Econometrics II

Code	14810
Year	3
Semester	S2
ECTS Credits	6
Workload	TP(60H)
Scientific area	Economics
Entry requirements	Knowledge base of the econometric sfotwares, namely Eviews and R Studio.
Mode of delivery	Face-to-face
Work placements	Not applicable
Learning outcomes	Aims: - To develop the skills in, and knowledge of, econometrics necessary for theoretical and empirical work based on cross-sectional, time-series or panel data. - To learn how to conduct empirical studies in economics and financial, related fields using modern econometric techniques. - Develop strategies to adapt the analysed methods to specific problems in empirical applications, such as missing data, data with noise, endogeneity problems and spurious correlations Competences: - Data analysis and manipulation Reason logically and work analytically. - Work with abstract concepts and in a context of generality. - Select and apply appropriate techniques to solve econometric problems. - Team work, written and oral communication. - Computing skills and knowledge of econometric software (EViews and Stata)
Syllabus	1. Autocorrelation 1.1. Nature of the problem 1.2. The 1st order auto-regressive process 1.3. Detection tests: from Durbin-Watson, from Breusch-Godfrey 1.4. Estimation methods 2. Models with lagged variables 2.1. Distributed lag models 2..2. Koyck transformation 2..3. Partial adjustment models 2.4. Adaptive expectations models 2.5. Estimation of autoregressive models 3. Univariate stationary and non-stationary models 3.1. Stationary and Unit Root Tests 3.2. ARMA / ARIMA models 4. Models of conditioned heteroscedasticity and volatility: ARCH / GARCH 5. Stationary and non-stationary multivariate models 5.1. Multivariate models - VAR (Vector auto-regression) 5.2. Granger Causality and Cointegration, 5.3. VECM (vector error correction models) and Johansen Method models; Applications and case studies
Main Bibliography	Principal: Asteriou, Dimitrios e Hall, Stephen G., Applied Econometrics, Palgrave Macmilan, 3rd Ed., 2015. Baltagi, Badi H., Econometric Analysis of Panel Data, John Wiley & Sons, 5th edition, 2013. Recomendada/ Recommended Armstrong, J. Scott, Illusions in regression analysis, in International Journal of Forecasting, Vol. 28, No. 3, July–September 2012, pp 689-694. Greene, William H., Econometric Analysis, Global Edition Pearson Higher Education, 8th edition, 2020. Wooldridge, Jeffrey, Econometric Analysis of Cross Section and Panel Data, The MIT Press, 2nd edition, 2010. Wooldridge, J.M. (2015), "Introductory Econometrics: A Modern Approach", 6th Ed., South Western Davidson, R. and J.G. MacKinnon (2003), Econometric Theory and Methods, Oxford University Press. Greene, W. (2011), Econometric Analysis, Pearson (7th Edition)Publishers.
Teaching Methodologies and Assessment Criteria	Students should attend classes weekly to understand and learn the theoretical arguments used to obtain the main estimation results and to familiarise themselves with the interpretation of results for the selected exercises / practical examples. The students' classification corresponds to the weighting of two teaching-learning components, two tests, the first of which will be held on a date to be agreed with the students in the first class, with a weighting of 50% in the final grade; and the second test to be held on a date proposed at the end of the academic semester by the Course Director, this test corresponding to the 2nd assessment component, with a weighting of 50% in the final grade.
Language	Portuguese. Tutorial support is available in English.