Research methods
A.Y. 2014/2015
Lesson for
Learning objectives
Part 1: The aim of this part of the course is to give students the preliminary elements of classical logic, and some basic information concerning the techniques of nonclassical deductive logics, especially: modal, paracomplete, and paraconsistent logics. At the end of the course, the student will be familiar with the language of contemporary logic, and the main logical devices for the analysis and evaluation of reasoning, in science as well as in usual communications.
Part 2: This second part of the course is dedicated to the introduction of basic elements of probability and inferential statistics. The final aim is to provide students with the theoretical and practical notions of estimation and hypothesis testing. Moreover, particular attention will be devoted to the linear regression model as well as the inference on the related parameters. Finally, an introduction to some multivariate statistical tools will be also provided.
Parts 3&4: The aim of these parts of the course is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to realworld questions with specific numerical answers.
Part 2: This second part of the course is dedicated to the introduction of basic elements of probability and inferential statistics. The final aim is to provide students with the theoretical and practical notions of estimation and hypothesis testing. Moreover, particular attention will be devoted to the linear regression model as well as the inference on the related parameters. Finally, an introduction to some multivariate statistical tools will be also provided.
Parts 3&4: The aim of these parts of the course is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to realworld questions with specific numerical answers.
Course structure and Syllabus
Active edition
Yes
Responsible
Unità didattica 1
SECSP/05  ECONOMETRICS  University credits: 3
Lessons: 20 hours
Professor:
Bacchiocchi Emanuele
Unità didattica 2
SECSP/05  ECONOMETRICS  University credits: 3
Lessons: 20 hours
Professor:
Bacchiocchi Emanuele
Unità didattica 3
SPS/04  POLITICAL SCIENCE  University credits: 3
Lessons: 20 hours
Professor:
D'Agostini Franca
Unità didattica 4
SPS/04  POLITICAL SCIENCE  University credits: 3
Lessons: 20 hours
Professor:
Salini Silvia
Unità didattica 1
Syllabus
· What is contemporary logic
· Propositional logic: the language · Propositional logic: the rules · Predicate logic: the language · Predicate logic: the rules · The reasons of nonclassical logics · Basic elements of Modal logics · Paracomplete and paraconsistent logics
Unità didattica 2
Syllabus
· Review of Probability: Basic Notions
· Review of Statistics  Estimation of the Population Mean  Hypothesis Tests Concerning the Population Mean  Confidence Intervals for the Population Mean  Comparing Means from Different Populations  DifferencesofMeans Estimation of Causal Effects Using Experimental Data  Using the tStatistic When the Sample Size Is Small  Scatterplot, the Sample Covariance, and the Sample Correlation · Linear Regression with One Regressor  The Linear Regression Model  Estimating the Coefficients of the Linear Regression Model  Measures of Fit  The Least Squares Assumptions  Appendix: Derivation of the OLS Estimators  Sampling Distribution of the OLS Estimator · Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals  Testing Hypothesis about one of the Regression Coefficients  Confidence Intervals for a Regression Coefficient  Regression when X Is a Binary Variable  Heteroskedasticity and Homoskedasticity  The Theoretical Foundation of Ordinary Least Squares  Using the tStatistic in Regression When the Sample Size Is Small  Appendix: Formulas for OLS Standard Errors
Unità didattica 3
Syllabus
· Linear Regression with Multiple Regressors
 Omitted Variable Bias  The Multiple Regression Model  The OLS Estimator in Multiple Regression  Measure of Fit in Multiple Regression  The Least Squares Assumptions in Multiple Regression  The Distribution of the OLS Estimators in Multiple Regression  Multicollinearity · Hypothesis Tests and Confidence Intervals in Multiple Regression  Hypothesis Tests and Confidence Intervals for a Single Coefficient  Tests of Joint Hypotheses  Testing Single Restrictions Involving Multiple Coefficients  Model Specification for Multiple Regression  Analysis of the Test Score Data Set · Assessing Studies Based on Multiple Regression (to read only)  Internal and External Validity  Threats to Internal Validity of Multiple Regression Analysis  Internal and External Validity when the Regression is Used for Forecasting  Example: Test Scores and Class Size
Unità didattica 4
Syllabus
· Regression with a Binary Dependent Variable
 Binary Dependent Variables and the Linear Probability Model  Probit and Logit Regression  Estimation and Inference in the Logit and Probit Models  Some applications · Instrumental Variable Regression  The IV Estimator with a Single Regressor and a Single Instrument  The General IV Regression Model  Checking Instrument Validity  Where Do Valid Instruments Come From?  Appendix: Derivation of the Formula for the TSLS Estimator  Appendix: LargeSample Distribution of the TSLS Estimator · Introduction to Time Series Regression and Forecasting  Using Regression Model for Forecasting  Introduction to Time Series Data and Serial Correlation  Autoregressions  Time Series Regression with Additional Predictors and ADL Model  Lag Length Selection Using Information Criteria · The Theory of Linear Regression with One Regressor (to read only)  The Extended Least Squares Assumptions and the OLS Estimator  Fundamentals of Asymptotic Distribution Theory (basic notions only)  Asymptotic Distribution of the OLS Estimator and tStatistic  Exact Sampling Distributions When the Errors Are Normally Distributed  Weighted Least Squares (basic notions only) · The Theory of Multiple Regression  The Linear Multiple Regression Model and the OLS Estimator in Matrix Form  Asymptotic Distribution of the OLS Estimator and tStatistic  Test of Joint Hypotheses Lesson period
Second trimester

Lesson period
Second trimester
Professor(s)
Reception:
This week, 1823 November 2019, the office hour will take place on Tuesday from 11:00 to 12:30.
Room 31, Department of Economics, Management and Quantitative Methods