Econometrics

A.A. 2024/2025
6
Crediti massimi
40
Ore totali
SSD
SECS-P/05
Lingua
Inglese
Obiettivi formativi
The aim of the course is to provide students with the basic principles of econometrics. All the aspects of econometric models treated during the course will be investigated through modern empirical applications in order to motivate students and respond to important problems coming from the real world with appropriate and specific numerical answers. Specifically, the first aim of the course is to extend the simple linear regression model, already thought in the course of Statistics, in different directions: extend the number of regressors, consider potential departures from the standard assumptions of the model, develop a theoretical framework for making inference on the parameters of the model, both for small sample and asymptotically. The second specific aim, concerns the introduction to non-linear regression models like models for binary dependent variables or non-linear specifications among the regressors.
Risultati apprendimento attesi
At the end of the course students will have received the introductory notions of econometrics. In particular, they will be able to specify a linear regression model, estimate the coefficients and perform tests of hypothesis on them. Moreover, students will be able to read and critically comment on the results of econometric analyses based on linear regression models or on regression models presenting some nonlinearities, like logit and probit ones. These expected outcomes should help students in understanding empirical analysis introduced in different courses, as well as provide them with quantitative tools for the development of the final thesis.
Corso singolo

Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.

Programma e organizzazione didattica

Edizione unica

Responsabile
Periodo
Terzo trimestre

Programma
Topic 0: Econometrics in Action

· Empirical Methods, Data and Causality
- Empirical Evidence and Decision-Making
- A Tale of Two Revolutions
- The Importance of Counterfactuals
- Time Heals all Wounds? Sometimes
- Understanding the Data
- Causality Vs Correlation


Topic 1: OLS Review

· Linear Regression with Multiple Regressors
- 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 (review)
- 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


Topic 2: Application: Class Size and Test Score

· Assessing Studies Based on Multiple Regression
- 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

Topic 3: Non-Linear Regression Functions

· Nonlinear Regression Functions
- 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 Student-Teacher Ratio


Topic 4: Binary Outcomes

· 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


Topic 5: OLS and Endogeneity

· OLS and Endoegeneity
- Endogeneity
- Omitted Variable Bias
- Other Sources of Endogeneity
- OLS in Matrix Form


Topic 6: Panel data

· Regression with panel data
- Panel data with two time periods
- Fixed effects regression
- Regression with time fixed effects
- The fixed effects regression assumptions and standard errors for fixed effects regression
- Empirical example: drunk driving laws and traffic deaths


Topic 7: IV variables

· 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: Large-Sample Distribution of the TSLS Estimator

Topic 8: Randomized Experiments (optional)

· Experiments and quasi-experiments
- Potential outcomes, causal effects and idealized experiments
- Threats to validity experiments
- Experimental estimates of the effect of class size reductions
- Quasi-experiments
- Potential problems with quasi-experiments
Prerequisiti
Basic course of Statistics, including notions of inferential statistics. Basic notions of calculus and matrix algebra.
Metodi didattici
Lezioni ed esercitazioni, usando il software econometrico STATA.
Materiale di riferimento
Textbook: "Introduction to Econometrics" by J.H. Stock e M.W. Watson.
Modalità di verifica dell’apprendimento e criteri di valutazione
Written exam.
SECS-P/05 - ECONOMETRIA - CFU: 6
Lezioni: 40 ore
Turni:
Turno
Docente: Fasani Francesco Maria
Docente/i
Ricevimento:
Martedì 17:00-19:00 (su appuntamento)
Ufficio 215 (Via Livorno 1) o su Teams