Westminster Business SchoolBEQM503 Principles of StatisticsIn-Module AssessmentSemester 1 2015/20162Submission Date: Before 13.00, Monday 11th January 2016BEQM503 PRINCIPLES OF STATISTICSIN-MODULE ASSESSMENT ACADEMIC SESSION 2015/16,SEMESTER 1ASSESSMENT OUTLINEYou are required to specify and estimate a multiple regression model that can beused for generating forecasts of some variable that is of interest to you.Broad Overview of the AssessmentYour first task is to identify a variable of interest. You may wish to search throughthe Office for National Statistics web site (http://www.ons.gov.uk/) , the databasescontained on the UK Data Service site (http://ukdataservice.ac.uk/), particularly theOECD Main Economic Indicators dataset (a guide to accessing and downloadingthese data can be found athttp://esds80.mcc.ac.uk/wds_oecd/TableViewer/document.aspx?ReportId=725), orthe various databases referred to on the Biz/ed web site(http://www.bized.co.uk/dataserv/freedata.htm) in order to identify a relevantvariable. In each case you will need to focus on searching for annual time-seriesdata. You may also consult the statistical collection in the Library, or any otherlibrary 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 othermodules that you are studying or previous work/study experience.In any event, the variable should be economics/finance/business/sociological innature, and you should obtain annual observations only (that is, you should notuse daily, weekly, monthly or quarterly data). You will then be required to specifyand estimate a regression model to be used for forecasting purposes, which shouldcontain at least two but not more than four independent variables. You mustobtain a dependent variable with at least 40 annual observations, and thetime-period should extend to at least 2013. That is, the start date of yourdata series must be no later than 1974.NBFor those students for whom this assessment is a referralassessment, you are required to revise, rework and improveyour original assignment, in the light of your critical evaluation3of your initial effort. You may select a different dependentvariable from that selected for your original assessment, shouldthis be appropriate or if you so wish. However, you mustensure that your data extends to 2013 for your dependentvariable.Assessment DetailsThe details of the assessment are as follows (your assessment should clearlyindicate your answers to each of the following 5 parts, each of which should belabelled/headed accordingly):1. Provide a description of the dependent variable you have selected, andprovide a detailed discussion as to why you consider this variable to be ofinterest. You should collect at least 40 annual observations on thisvariable, 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 shouldalso include the data and sources on your independent variables detailed inParts 2 and 3 below (if more than 40 observations are available you shoulduse all of the observations). FAILURE TO USE A DATA SERIES MEETINGTHESE REQUIREMENTS WILL RESULT IN A REDUCTION OF UP TO 20MARKS FROM THE FINAL GRADE AWARDED TO THE ASSESSMENT.You should place an emphasis on deriving an ‘interesting dependent variablethat exhibits considerable variability and would therefore be challenging tomodel. For example, should your selected variable exhibit very little year toyear variability, and hence be of little interest for modelling and forecastingpurposes then you should consider transforming this variable into growth rateform that is, transform the variable so that it measures the percentagechange 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 followingparagraph and the appendix for a more detailed discussion of whatconstitutes an appropriate data series for the purposes of this assessment.)Present a graph of the data on your dependent variable, and place yourdiscussion within the context of this graph, providing an overview of thebroad movements in the data, and if appropriate, some tentative explanationsfor 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. Youmust not use any textbooks as a data source, nor should you use the4dependent variables that have been used in examples that havebeen covered in lectures, seminars and handouts In particular, youshould NOT develop any models of aggregate consumersexpenditure, either for the UK or any other country. If you are in anydoubt as to the appropriateness of your selected data series you shouldconsult the module leader. (10 marks)(The Appendix to these assessment details provides graphs ofunacceptable and acceptable dependent variable data series. ThusFigure 1 presents a data series that would NOT be acceptable for thepurposes of this assessment as it exhibits very predictable year toyear variation, and therefore can be forecast very easily by simpleextrapolation, rather than requiring an econometric model. Figure 2presents a data series exhibiting much more irregular year to yearvariation than is the case with the data in Figure 1, and hence wouldbe an acceptable dependent variable for the purposes of thisassessment. Figure 3 presents the annual percentage change of thedata series in Figure 1 simply derived as the percentage change inthe series from year to year and also would be an acceptable dataseries for the purposes of this assessment. That is, if your selecteddata series is similar in form to that shown in Figure 1, but you stillconsider the data series to be of some intrinsic interest, then youshould 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 carefulconsideration to the appropriate form of the independent variablesin your model.)2. Specify a single equation econometric model that you consider should providean adequate explanation for the annual variation in the dependent variableyou have identified under Part(1) above. Your model should contain at least2 but not more than 4 independent variables. Provide a detailed discussion ofthe expected relevance of the variables that you have selected, and themanner in which you would expect these variables to influence yourdependent variable. At this stage, you should not be concerned about theavailability of data on your proposed independent variables, but rather youshould 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 atextbook nor derived from lectures, seminars, handouts). You should includethese data in a table in an appendix. Again, if any of your independentvariables are measured in monetary terms, be clear as to whether the dataare measured in current or constant prices, and why you consider the price5base you are using to be appropriate.If you cannot locate an appropriate data series for one or more of yourproposed independent variables, feel free to use appropriate proxy variablesthat 