Econometrics II

Code: ECN02361L

6 ECTS

Duration: 15 weeks/156 hours

Scientific Area: Economy

Teaching languages: Portuguese

Languages of tutoring support: Portuguese

Presentation

Complements Econometrics I, providing an approach to binary choice models, dynamic models and cointegration, panel data, instrumental variables and multiple equations.

Sustainable Development Goals

Educação de qualidade - Assegurar a educação inclusiva, e equitativa e de qualidade, e promover oportunidades de aprendizagem ao longo da vida para todos.

Learning Goals

Complementing the skills developed in Econometrics I, considering new models for cross-sectional data and introducing techniques and models appropriate to deal with time series and panel data.

Studying binary choice models, dynamic models, co-integration, instrumental variables and simultaneous equations.

Analysing several econometric models appropriate to describe a wide variety of models emerging from the economic theory, as well as directing the application of those models to real problems at both the macroeconomic and microeconomic level, connected to the Portuguese and/or the European reality.

Developing the ability of using econometric software and real datasets.

Developing the ability of formulation and interpretation of econometric models.

Developing critical reasoning and the ability of abstraction.

Contacting with the development of empirical economic research.

BINARY CHOICE MODELS:
Linear probability model
Maximum likelihood estimation
Logit and probit models

BASIC REGRESSION ANALYSIS WITH TIME SERIES DATA:
Types of models
Trends and seasonality
Stationary and nonstationary time series.

AUTOCORRELATION AND HETEROSKEDASTICITY IN TIME SERIES REGRESSIONS:
Autocorrelation tests
Generalized least squares
Dynamically complete models
Heteroskedasticity
ARCH models

DYNAMIC MODELS AND FORECASTING:
Infinite distributed lag model
Stationarity and unit roots tests
Spurious regression and cointegration
Forecasting

PANEL DATA MODELS:
Fixed effects model
Random effects model

INSTRUMENTAL VARIABLE REGRESSION:
Motivation: Omitted variables and measurement error
Estimation
Endogeneity test and overidentifying restrictions test

SIMULTANEOUS EQUATION MODELS:
Reduced form model and structural model
The identification problem
Two stage least squares

Teaching Methods

Small group classes are delivered with a theoretical-practical nature, where the theoretical contents are supplemented by exercises. Most of these exercises are based on real datasets and involve the use of the software Stata.
The main assessment system involves the presence in no less than 75% of the classes, a mid-term exam paper, an empirical group project and a final exam paper. However, students may also choose to so a final written exam.