has also coauthored Machine Learning for OpenCV 4 (second edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

 
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