Essays on Sparse-Grids and Statistical-Learning Methods in Economics
Creators: name: Valero, Rafael
Publisher: name: Zenodo
Year: 2017
Subjects: Synthetic Control Method
projections methods
smolyak grid
curse of dimensionality
sparse grids

description: This thesis is split into three chapters: chapter 1 is about the efficient implementation of the Smolyak Method; chapter 2 proposes alternatives ways to synthetic series with Statistical Learning to assess policies, with two cases of unemployment; and chapter 3 uses the proposals in chapter 2 and applies them to discern the effects of the points based driving licenses in different countries. The first chapter goes in the direction of getting a step closer to the implementation of large economic models in more efficient ways; as will be illustrated, large economic models can be computed in a standard computer. To make this possible the chapter is focused on Smolyak methods, which makes the results applicable not only for economics but for engineering and other fields with large dimensional interpolation problems. It is explained in very simple terms the implementations of Smolyak method. This leads to easy and more efficient implementations by avoiding repetitions that come from the mathematical expressions. Also are studied other techniques for further reduction of the cost of the implementations by stressing the most important dimensions and regions to work with. The main focus of the second chapter is the creation of synthetic or counterfactual series in order to asses policies. Synthetic or counterfactual series are series that express what could have happened if variables had not been introduced, for example changes in policies. In order to evaluate a policy in unemployment, for example, we observe the unemployment, GDP, and other indicators. However we can estimate the synthetic or counterfactual ones for unemployment, GDP or other, and through this difference figure out the effects. In order to do that we currently have different techniques, as Synthetic Control (SC) methods. The role of this chapter is to reinforce previous literature by introducing in the field Statistical Learning techniques, already developed and used for other purposes. This is true until the point that most of these techniques are broadly implemented in scientific software packages making their implementation simple. In this chapter I explain how to apply them for the particular purpose of creating synthetic and counterfactual series and I illustrate that with two real cases of unemployment: a) a reduction of maximum work week hours, from the example of Portugal in 1996; and b) the reduction of firing cost, the example is Spain in 2010. In previous works there is no strong evidence that these types of labor reforms were effective or have a macroeconomic impact. The results are that Portugal enjoyed a reduction of unemployment. For the Spanish case the law showed an apparent increase in unemployment. In the third chapter I study the effect of the Third European Road Safety Action Program (3ERSAP) is studied here. Not only respect when it started to work but, also regarding what would had happened if it had not been implemented. This is a novel assessment of the 3ERSAP, and helps to distinguish effects coming from European collaboration and within each country. Three methods are compared: synthetic control methods (SC), synthetic control with statistical learning (SCSL) and interactive counterfactual effects (ICE). The overall view is that the implementation of the road safety program at European level had larger effects than if the countries had continued their own paths. This paper provides quantitative estimates for each country. At European level it is possible to say that the 3ERSAP in the year 2010 helped to avoid between 13900 and 19400 fatalities. Regarding the different methodologies, an easy way to compare them is provided. In this particular case SCSL tends to perform better than SC and ICE.
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