Syllabus query



Academic Year/course: 2016/17

8056 - Master in Current Democracies: Nationalism, Federalism and Multiculturalism

32229 - Techniques of Statistical Analysis I


Teaching Guide Information

Academic Course:
2016/17
Academic Center:
805 - Masters Centre of the Department of Political and Social Sciences
Study:
8056 - Master in Current Democracies: Nationalism, Federalism and Multiculturalism
Subject:
32229 - Techniques of Statistical Analysis I
Credits:
5.0
Course:
1
Teaching languages:
Theory: Group 1: Pending
Group 2: Pending
Teachers:
Jorge Rodriguez Menes, Sandra Bermudez Torres, Daniel Andres Ciganda Soliņo
Teaching Period:
First Quarter
Schedule:

Presentation

Techniques of Statistical Analysis I (Group 2) provides an introduction to regression analysis in the social sciences. The course builds on students’ knowledge of basic statistics to deepen their understanding of linear regression techniques, placing special emphasis on problems associated with model specification and solutions to other violations of regression assumptions. The course also provides an introduction to regression with independent and binary dependent categorical variables with logistic and probit models. The teaching approach avoids as much as possible formalistic-mathematical presentations and instead focuses on the logic that lies behind the techniques. The lectures combine a theoretical presentation of each subject with hands-on applications using STATA. 

Associated skills

  • General competences (Instrumental, Interpersonal and Systemic)

 

Instrumental Competences

Ability to analyse and synthesise

Planning and management of time

Basic computer competence and ability of using statistical software (STATA)

Information management abilities (ability to search and analyse information coming from a variety of sources)

 

Interpersonal Competences

Critical and self-critical ability

Team work

Ability to work in a interdisciplinary team

Ability to communicate with people that are not experts in the subject

 

Systemic Competences

Research abilities

Ability to work autonomously

Ability to generate new ideas (creativity)

Design and management of projects

 

  • Specific competences
  1. Knowledge of the basics of linear regression analysis
  2. An understanding of the assumptions underlying classical linear regression models, and of the importance for testing for, and applying remedial measures to, deviations from such assumptions
  3. Knowledge of regression techniques for categorical dependent variables
  4. Interpretation skills, as crystallized into the ability to describe results from intermediate statistical analyses

Prerequisites

Knowledge of elementary statistics

Contents

  • There will be three basic content blocks.

 1. The Classical Regression Model

Deterministic, probabilistic & observational models. Metric, significance & strength of linear relations. Estimation: OLS & ML. Practical significance an: Beta coefficients:  total, part, and partial correlations; the R2 and its decomposition. Statistical significance: t and F tests. Model building: J & LR tests. Regression with dummy variables (ANOVA); regression with nominal & continuous independent variables (ANCOVA). Interaction effects.

2.  Deviations from regression assumptions

Regression assumptions. Model miss-specification: wrong regressors, wrong functions (non-linearities and parameter inconsistencies). Tests and remedial measures for miss-specification. Inefficiencies: heteroskedasticity, autocorrelation, & multicollinearity. Testing and correcting inefficiencies with weighted and generalized least squares.

3.  Regression applications: ANOVA, ANCOVA, & Regression with categorical dependent variables

Logit & probit models; logistic regression: metric, goodness of fit, significance & strength of relationship; maximum likelihood (ML) estimation; likelihood ratio & Chi-square; pseudo R2 measures. Multinomial models.

Teaching Methods

Each weekly 3-hour session will be divided into two parts: a lecture-type, 1:45 minute’s session where the main aspect of the weekly topics will be laid out, and a 1 hour practical session where students will practice their newly acquired knowledge with Stata.

Evaluation

  • Assessment will be based on the 0-10 scale

          0-4.9 – Fail

          5 -6.9 – Pass

          7-8.9 – Good

          9-10 – Outstanding

 

  • 2 take-home tests at weeks 5 (due on week 7) and 10 (due two weeks after classes end), with an emphasis on problem solving and interpretations of results using real data. Each will count 50% of the final mark.
  • Every week, students will be given sets of computer exercises to complete at home (due in class on the following week). The problems will not be marked, i.e., but for every two sets not returned on time, 1 full mark will be deducted from the final mark. 

