TAILIEUCHUNG - Lecture Introductory Econometrics for Finance: Chapter 5 - Chris Brooks

Chapter 5 - Classical linear regression model assumptions and diagnostics. In this chapter, students will be able to understand: Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation, explain the impact of heteroscedasticity or autocorrelation on the optimality of OLS parameter and standard error estimation, distinguish between the Durbin--Watson and Breusch--Godfrey tests for autocorrelation,. | ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Chapter 5 Classical linear regression model assumptions and diagnostics ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Violation of the Assumptions of the CLRM Recall that we assumed of the CLRM disturbance terms: 1. E(ut) = 0 2. Var(ut) = 2 Investigating Violations of the Assumptions of the CLRM We will now study these assumptions further, and in particular look at: - How we test for violations - Causes - Consequences in general we could encounter any combination of 3 problems: - the coefficient estimates are wrong - the associated standard errors are wrong - the distribution that we assumed for the test statistics will be inappropriate - Solutions - the assumptions are no longer violated - we work around the problem so that we use .

• Kinh tế học
• Econometrics for Finance
• Introductory Econometrics for Finance
• Regression model
• Purpose of econometrics
• Classical linear regression model
• Statistical distributions
• Econometric model
• Financial modelling
• Straight lines
• Statistical foundations
• Regression analysis
• Multiple linear regression
• Univariate time series modelling
• Stochastic processes
• Multivariate models
• Simultaneous equations models
• Modelling long run relationship
• Stochastic non stationarity
• Modelling volatility
• GARCH models
• Switching models
• Switching behaviour
• Panel data
• Fixed effects models
• Limited dependent variable models
• Linear probability model
• Simulation methods
• Random number generation
• Empirical research project
• Research project
• Financial data