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Essays On Machine Learning Methods In Economics, Mani Bayani

Dissertations, Theses, and Capstone Projects

This dissertation consists of three chapters on machine learning modeling in economics. Chapter 1 - Robust PCA Synthetic Control: In this chapter, I propose an algorithm for comparative studies called robust PCA synthetic control. My algorithm builds on the synthetic control model of Abadie et al., 2015 and the robust synthetic control model of Amjad et al., 2018. I apply all three methods (robust PCA synthetic control, synthetic control, and robust synthetic control) to answer the hypothetical question, what would have been the per capita GDP of West Germany if it had not reunified with East Germany in 1990? I …

Three Essays On Natural Rates, Huseyin U. Demir

Dissertations, Theses, and Capstone Projects

Chapter 1. Non-accelerating inflation rate of unemployment and Non-accelerating inflation rate of output We followed Ball and Mankiw (2002) to estimate the natural rates of output and unemployment. The primary purposes of this paper are to provide more accurate estimates of a varying non-accelerating inflation rate of unemployment (NAIRU) than currently exist and to nd a new measure for the nonaccelerating inflation rate of output so we can estimate the output gap more accurately. Our contributions are adding time-varying coefficients estimated with a break test and finding more accurate measurements for the natural rate of unemployment. We also estimated the …

Applications Of Machine Learning And Deep Learning In Macroeconomic And Financial Forecasting, Andi Cupallari

Dissertations, Theses, and Capstone Projects

This dissertation consists of three chapters.

In the first chapter I propose a novel approach to forecast risk premia selecting relevant predictors among hundreds of correlated stock characteristics. I adapt a recently developed method from the deep learning literature, Deep Neural Networks with Group Lasso Regular- ization. This method achieves high out of sample R2, and at the same time yields a sparse representation of the characteristics space that allows for interpretability of the otherwise black box deep learning model. For each period, the model chooses a subset of characteris- tics to be relevant for the risk premia forecast. Our …

Essays On Applied Machine Learning For Implied Volatility Interpolation And Artificial Counterfactuals, Pablo A. Crespo

Dissertations, Theses, and Capstone Projects

This dissertation consists of two chapters.

Chapter 1: Volatility estimates under the risk neutral density have become a much revisited topic of interest in recent years. The density proves itself a powerful tool for sentiment analysis, since its moments provide insights about expectations in price trends. A standard procedure for its extraction utilizes artificial volatility predictions to form a dense enough grid for approximating a complete probability distribution. This paper proposes two common machine learning technique variations to produce implied volatility predictions when data is very scarce. First, a model using regularization through a variation of a generalized LASSO path …