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# BUS 10123 Chapter Notes - Chapter 11: Panel Data, Time Series, MulticollinearityExam

Department
Course Code
BUS 10123
Professor
Eric Von Hendrix
Study Guide
Final

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1. (a) There are several advantages from using panel data if they are available:
â€¢ 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.
â€¢ It is often of interest to examine how variables, or the relationships
between them, change dynamically (over time). To do this using pure
time-series data would often require a long run of data simply to get a
sufficient number of observations to be able to conduct any meaningful
hypothesis tests. But by combining cross-sectional and time-series data,
one can increase the number of degrees of freedom, and with it the
power of the test, by employing information on the dynamic behaviour of
a large number of entities at the same time. The additional variation
introduced by combining the data in this way can also help to mitigate
problems of multicollinearity that may arise if time-series are modelled
individually.
â€¢ By structuring the model in an appropriate way, we can remove the
impact of certain forms of omitted variables bias in regression results.
(b) The seemingly unrelated regression (SUR) framework was 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.
The example given in the book relates to the flow of funds (i.e. net new
money invested) to portfolios (mutual funds) operated by two different
investment banks. The flows could be related since they are, to some extent,
substitutes (if the manager of one fund is performing poorly, investors may
switch to the other). The flows are also related because the total flow of
money into all mutual funds will be affected by a set of common factors (for
example, related to peoples' propensities to save for their retirements).
Although we could entirely separately model the flow of funds for each bank,
we may be able to improve the efficiency of the estimation by capturing at
least part of the common structure in some way. Under the SUR approach,
one would allow for the contemporaneous relationships between the error
terms in the two equations for the flows to the funds in each bank by using a
generalised least squares (GLS) technique. The idea behind SUR is essentially