Statistics and econometrics
A.Y. 2016/2017
Lesson for
Learning objectives
The course is organized in two parts.
Part 1: This first 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.
Part 2: The aim of these part 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 1: This first 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.
Part 2: The aim of these part 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
Practicals: 16 hours
Lessons: 64 hours
Professors:
Salini Silvia, Verdolini Elena
Syllabus
· Economic Questions and Data
 Economic Questions We Examine  Causal Effects and Idealized Experiments  Data: Sources and Types · 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 2: 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 1: Formulas for OLS Standard Errors · 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 · 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 2: Derivation of the Formula for the TSLS Estimator  Appendix 3: 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 (to read only)  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 semester

Lesson period
Second semester
Professor(s)