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Neural Networks in Economics

Creative Commons 'BY' version 4.0 license

The chapters of this dissertation explore the theoretical and empirical potential of neural networks and deep learning as estimation techniques in economics. The first chapter provides a novel approximation result for two hidden layer neural networks that makes clear the trade-off between width and depth. I leverage this result to provide consistency and $o_p(n^{-1/4})$ convergence rates for this estimator and demonstrate its flexibility in finite samples. In addition, I introduce a new algorithm called cross-training that allows construction of asymptotic confidence intervals for linear functionals. In the second chapter I provide a new neural network designed for a panel data setting with a common index. I allow for unobserved heterogeneity to enter in the form of additive cross-sectional fixed effects and provide a correction for the incidental parameter bias by re-centering the score. I apply this estimator to the demand for cigarettes in the United States. In the third and final chapter I explore the role of autoencoders as a dimensionality reduction technique when outcomes are binary. The autoencoder outperforms other dimensionality reduction techniques, like principal components analysis, in uncovering latent choice probabilities. This estimator is applied in a consumer segmentation exercise.

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