Checking date: 30/04/2024

Course: 2024/2025

Econometric Methods
Bachelor in Statistics and Business (Plan: 400 - Estudio: 203)

Coordinating teacher: RUIZ ORTEGA, ESTHER

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
All the previous courses on Mathematics, Statistics and Econometrics.
An adequate knowledge on the following topics. 1.- Main stylized facts in economic time series. 2.- Quantitative models to represent these series: a) univariate models: deterministic and Arima; b) dynamic single-equation models with exogenous variables; c) VAR models for stationary time series and d) single-equation and multi-equation models for cointegrated time series. 3.- Methodology for building the models in point 2 with real data of the Spanish and Euro Area economies. BY-PRODUCTS. Resolution of problems using data. Knowledge of econometric software for professional work. Analysis of real data through econometric models.
Skills and learning outcomes
Description of contents: programme
Chapter 1. TIME SERIES ECONOMETRICS FOR BUSINESS. PROPERTIES OF TIME SERIES AND STATISTIC FRAMEWORK FOR THEIR ANALYSIS 1.1 Econometrics for economists. Quantitative methods for business 1.2 Random samples and properties of time series 1.3 Components of a time series: trend, seasonality, cycle and disturbances. 1.4 Trend and seasonal. Stationarity transformations 1.7.1 Deterministic trends and seasonality 1.7.2 Segmented trends 1.7.3 Stochastic trend and seasonality Chapter 2. LINEAR UNIVARIATE ARIMA MODELS 2.1 Stationary stochastic processes 2.2 Autocorrelation function and its estimation 2.3 White noise process 2.4 First order autoregressive model: AR (1) 2.5 Generalization to AR (p) models 2.6 Integrated models: ARI (l, p) 2.7 ARMA and ARIMA models Chapter 3. SPECIFICATION, ESTIMATION AND DIAGNOSIS OF ARIMA MODELS 3.1 Box-Jenkins methodology 3.2 Initial specification 3.2.1 Unit root tests 3.2.2 Analysis of correlograms and partial correlograms of the original series and its transformations 3.2.3 Information criteria 3.3 Estimation: hipothesis testing 3.4 Diagnosis of ARIMA models: a) Residual analysis b) Tests of alternative models Chapter 4. STATIONARY MULTIVARIATE MODELS 4.1 Stationary VAR(p) model. 4.2 Granger causality. 4.3 Estimation of VAR models 4.4 VAR models with exogenous variables 4.5 Uniequational dynamic models: autoregressive distributed lag models (ADL) 4.6 Impact and long run multipliers Chapter 5. NON-STATIONARY MULTIVARIATE MODEL 5.1 Models with integrated variables. Spurious regression. 5.2 Cointegration 5.3 Vector equilibrium correction models (VEqCM)
Learning activities and methodology
Lectures with slides available in the web page of the course. Classes for analytical and empirical problems, with additional solved problems in the web page of the course. Classes in the computer room to work with real data.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40

Calendar of Continuous assessment

Extraordinary call: regulations
Basic Bibliography
  • GONZALEZ-RIVERA, G.. Forecasting for Economics and Business. Pearson. 2013
  • PEÑA, D.. Análisis de series temporales. Alianza Editorial . 2005
Additional Bibliography
  • BOX, G.E.P. y JENKINS, G.M.. Time Series Analysis, Forecasting and Control. Edward Elgar. 1970
  • CHAREMZA, W.W. y DEADMA, D.R.. New directions in Econometrics. Cambridge University Press. 1997
  • LÜTKEPOHL, H. y KRÜKZIG, M.. Apllied Time Series Econometrics. Cambridge University Press. 2004
  • MILLS, T.C.. Time Series Techniques for Econometrics. Cambridge University Press . 1990
  • MILLS, T.C.. Modelling Trends and Cycles in Economic Time Series. Palgrave Macmillan . 2003

The course syllabus may change due academic events or other reasons.