Impact Evaluation: Statistics and Economics

Impact Evaluation


 Orazio Attanasio Profesor Orazio Attanasio, University College in London.

Elisa Cavatorta Profesora Elisa Cavatorta, King’s College Londres.

16 al 30 de julio 2018
No incluye sábado 21 de julio pero si sábado 28 de julio.               
Cavatorta: 16-24 de julio 10:00 a.m. a 12:00 m.  Monitoria 12:00 m a 1:00 p.m.                             
Attanasio:  25-28 de julio 10:00 a.m. a 1:00 p.m. Examen 30 julio 10:00 a.m. a 1:00 p.m.                          
Curso en inglés

In this course we will look at the evaluation of policy interventions. We will discuss the desirability of evaluations and the main conceptual problems in identifying the effect of policy interventions. We will then discuss different econometric techniques that have been proposed in the literature to deal with these issues relating them to the economics behind the evaluation problem. In particular, we will discuss critically: Randomized Controlled Trials, Propensity Score Matching, Regression Discontinuity Design, Difference in Difference Methods, Instrumental Variables and Control Functions, Structural Models. There will be a number of applications that will illustrate the techniques discussed and some practical exercises. The focus will be on applications in Colombia and Latin America.

Prerrequisitos: Econometría I y Stata.


Programa preliminar del curso

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Impact evaluation statistics and economics interno.2.fw



We will begin at the beginning of spatial analysis, around mid-18th century. The introduction will be at a general level of epidemiology. Then we will slowly move to spatial statistics, spatial econometrics and finally to, spatial urban economics. We will touch on the contrast between time-series and spatial dependence, and the need for moving from linear to non-linear models; from fixed to space varying coefficient models. More specifically, in the context of urban econometrics, we will study the price of housing; we will argue that not only the spatial dependence of house prices, but also the dependence in the variability (risk) of prices need to be considered, leading to non-linear spatial autoregressive conditional heteroskedastic (SARCH) model. The major highlight of the course will be how to test various spatial models, particularly, in the context of possible misspecification.

The theories covered in the course will be illustrated in the computer lab by substantial applications using data from several countries, including Colombia. The aim of the computer lab is to introduce to students the problems related with handling spatial data and the properties of the basic econometric models. With hands-on experience, students will have a broad overview of the most recent developments and refinements of the basic models used in spatial econometrics. At the end of the course, students will have acquired the necessary skills to build, estimate and test these models in the R environment.

Prerequisites: A first course in econometrics.


Topic 1. History of Spatial Analysis

1.1. Spatial Analysis in Time of Cholera: Work of Dr. John Snow (1854)

1.2. Design of Experiments and Sample Survey: Work of Fisher and Mahalanobis (1920-1950)

1.3. First Formal Paper on Spatial Analysis: PAP Moran (1948)


Topic 2. Theory of Spatial Analysis

2.1. Why (Spatial) Dependence Matter?

2.2. Contrast between Time-Series and Spatial Dependence

2.3. Different Spatial Models

2.4. Estimation of Spatial Models


Topic 3. Testing Spatial Models

3.1 General Principles of Testing

3.2 Testing with Misspecified Models

3.1. Specification Tests for Spatial Models

3.2. Spatial Panel Models


Topic 4. Applications of Spatial Analysis

4.1. Applications of Spatial Analysis to Crime (Columbus, OH, USA)

4.2. Applications of Spatial Analysis House Prices (Boston, MA, USA)

4.3. Applications of Spatial Analysis to Regional Growth Convergence (of 61 countries)

4.4 Special Spatial Applications Using Colombian Data