| Center |
| Faculty of Mathematics |
| Departament |
| Economic Analysis |
| Lecturers in charge |
| Sin datos cargados |
| Met. Docent |
| In the theoretical classes they will be appeared the different mathematical models. The explanation of each model will go supported by practical cases and articles. The practical exercises will begin to the students experience in the use of the econometric program Eviews. |
| Met. Avaluació |
| As much the theoretical part as the practice will be evaluated through a work made with Eviews. Those students who are examined in September made exclusively a test written like evaluation means. The concession or not of an extraordinary examination is an exclusive attribution of the professor. |
| Bibliografia |
| Aznar, A. and Trívez, F.J. (1993) Métodos de predicción en economía I y II. Ariel Economía: Barcelona Greene, W. (1998) Análisis econométrico. Prentice Hall Markridakis,s. and Weelwright,s. (1985) Forecasting Methods for Management. Whiley Pulido, A. (1989) Predicción Económica y Empresarial. Pirámide. Uriel, E. (1992) Análisis de series temporales. Modelos ARIMA. Paraninfo, Colección Ábaco Uriel, E. and Peiró, A. (2000) Introducción al análisis de series temporales. Editorial AC |
| Continguts |
| PROGRAM 1. Time Series 1.1. Definition 1.2. Graphic representation 1.3. Main components: trend, season, cycle and error 1.4. Additive model and Multiplicative Model 2. Analysis of short term series 2.1. Exponential Smoothing. 2.2. Other simple methods of forecast. 2.3. Seasonal adjustment 2.4. Practical application through an article. 3. ARIMA models 3.1. Autoregressive process and moving average process 3.2. Integration 3.3. Seasonal models 3.4. Identification of the model, estimation and validation 3.5. Forecasting 3.6. Analysis if intervention 3.7. Practical application through an article. 4. Nonstationary processes 4.1. Unitary roots 4.2. Cointegration 4.3. models of correction of error 4.4. Practical application through an article. 5. Autoregressive vectors 5.1. Theoretical model 5.2. Impulse response analysis 5.3. Variance decomposition of the forecasting errors 5.4. Practical application through an article. Practical 1.- Forecast in a PYME 2.- Analysis of Conjuntural series. 3.- Analysis of the demand in a 24 hours bank. 4.- Analysis of the demand in a supermarket |
| Objetius |
| The content of this module has the fundamental objective of teaching the necessary econometric contents to approach problems related to time series. In more detail, the student will learn to model the behaviour of a time series with the purpose to make forecasting with the minimum variance. Moreover, it will be considered the case in which two or more time series are predicted simultaneously. |
| URL de Fitxa |
| http://www.uv.es/~iarribas |