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Lecture Introductory Econometrics for Finance: Chapter 11 - Chris Brooks

In this chapter, you will learn how to: Describe the key features of panel data and outline the advantages and disadvantages of working with panels rather than other structures; explain the intuition behind seemingly unrelated regressions and propose examples of where they may be usefully employed; contrast the fixed effect and random effect approaches to panel model specification, determining which is the more appropriate in particular cases; construct and estimate panel models in EViews. | ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Chapter 11 Panel Data ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 The Nature of Panel Data Panel data, also known as longitudinal data, have both time series and cross-sectional dimensions. They arise when we measure the same collection of people or objects over a period of time. Econometrically, the setup is where yit is the dependent variable, is the intercept term, is a k 1 vector of parameters to be estimated on the explanatory variables, xit; t = 1, , T; i = 1, , N. The simplest way to deal with this data would be to estimate a single, pooled regression on all the observations together. But pooling the data assumes that there is no heterogeneity – i.e. the same relationship holds for all the data. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 The Advantages of using Panel Data There are a number of advantages from using a full panel technique when a panel of data is . | ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Chapter 11 Panel Data ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 The Nature of Panel Data Panel data, also known as longitudinal data, have both time series and cross-sectional dimensions. They arise when we measure the same collection of people or objects over a period of time. Econometrically, the setup is where yit is the dependent variable, is the intercept term, is a k 1 vector of parameters to be estimated on the explanatory variables, xit; t = 1, , T; i = 1, , N. The simplest way to deal with this data would be to estimate a single, pooled regression on all the observations together. But pooling the data assumes that there is no heterogeneity – i.e. the same relationship holds for all the data. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 The Advantages of using Panel Data There are a number of advantages from using a full panel technique when a panel of data is available. We can address a broader range of issues and tackle more complex problems with panel data than would be possible with pure time series or pure cross-sectional data alone. It is often of interest to examine how variables, or the relationships between them, change dynamically (over time). By structuring the model in an appropriate way, we can remove the impact of certain forms of omitted variables bias in regression results. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Seemingly Unrelated Regression (SUR) One approach to making more full use of the structure of the data would be to use the SUR framework initially proposed by Zellner (1962). This has been used widely in finance where the requirement is to model several closely related variables over time. A SUR is so-called because the dependent variables may seem unrelated across the equations at first sight, but a more careful consideration would allow us to conclude that they are in fact related after all.