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Syllabus 2019-20 - 11313007 - Econometric Models in Finance (Modelos econométricos en finanzas)

Caption
  • Level 1: Tutorial support sessions, materials and exams in this language
  • Level 2: Tutorial support sessions, materials, exams and seminars in this language
  • Level 3: Tutorial support sessions, materials, exams, seminars and regular lectures in this language
DEGREE: Grado en Finanzas y contabilidad
FACULTY: FACULTY OF LAW AND SOCIAL SCIENCES

ACADEMIC YEAR: 2019-20
SYLLABUS
1. COURSE BASIC INFORMATION
NAME: Econometric Models in Finance
CODE: 11313007 ACADEMIC YEAR: 2019-20
LANGUAGE: English LEVEL: 0
ECTS CREDITS: 6.0 YEAR: 4 SEMESTER: PC
 
2. LECTURER BASIC INFORMATION
NAME: OLMO JIMÉNEZ, MARÍA JOSÉ
DEPARTMENT: U112 - ESTADISTICA E INVESTIGACIÓN OPERATIVA
FIELD OF STUDY: 265 - ESTADÍSTICA E INVESTIGACIÓN OPERATIVA
OFFICE NO.: B3 - 056 E-MAIL: mjolmo@ujaen.es P: 953211909
WEBSITE: -
LANGUAGE: - LEVEL: 1
 
3. CONTENT DESCRIPTION

PART I. THE MULTIPLE LINEAR REGRESSION MODEL

1. Introduction to econometric models

  • Definition of Econometrics
  • The econometric method
  • Econometric models

2. The multiple linear regression model

  • Model especification
  • Ordinary least squares (OLS) estimation
  • Properties of the OLS estimators
  • Goodness of fit
  • Confidence intervals
  • Hypothesis testing
  • Prediction

3. Extensions of the MLR model

  • Extensions to non-linear models 
  • Regression with dummy variables

4. Model selection

  • Specification errors
  • Model construction

5. Non-spherical errors

  • Generalized least squares (GLS) estimation. Properties
  • Confidence intervals and hypothesis testing
  • Normality and residual analysis

6. Heteroskedasticity

  • Nature of the problem. Causes and consequences of heteroskedasticity
  • Heteroskedasticity detection
  • Heteroskedasticity correction

7. Autocorrelation

  • Nature of the problem. Causes and consequencies of autocorrelation
  • Autocorrelation detection
  • Autocorrelacion correction

8. Multicollinearity

  • Nature of the problem
  • Multicollinearity detection
  • Multicollinearity correction

PART II. TIME SERIES ANALYSIS

9. Introduction to time series analysis

  • Stochastic process: description and clasification
  • Autocorrelation and partial autocorrelation functions
  • Sample functions
  • The white noise process

10. Stationary time series models

  • Autoregressive models
  • Moving average models
  • Relationship between AR and MA models
  • Autoregressive- moving average models

11. Non-stationary time series models

  • Non-stationarity in mean
  • Autoregressive integrated moving average models
  • Non-stationarity in variance and covariance

12. ARIMA modelling approach

  • Model identification
  • Model estimation
  • Model diagnostic checking
  • Model validation
  • Forecasting

13. Seasonal models

  • Introduction
  • Stationary seasonal models
  • Non-stationary seasonal models
  • General seasonal models
  • Identification, estimation, diagnostic ckecking, validation and forecasting in seasonal models

4. COURSE DESCRIPTION AND TEACHING METHODOLOGY

Default teaching session language will be Spanish.

All the sessions will take place in a computer laboratory and will be structured in:

  • theoretical sessions, where learning contents will be developed and illustrative examples will be shown using slides, the blackboard and Gretl, a free and open-source econometric software.
  • practical sessions, to solve exercises by Gretl.

Tutorial support sessions, bibliography, additional materials and exams will be available in English. Exchange international students can join regular course in Spanish whenever they wish.

Students with special educational needs should contact the Student Attention Service (Servicio de Atención y Ayudas al Estudiante) in order to receive the appropriate academic support

5. ASSESSMENT METHODOLOGY

The assessment procedure consists of:

  • A continuous evaluation of the practical sessions in which the proposed exercises will be solved by Gretl (75%).
  • A final exam for those students who wish to improve the mark obtain in the continuous evaluation. This exam will be similar to the exercises of the practical sessions and will take place in a computer laboratory (75%).
  • Solution of proposed exercises at the end of each lesson (25%). The mark obtained in this part will be kept for the next examination session (if necessary).

6. BOOKLIST
MAIN BOOKLIST:
  • Introductory econometrics : a modern approach. Edition: 6th ed.. Author: Wooldridge, Jeffrey M.. Publisher: Boston : Cengage Learning, cop. 2016  (Library)
  • Time series analysis: forecasting and control. Edition: 4th ed.. Author: Box, George E. P.. Publisher: Hoboken, N.J. : John Wiley, cop. 2008  (Library)
ADDITIONAL BOOKLIST:
  • Time series analysis: univariate and multivariate methods. Edition: 2nd ed.. Author: Wei, William W. S.. Publisher: Redwood City [etc.] : Addison-Wesley , 2006  (Library)
  • Introduction to linear regression analysis. Edition: 5th ed. Author: Montgomery, Douglas C. Publisher: Hoboken, NJ : Wiley, 2012  (Library)