TAILIEUCHUNG - Lecture Introductory econometrics for finance – 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,. | Chapter 5 Classical linear regression model assumptions and diagnostics ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 1 Violation of the Assumptions of the CLRM • Recall that we assumed of the CLRM disturbance terms: 1. E(ut ) = 0 2. var(ut ) = σ 2 < ∞ 3. cov(ui ,uj ) = 0 4. The X matrix is non-stochastic or fixed in repeated samples cov(ut ,xt ) = 0 5. ut ∼ N(0, σ 2 ) ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 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 alternative techniques which are still valid ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 3 Statistical Distributions for Diagnostic Tests • Often, an F- and a χ2 - version of the test are available. • The F-test version involves estimating a restricted and an unrestricted version of a test regression and comparing the RSS. • The χ2 - version is sometimes called an “LM” test, and only has one degree of freedom parameter: the number of restrictions being tested, m. • Asymptotically, the 2 tests are equivalent since the χ2 is a special case of the F-distribution: χ2 (m) → F (m, T − k) as m (T − k) → ∞ • For small samples, the F-version is preferable. ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 4 Assumption 1: E (ut ) = 0 • Assumption that the mean of the disturbances is zero. • For all diagnostic tests, we cannot observe the disturbances and so perform the tests of the residuals. • The mean of the residuals will always be zero provided that there is a constant term in the .

• Kinh tế học
• Classical linear regression model
• Statistical distributions
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
• Financial modelling
• Multiple linear regression
• Regression analysis
• Classical linear regression
• Mô hình hồi quy tuyến tính
• Hồi quy tuyến tính cổ điển
• Assumptions of the classial regression model
• Least souares estimation
• Nghiên cứu mô hình hồi quy tuyến tính
• Regression model
• Purpose of econometrics
• Basic econometrics
• Classical normal linear regression model
• Dummy variable regression models
• Hypothesis testing
• Econometric modeling
• Diagnostic testing
• Univariate time series modelling
• Stochastic processes
• Modelling long run relationship
• Stochastic non stationarity
• Modelling volatility
• GARCH models
• Teachers’ financial literacy
• Does it impact on financial behaviour
• Long term planning
• The Greater Jakarta
• Partial Least Square Structural Equation Modelling
• Financial literacy on financial behaviour among teachers
• Islamic banking
• Mathematical modeling
• Product musharaka Islamic financial risk
• Modelling and assessment
• Manage risks related
• Lecture Consumer finance
• Rational economic choice
• Financial decisions
• Modelling human financial behavior
• Optimal consumption
• strategic benchmark
• financial planner
• individual accounts
• modelling uncertainty
• parameter lognormal
• performance measurement
• Business Cycles
• Financial Crises
• Dynamic Economic Development
• Unfinished Research Agenda
• Cycle Modelling
• Business Cycle Theory
• Econometric model
• Multivariate models
• Simultaneous equations models
• Switching models
• Switching behaviour
• Panel data
• Fixed effects models
• Limited dependent variable models
• Linear probability model
• Evolution of analytical methods
• Real world inferential insights
• Miller and Modigliani
• Firm’s financial leverage
• Corporate bankruptcy model
• Indian Construction Industry
• Structural Equation Modelling
• Financial and non financial encouragements
• Employees of construction industry
• Help of structure questionnaires
• Modelling
• Virtual Crack Closure
• Technique and Finite
• Predicting the Delamination
• Growth Initiation
• Composite Structures
• Modelling Methodology
• Business Perspective
• Conceptual Data Model
• Role Model
• Financial Instruments
• Practical Risk
• ncreasingly
• economic development
• abandoning traditional
• recruiting large
• tax breaks
• financial incentives
• Introduction to Computing
• Financial Accounting Systems
• Booking Systems
• Management Applications
• Modelling and Simulation
• Reasons for using simulations
• Journa science
• International journal of financial research
• Value at Risk modelling
• Warsaw Stock Exchange
• Realized volatility
• Straight lines
• Statistical foundations
• Simulation methods
• Random number generation