You are required to specify and estimate a multiple regression model that can be used for generating forecasts of some variable that is of interest to you.
You are required to specify and estimate a multiple regression model that can be
used for generating forecasts of some variable that is of interest to you.
Broad Overview of the Assessment
Your first task is to identify a variable of interest. You may wish to search through
the Office for National Statistics web site (http://www.ons.gov.uk/) , the databases
contained on the UK Data Service site (http://ukdataservice.ac.uk/), particularly the
OECD Main Economic Indicators dataset (a guide to accessing and downloading
these data can be found at
the various databases referred to on the Biz/ed web site
(http://www.bized.co.uk/dataserv/freedata.htm) in order to identify a relevant
variable. In each case you will need to focus on searching for annual time-series
data. You may also consult the statistical collection in the Library, or any other
library to which you have access, or any other database to which you have access.
Alternatively, you may already have a variable of interest derived from the other
modules that you are studying or previous work/study experience.
In any event, the variable should be economics/finance/business/sociological in
nature, and you should obtain annual observations only (that is, you should not
use daily, weekly, monthly or quarterly data). You will then be required to specify
and estimate a regression model to be used for forecasting purposes, which should
contain at least two but not more than four independent variables. You must
obtain a dependent variable with at least 40 annual observations, and the
time-period should extend to at least 2013. That is, the start date of your
data series must be no later than 1974.
The details of the assessment are as follows (your assessment should clearly
indicate your answers to each of the following 5 parts, each of which should be
1. Provide a description of the dependent variable you have selected, and
provide a detailed discussion as to why you consider this variable to be of
interest. You should collect at least 40 annual observations on this
variable, and the time-period should extend to at least 2013.
You must provide details of the source(s) from which you obtained your data,
in addition to presenting a table of your data in an appendix, which should
also include the data and sources on your independent variables detailed in
Parts 2 and 3 below (if more than 40 observations are available you should
use all of the observations). FAILURE TO USE A DATA SERIES MEETING
THESE REQUIREMENTS WILL RESULT IN A REDUCTION OF UP TO 20
MARKS FROM THE FINAL GRADE AWARDED TO THE ASSESSMENT.
You should place an emphasis on deriving an ‘interesting’ dependent variable
that exhibits considerable variability and would therefore be challenging to
model. For example, should your selected variable exhibit very little year to
year variability, and hence be of little interest for modelling and forecasting
purposes then you should consider transforming this variable into growth rate
form – that is, transform the variable so that it measures the percentage
change from year to year – and use this variable as your dependent variable.
In general a variable expressed in growth rate form, rather than levels form,
presents a more interesting forecasting challenge. (See the following
paragraph and the appendix for a more detailed discussion of what
constitutes an appropriate data series for the purposes of this assessment.)
Present a graph of the data on your dependent variable, and place your
discussion within the context of this graph, providing an overview of the
broad movements in the data, and if appropriate, some tentative explanations
for some of these movements. If your data are measured in monetary terms,
be clear as to whether the data are measured in current or constant prices,
and why you consider the price base you are using to be appropriate. You
must not use any textbooks as a data source, nor should you use the dependent variables that have been used in examples that have
been covered in lectures, seminars and handouts In particular, you
should NOT develop any models of aggregate consumers’
expenditure, either for the UK or any other country. If you are in any
doubt as to the appropriateness of your selected data series you should
consult the module leader. (10 marks)
(The Appendix to these assessment details provides graphs of
unacceptable and acceptable dependent variable data series. Thus
Figure 1 presents a data series that would NOT be acceptable for the
purposes of this assessment as it exhibits very predictable year to
year variation, and therefore can be forecast very easily by simple
extrapolation, rather than requiring an econometric model. Figure 2
presents a data series exhibiting much more irregular year to year
variation than is the case with the data in Figure 1, and hence would
be an acceptable dependent variable for the purposes of this
assessment. Figure 3 presents the annual percentage change of the
data series in Figure 1 – simply derived as the percentage change in
the series from year to year – and also would be an acceptable data
series for the purposes of this assessment. That is, if your selected
data series is similar in form to that shown in Figure 1, but you still
consider the data series to be of some intrinsic interest, then you
should transform this series to a growth rate series, as in Figure 3,
and then use this growth rate series as your dependent variable.
But note that if you adopt this approach you should give careful
consideration to the appropriate form of the independent variables
in your model.)
2. Specify a single equation econometric model that you consider should provide
an adequate explanation for the annual variation in the dependent variable
you have identified under Part(1) above. Your model should contain at least
2 but not more than 4 independent variables. Provide a detailed discussion of
the expected relevance of the variables that you have selected, and the
manner in which you would expect these variables to influence your
dependent variable. At this stage, you should not be concerned about the
availability of data on your proposed independent variables, but rather you
should place an emphasis on the structure of your ideal model.
3. Collect sample data on the independent variables you identified under Part 2.
above, indicating your data source(s) clearly (which again should not be a
textbook nor derived from lectures, seminars, handouts). You should include
these data in a table in an appendix. Again, if any of your independent
variables are measured in monetary terms, be clear as to whether the data
are measured in current or constant prices, and why you consider the price base you are using to be appropriate.
If you cannot locate an appropriate data series for one or more of your
proposed independent variables, feel free to use appropriate proxy variables –
that is, variables that you consider should exhibit similar variability to your
‘ideal’ variables that you discussed in Part (2).
You may find that you identify appropriate independent variables, but that
data are not available over the full 40 (or more)-year period corresponding to
the dependent variable. You should make whatever compromises that you
consider appropriate, and justify these compromises.
Using EViews, estimate the initial version of your model, but drop the last 5
years from your data set (that is, the years 2009 to 2013 – this period will be
used to test the forecasting performance of your model). Present the EViews
output, and provide a discussion of the main features of your estimated
model, using the appropriate diagnostic testing procedures in EViews.
In the light of your regression output, discuss any inadequacies in your
model. Amend your model appropriately, in terms of re-specifying the form in
which your independent variables enter the model, disturbance term
specifications, etc. You should not spend too much time finding data
series on new independent variables, but rather indicate additional
or replacement variables that you might explore, given the time.
Re-estimate your model in the light of this discussion, again presenting and
discussing the EViews output. Provide a clear statement of your finally
selected model, and provide a clear justification for this finally selected model.
The objective of this part of the assessment is for you to provide a detailed
discussion of the process you went through to decide upon the final version of
your model. (40 marks)
4. Using your finally selected model, generate forecasts over the 5 year forecast
period, and discuss the forecasting performance of your model in the light of
these forecasts, and in comparison to the actual data values for this 5 year
period. You should use the various procedures in EViews for evaluating
forecasting performance. Does this forecasting performance suggest any
further improvements that could be made to your model? If so, what
adjustments would you consider making to your model? (15 marks)
5. Provide a critical evaluation of the econometric approach to model building
and forecasting in the light of your answers to Parts 1 to 4 above.
Not to exceed 2000 words, excluding computer output, graphs and appendices.
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