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. Order Description ASSESSMENT OUTLINE 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 http://esds80.mcc.ac.uk/wds_oecd/TableViewer/document.aspx?ReportId=725), or 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. Assessment Details 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 labelled/headed accordingly): 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. (25 marks) 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. (10 marks) Not to exceed 2000 words, excluding computer output, graphs and appendices. PLACE THIS ORDER OR A SIMILAR ORDER WITH US TODAY AND GET AN AMAZING DISCOUNT :)