TAILIEUCHUNG - Lecture Introductory econometrics for finance – Chapter 12: Limited dependent variable models

In this chapter, you will learn how to: Compare between different types of limited dependent variables and select the appropriate model, interpret and evaluate logit and probit models, distinguish between the binomial and multinomial cases, deal appropriately with censored and truncated dependent variables. | Chapter 12 Limited Dependent Variable Models ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 1 Some Examples of when Limited Dependent Variables may be used • There are numerous examples of instances where this may arise, for example where we want to model: • Why firms choose to list their shares on the NASDAQ rather than the NYSE • Why some stocks pay dividends while others do not • What factors affect whether countries default on their sovereign debt • Why some firms choose to issue new stock to finance an expansion while others issue bonds ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 2 Some Examples of when Limited Dependent Variables may be used (Cont’d) • Why some firms choose to engage in stock splits while others do not. • It is fairly easy to see in all these cases that the appropriate form for the dependent variable would be a 0-1 dummy variable since there are only two possible outcomes. There are, of course, also situations where it would be more useful to allow the dependent variable to take on other values, but these will be considered later. ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 3 The Linear Probability Model • We will first examine a simple and obvious, but unfortunately flawed, method for dealing with binary dependent variables, known as the linear probability model. • it is based on an assumption that the probability of an event occurring, Pi , is linearly related to a set of explanatory variables Pi = p(yi = 1) = β1 + β2 x2i + β3 x3i + · · · + βk xki + ui • The actual probabilities cannot be observed, so we would estimate a model where the outcomes, yi (the series of zeros and ones), would be the dependent variable. • This is then a linear regression model and would be estimated by OLS. ‘Introductory Econometrics for Finance’ c Chris Brooks 2013 4 The Linear Probability Model (Cont’d) • The set of explanatory variables could include either quantitative variables or dummies or .

• Kinh tế học
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
• Financial modelling
• Limited dependent variable models
• Linear probability model
• Regression model
• Purpose of econometrics
• Econometric model
• Straight lines
• Statistical foundations
• Classical linear regression model
• Regression analysis
• Multiple linear regression
• Statistical distributions
• 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
• Simulation methods
• Random number generation
• Empirical research project
• Research project