Universidad Pública de Navarra - Nafarroako Univertsitate Publikoa
Public University of Navarre
| Course code: 172828 | Subject title: ECONOMETRICS II | ||||
| Credits: 6 | Type of subject: Optative | Year: 4 | Period: 2º S | ||
| Department: Economía | |||||
| Lecturers: | |||||
| HUALDE BILBAO, JAVIER (Resp) [Mentoring ] | DEL VILLAR OLANO, ALBA MARIA [Mentoring ] | ||||
Contents
Most of the course will be devoted to the analysis of the dynamic dimension of different econometric models. The course starts with a quick review of maximum likelihood inference. Then, the classical methodology employed with time series models, known as Box-Jenkins methodology, is revised. Additionally, we analyze the properties of standard regression models when time series data is employed. In this context, issues like stationarity are crucial, and this will be later analyzed in relation to integrated and cointegrated time series.
The second part of the course will be devoted to discuss different concepts which should belong to the toolkit of any applied econometrician: simultaneous equation models, panel data models and limited dependent variable models.
Each chapter will be illustrated by corresponding empirical applications.
General proficiencies
CG01. Capacity for analysis and synthesis.
CG04. Oral and written communication in a foreign language.
CG07. Capacity to solve problems.
CG09. Capacity to work in teams.
CG11. Work in an international context.
CG12. Ability to retrieve and analyze information from different sources.
CG17. Capacity to self-learning.
CG19. Work with creativity
Specific proficiencies
CE01. Understand the economic institutions as a result and application of theoretical and formal representations of the mechanisms which operate in economics.
CE02. Identify relevant economic information sources.
CE03. Derive from micro and macroeconomic data relevant information impossible to assess by non-specialists.
CE04. Use of professional criteria in the economic analysis, mainly those criteria based on technical tools.
CE05. Draft policy advice reports on international, national or regional economics.
CE10. Evaluate the consequences of alternative policy actions and select the optimal one for a given target.
Learning outcomes
R14. Econometrics and the estimation of economic models. Time series.
| Learning outcomes | Formative activity | Evaluation |
| Understand the advantages and disadvantages of the different applicable econometric tools depending on the particular characteristics of the available data. | Lectures Team work Individual work | Exam Assignments |
| Use the specific econometric tools designed to model time series data. | Computer class session | Computer class assignments |
| Choose the econometric tool which adapts best to the model to be estimated and to the available data. | Discussion of real cases in seminars Report analysis | Assignments Exams |
Methodology
Lectures. Presentation of the main theoretical aspects of the course. Student participation: questions made by the lecturer, brief presentations of previous lectures. (Lecturer)
Individual work (Student)
Classes in computer room. Presentation and revision of exercises made individually and in small groups. Use of econometric packages.
Evaluation (classes, team and individual exercises, final exam)
| Methodology - Activity | On-site | Self preparation |
| A-1 Lectures | 28 | |
| A-2 Classes | 26 | |
| A-3 Debates, tutorials | 12 | |
| A-4 Preparation of assignments | 24 | |
| A-5 Reading of material | 18 | |
| A-6 Individual study | 30 | |
| A-7 Exams | 06 | |
| A-8 Individual tutorials | 06 | |
| Total | 60 | 90 |
Evaluation
| Learning outcomes | Instrument | Weight (%) | Recoverable |
| R14. Econometrics and the estimation of economic models. Time series | Preparation and submission of individual problem sets Test | 30% | Recoverable |
| R14. Econometrics and the estimation of economic models. Time series | Preparation and submission of team problem sets Computer class exercises | 30% | Non-recoverable |
| R14. Econometrics and the estimation of economic models. Time series | Final exam | 40% | Recoverable |
Agenda
1. Maximum likelihood inference
1.1. Estimation.
1.2. Hypothesis testing.
2. Univariate analysis of time series
2.1. Introduction and main concepts.
2.2. Autoregressive (AR) models. Definition and properties.
2.3. Moving average (MA) models. Definition and properties.
2.4. Autoregressive and moving average (ARMA) models
2.5. Nonstationary models. ARIMA models
2.6. Seasonal ARIMA models
2.7. Forecasting.
3. Regression models with time series data
3.1. Finite sample properties of the OLS estimator.
3.2. Asymptotic properties.
3.3. Autocorrelation and heteroskedasticity in regressions with time series data: efficient estimation and robust inference.
3.4. Autoregressive conditional heteroskedasticity (ARCH) models.
3.5. Generalized autoregressive conditional heteroskedasticity (GARCH) models.
3.6. Detection and estimation in GARCH models.
3.7. Forecasting.
4. Regression models with nonstationary time series
4.1. Spurious regression
4.2. Unit root hypothesis testing.
4.3. Cointegration.
4.4. Error correction models.
4.5. Forecasting.
5. Simultaneous equation models
5.1. Instrumental variables estimation.
5.2. Two stage least squares.
5.3. Simultaneity bias of the OLS estimator.
5.4. Identification and estimation.
6. Panel data models
6.1. Simple methods.
6.2. Fixed effects estimator.
6.3. Random effects estimator
Bibliography
Access the bibliography that your professor has requested from the Library.
The main reference for the course is:
Wooldridge, J.M. ¿Introductory econometrics: a modern approach¿. South-Western College Pub; 4 edition (2008).
Alternative useful textbooks are:
Brooks, C.: Introductory Econometrics for Finance (2nd Edition), Cambridge University Press. 2008
Granger, C.W.J.: Forecasting in Business and Economics (2nd Edition) Academic Press. 1986.
Greene, W. H.: Econometric Analysis (7th Edition). Prentice Hall, 2011.
Hamilton J.D.: Time Series Analysis Princeton University Press, 1994
Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer-Verlag 2006.
Tsay, R.S.: Analysis of Financial Time Series. Wiley. 2005.