Bibliography and information resources

 

  Week

Classroom activity

Readings & Incidences

1

Lecture

Simple regression (1)

Basic notation. Deterministic & probabilistic models. Error term. Expected values. OLS parameter estimation.

Baum: chapters 2 & 3

Agresti & Finlay: Chapter 9

Kennedy: chapters 1 & 2

 

Lab

Working with Stata: Basics operations with files & variables

2

Lecture

Simple regression (2)

 Betas, Covariance & Correlation. R2. Hypothesis testing

Lab

Producing and interpreting regression output with Stata

3

Lecture

Multiple regression

Notation.  OLS estimation.  Part & partial correlations. Multiple Determination. Adjusted R2. T tests.

Baum: chapter 4.1 – 4.4, 4.6

Kennedy: chapter 4

Agresti & Finlay: Chapter 11.1 to 11.3, 11.7 & 11.8

 

 

Lab

Multiple regression with Stata: basic output

4

Lecture

Model building strategies

Complete and Reduced Models. F & J tests Deductive and inductive strategies.  Forward & Backward selection

Baum: chapter 4.5

Agresti & Finlay: Chapter 11.6, 14.1

Kennedy: chapters 3 & 5.1-5.2

 

Lab

Simple and complex hypothesis-testing with Stata

5

Lecture

Regression with categorical independent variables

 ANOVA & ANCOVA. Marginal and interaction effects

Baum: chapter 7.1 – 7.2

Agresti & Finlay: Chapter 12 & 13

Kennedy: chapters 8 & 11 (except ridge regression & with principal components)

Lab

Regression with dummy & continuous variables in Stata

First take-home test handed in to students

6

Lecture

Specification error

Omission of relevant variables & wrong functional forms. Causal Models. Exogeneity and endogeneity

Baum: chapter 5

Agresti & Finlay: Chapter 10, 14.4

Kennedy: chapters 5.3 - 5.5, 6 & 7 (excl. limited depende nt var)

 

Lab

RESET test. Non-linear functions & linear transformations

 

7

 

Inefficiencies

 Non-spherical disturbances (heteroskedasticity) & multicollinearity. WLS, Robust standard errors

Baum: chapter 6.2

Agresti & Finlay: Chapter 14.2 & 14.3

Kennedy: chapters 8.1-8.3 & 11 (except ridge regression & with principal components)

Lab

Tests of heteroskedasticity in Stata, Weighted Least Squares

8

Lecture

Regression with binary dependent variables

Logit models. Estimation, testing, and interpretation. Odds ratios. Wald and LR tests

Baum: chapter 10.1

Agresti & Finlay: Chapter 15

Kennedy: chapter 15.1

 

Lab

Logistic regression in Stata

9

Lecture

Model building and testing with logistic regression

Logistic regression with continuous and dummy variables Two- and three-way interaction effects

Baum: chapter 10.1

Agresti & Finlay: Chapter 15

Kennedy: chapter 15.1

 

Lab

Regression with dummy & continuous variables in Stata. LR and Bayesian Model Selection

Second take-home test handed-in to students

10

Lecture

Probit models  

The Probit transformation. Estimation, fit and testing. Marginal Effects

Baum: chapter 10.1

Kennedy: chapter 15.1

 

Lab

Probit regression with Stata

 

  • Basic bibliography

 

Baum Christopher F. 2006. An Introduction to Modern Econometrics Using Stata. Stata Press

Agresti, A. & Finlay, B. 1997 Statistical Methods for the Social Sciences. 3rd edition. Prentice Hall.

Kennedy, Peter. 2003. A Guide to Econometrics, Fifth Edition. The MIT Press.

 

  • Complementary bibliography

 

Wooldridge, Jeffrey M. 2003. Introductory Econometrics. A Modern Approach. 2nd Ed. Thompson

Stock, James H. & Watson, Mark W. 2003. Introduction to econometrics. Boston: Pearson Education.

Chatterjee, S., Hadi, A. S., and Price, B. 2000. Regression Analysis by Example. Third Edition. New Yor: John Wiley and Sons, Inc.

Gujarati, Damodar. 2002. N. Basic Econometrics, Fourth Edition. McGraw-Hill.

Maddala, G.S. 2001. Introduction to Econometrics, Third Edition. Wiley.