|Course code: 174403||Subject title: ECONOMETRICS|
|Credits: 6||Type of subject: Mandatory||Year: 2||Period: 2º S|
|HUALDE BILBAO, JAVIER (Resp) [Mentoring ]|
Quantitative Methods: Econometrics.
CG04. Oral and written communication in a foreign language.
CG05. Developing software knowledge applied to the corresponding subject.
CG06. Ability to analyze and extract information from different sources.
CG07. Capacity to solve problems.
CG09. Capacity to work in teams.
CG16. Capacity to work under pressure.
CG17. Capacity to self-learning.
CE02. Identify relevant economic information sources.
CE03. Derive from micro and macroeconomic data relevant information impossible to assess by non-specialists.
CE04. Use of professional criteria in the economic analysis, mainly those criteria based on technical tools.
R20. Simple regression and explanatory variables models, econometric models.
At the end of the course, the student must be able to translate into mathematical language specific issues from macroeconomics and microeconomics by means of econometric models. Additionally, the student must be able to use the econometric software Gretl and extract conclusions from the estimated models.
Presentation of the main theoretical aspects of the course. Student participation: questions made by the lecturer, brief presentations of previous lectures.
Classes in computer room. Small groups. Presentation and revision of exercises made individually and in small groups. Use of econometric packages (GRETL).
Individual and team work
Preparation of exercises and presentations.
Periodic tutorials with lecturer
Individual and group meetings.
Personal study and exam
|Individual preparation of exercises and presentations||26|
|Team preparation of exercises and presentations||-|
|Activities||Evaluated proficiencies||Weight (%)|
|Preparation and presentation of reports and exercises||CG04, CG05, CG06, CG07, CG16, CE02, CE03 y CE04||10%|
|Partial exams (theoretical and computer-based)||CG04, CG07, CG16, CG17, CE02, CE03 y CE04||20%-20%|
|Final exam||CG04, CG07, CG16, CG17, CE02, CE03 y CE04||50%|
|Weight (%)||It allows
|R18. Simple regression and explanatory variables models||Theoretical tests and class participation||25%||Yes*||None|
|R18. Simple regression and explanatory variables models||Tests in computer classroom and class participation||25%||Yes*||None|
|R18. Simple regression and explanatory variables models||Final exam||50%||Yes*||3 out of 10|
Those students who do not attend the final exam will get a grade of "No Presentado"
*recoverable through a single exam that will weigh 100% of the grade.
First, we will introduce the idea of econometric model. This model must take into account the special features of economic data. We will focus on the ideas of causality and ceteris paribus analysis, arising from the correct interpretation of these models. From a formal viewpoint, the initial step towards an appropriate conceptual framework will be to introduce the simple regression model. Here, we will study the basic assumptions, interpretation of parameters of interest, ordinary least squares estimation and statistical properties of the estimators.
The second building block of the course is the study of the general regression model. We will analyze its basic properties, focusing on the motivation behind multivariate regression, stressing its usefulness over the bivariate framework which characterizes the simple regression model. Again, we will study in this more general framework the basic assumptions, interpretation of parameters of interest, ordinary least squares estimation and statistical properties of the estimators.
In the last part of the course, we will give a brief introduction to two extensions of the previous model: regression with panel data and regression with a binary dependent variable.
Chapter 1. Introduction
Economic questions we examine
Causal effects and idealized experiments
Data: Sources and types
Chapter 2. Linear regression with one regressor
The linear regression model
Estimating the coefficients of the linear regression model
Measures of fit and prediction accuracy
The least squares assumptions for causal inference
The sampling distribution of the OLS estimators
Testing hypotheses about one of the regression coefficients
Confidence intervals for a regression coefficient
Regression when X is a binary variable
Heteroskedasticity and homoskedasticity
The theoretical foundations of ordinary least squares
Using the t-statistic in regression when the sample size is small
Chapter 3. Linear regression with multiple regressors
Omitted variable bias
The multiple regression model
The OLS estimator in multiple regression
Measures of fit in multiple regression
The least squares assumptions for causal inference in multiple regression
The distribution of the OLS estimators in multiple regression
Control variables and conditional mean independence
Hypothesis tests and confidence intervals for a single coefficient
Tests of joint hypotheses
Testing single restrictions involving multiple coefficients
Confidence sets for multiple coefficients
Model specification for multiple regression
Analysis of the test score data set
A general strategy for modeling nonlinear regression functions
Nonlinear functions of a single independent variable
Interactions between independent variables
Nonlinear effects on test scores of the student-teacher ratio
Internal and external validity
Chapter 4. Regression with panel data
Panel data with two time periods
Fixed effects regression
Regression with time fixed effects
Chapter 5. Regression with a binary dependent variable
Binary dependent variables and the linear probability model
Probit and Logit regression
Application to the Boston HMDA data
The main references for the course are:
Stock, J.H. and Watson, M.W. "Introduction to econometrics". Pearson Education Limited; 4 edition (2020).
Wooldridge, J.M. "Introductory econometrics: a modern approach". Cengage Learning Inc.; 7 edition (2020).
Classroom and Computer room at the Aulario.