School

Kent State UniversityDepartment

Business Administration InterdisciplinaryCourse Code

BUS 10123Professor

Eric Von HendrixStudy Guide

FinalThis

**preview**shows page 1. to view the full**4 pages of the document.**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

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