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 thatdata are not available over the full 40 (or more)-year period corresponding tothe dependent variable. You should make whatever compromises that youconsider appropriate, and justify these compromises.Using EViews, estimate the initial version of your model, but drop the last 5years from your data set (that is, the years 2009 to 2013 this period will beused to test the forecasting performance of your model). Present the EViewsoutput, and provide a discussion of the main features of your estimatedmodel, using the appropriate diagnostic testing procedures in EViews.In the light of your regression output, discuss any inadequacies in yourmodel. Amend your model appropriately, in terms of re-specifying the form inwhich your independent variables enter the model, disturbance termspecifications, etc. You should not spend too much time finding dataseries on new independent variables, but rather indicate additionalor replacement variables that you might explore, given the time.Re-estimate your model in the light of this discussion, again presenting anddiscussing the EViews output. Provide a clear statement of your finallyselected 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 detaileddiscussion of the process you went through to decide upon the final version ofyour model. (40 marks)4. Using your finally selected model, generate forecasts over the 5 year forecastperiod, and discuss the forecasting performance of your model in the light ofthese forecasts, and in comparison to the actual data values for this 5 yearperiod. You should use the various procedures in EViews for evaluatingforecasting performance. Does this forecasting performance suggest anyfurther improvements that could be made to your model? If so, whatadjustments would you consider making to your model? (15 marks)5. Provide a critical evaluation of the econometric approach to model buildingand 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.6ASSESSMENT CRITERIANB The assessment is not concerned with deriving ‘perfect forecasts.You will be assessed on the approach you take to constructing,estimating and evaluating your proposed model. It is assumed thatyour forecasts will be poor!(a) Originality in the selection of a challenging dependent variable andspecification of the associated regression model.(b) Efficient use of data sources, particularly computerised sources.(c) Use of EViews, and interpretation of output from EViews, particularly theDiagnostic Tests output.(d) Using EViews to generate forecasts, your assessment and evaluation of theforecasts, and your critical evaluation of the econometric approach toforecasting.COURSEWORK SUBMISSIONYou are required to submit your coursework electronically using TURNITIN, which isaccessed via the modules Blackboard site (the Help menu on Blackboard providesinstructions for how to use TURNITIN). Please note that your coursework must besubmitted in a single file, in Word or pdf format, including all appendices (TURNITINdoes not accept Excel files), and should not exceed 10 MB in size.The precise details of the submission process are as follows:Your coursework will automatically be scanned through a text matching system(designed to check for possible plagiarism).? DO NOT attach a CA1 form or any other form of cover sheet;? YOU MUST include your name and student ID on the first page of yourassignment.To submit your assignment:? Log on to Blackboard at http://learning.westminster.ac.uk;? Go to the relevant module Blackboard site;7? Click on the ‘Submit Coursework link in the navigation menu on the left-handside, as advised by the module teaching team;? Click on the link for the relevant assignment;? Follow the instructions.Finance holdsIf on the due date you have a finance hold on your student account, you may not beable to access Blackboard to be able to submit electronically. If this is the case, youmay be able to submit a paper copy to the Registry. Assignments submitted this waywill ONLY be accepted if it is clear that you have a finance hold on the due date. Thepenalties for late submission will still apply.You will be given details by the module teaching team about how and when you willreceive your marks and feedback on your work.REMEMBER:It is a requirement that you submit your work in this way. All courseworkmust be submitted by 1.00 p.m. (13.00) UK time on the due date.If you submit your coursework late but within 24 hours or one workingday of the specified deadline, 10% of the overall marks available for thatelement of assessment will be deducted, as a penalty for late submission,except for work which is marked in the range 40 49%, in which case themark will be capped at the pass mark (40%).If you submit your coursework more than 24 hours or more than oneworking day after the specified deadline you will be given a mark of zerofor the work in question.The Universitys mitigating circumstances procedures relating to the nonsubmissionor late submission of coursework apply to all coursework.8Suggested timetable for completing the assessmentAs this assessment accounts for 70% of the module grade, it requires a substantiallevel of work, and you are strongly advised NOT to leave the completion of thecoursework to the last minute. In the past students who have only started thecoursework a week or two before the submission date, have tended to fail theassessment and hence the module. Thus the following timetable is suggested forthe successful completion of the coursework:By Week 6 of the Module: Identification of an appropriate dependent variable,and a discussion of this dependent variable completed. That is, completion of thefirst publish of Part 1. of the assessment as detailed above.By Week 8 of the Module: The development of an appropriate theoreticaleconometric model that is, the identification of up to 4 independent variables thatmight be relevant in explaining the annual variation in the dependent variable, and adiscussion of the expected individual influences of these variables. That is, a firstpublish of your answer to Part 2. of the assessment.By Week 11 of the Module: A start made on Part 3. of the assessment. Variousmodels estimated, and familiarity developed with your data series and an attemptmade to determine the form of your final forecasting model.Week 12, the Xmas Period and up to submission date: Completion of Parts 4.and 5. of the assessment, and where appropriate, revision of Parts 1. to 3.9APPENDIXFigure 1 Unacceptable dependent variable for the assessmentFigure 2 Acceptable dependent variable for the assessment2000004000006000008000001000000120000014000002200023000240002500026000270002800010Figure 3 Acceptable dependent variable for the assessment annual percentagechange of data in Figure 1 (annual growth rate)-10123456