Economic Forecast for Lowes (the home improvement store)

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Semester Project

Lowes (the home improvement store)




Forecasting is an important process in almost all businesses and is an important skill to possess as an employee, manager or a business owner.   Forecasts are required for developing business plans, developing sales strategies, determining resource requirements, obtaining funding etc.  Developing the skills to forecast and to professionally present forecast results will be critical to your career.  Many times in your career you will certainly be required to either develop a forecast or evaluate forecasts developed by others.


The objective of the project is to simulate a problem you might face in your first job or business.  That is to develop and present the best forecast for a single important data series.  You will explore four methods of forecasting an objective data series and select the best forecast result from among them.   .


The project is due by midnight Central Time April, 28.  You are strongly encouraged to spread the work throughout the semester.  You will note that each class assignment in this course contributes to the class project.  That is, if the class assignments are done correctly they may serve as sections of your final project with a little adjustment.  On of the adjustments would be changing the assignment questions into statements as to what you are doing and why.  After each forecasting technique is covered you should apply it to your forecast project data.


  1. Obtaining the Data for your project[1]

You have been assigned a company and provided a quarterly objective data set (call it Y) that can be found in Doc Sharing.  You will need at least 50 observations for any other data series used in your analysis.


In this course you will learn and apply several forecasting techniques to a single data series that is your forecast objective.  Most of these techniques will only use your objective data series (Y).  There is one technique you will use that will require at least 3 other data series for the same period and length.  These additional data series should be related or “cause” your objective data series.  You should use Macroeconomic variables for these 3 related or “causal” independent data sets (X data sets).  Note that they should be of same length and time covered as the objective data set (Y). You need to start looking for those explanatory variables now!!  This information will be requested from you in the form of a class project proposal (See Project Proposal Description in Doc Sharing.)



  1. Writing an Introduction


After you pick your topic, it is a good idea to write-up a brief abstract to help you focus your thoughts.  The abstract should then be developed or expanded into a Project Proposal  It should include the following points.


  • State your forecasting problem relative to Y data series and why you picked it.
  • State your hypothesis relative to the Y and each X variable relationship. (Why you believe that X causes Y)
  • Information about and description of your data
  • Description of your proposed approach


You will note that the project proposal should fit nicely as the project introduction.  . Hopefully, you will have the proposal that addresses each of the points above.


  1. Methodology

In this section you are explaining to executives what you did to derive the best forecast for your assigned company. You must state that your project will include the application of all four major forecasting techniques:


  • Exponential Smoothing (Chapter 4): You need explain which model you selected and why and how you selected it, etc.

(ii)     Decomposition (Chapter 5):  You need explain what, why, how etc

(iv)    Box-Jenkins (ARIMA)   (Chapter 9):

(iii)    Regression (Chapter 6, 7 and 8): You will need other independent or                         causal variable(s) to help explain the variation in your variable of interest.                       Again, do not underestimate the time the time it will take you to obtain three                             good explanatory X variables. You need to start looking for those explanatory                            variables now


The methodology of obtaining the best forecast from each method includes error measure and residual analysis. When possible you will select the best model form based on this analysis.


Your methodology for selecting the best forecast of the objective or Y variable will be based on the lowest error measures for the forecast period.


  1. Forecast Models and Results

The paper will contain detailed explanations of your thought processes and methodology. At every stage you need to answer the following questions:


  • What did you do? (eg. Winter’s method with these parameter values…)
  • Why did you do it? (Why you chose each specific forecast model used)
  • What did you find? (Interpretation of your results)
    1. Fit period Error Measures (RMSE and MAPE)
    2. Fit period Residual Analysis (Are they random, if not why not?)
    3. Hold out period Error Measures (RMSE and MAPE)
    4. Hold out period Residual Analysis (Are they random, if not why not?)

Repeat the above steps i through iii above for each forecast method and model.  Remember, that your project should include only 4 models and forecasts – one for each method.  Do not include any model tries or failures.  Always only show success in a business report.  Showing failures is a sign of your weakness – do not show attempts or failures!



  1. Conclusion What is your conclusion?  Which method produced the best                                     forecast?  This is the purpose and culmination of the course.


A business report is not of use unless it has a conclusion.  In this part include a table with RMSE and MAPE for the fit period and the forecast period for each of the four forecast methods to support your choice.  Clearly point out the best forecast model results in that table for the forecast period error measures.  Do not leave any tables, plots or statistics stranded without narrative.  Tell the reader why you are showing each table or plot and what it indicates.


  1. Appendix

Your appendix should contain all the relevant supporting information such as the original Y and X data (along with exact web page citations), any data transformations, tests, plots, graphs, diagrams, etc.  It is better to divide the appendix into parts where each part is representing the output from each methodology (ex: Appendix A: Smoothing outputs, Appendix B: Decomposition outputs, etc.) Don’t forget to label or number them.  You will need to refer to them when you are talking about a specific methodology.  It is best to include s short description of each item in the appendix to avoid confusion.


Final Report

Your final report should include an Executive Summary of your findings that will include the conclusions (Which of the techniques works best and why?) in the front of your project.  You can add the results to your introduction and summarize the result into an executive summary. You need to have an Introduction to talk about your topic in above section 2 in greater length (Your proposal should fit well for this).  It will include the reference of your data source in above section 2.  The Conclusion should clearly identify the best estimation methodology and model and explain why it is the best.  Be detailed and complete in all your sections.  This is a case of more is better than less as you explain to me what you have done.  The Appendix should follow showing your work that you did not show in the body of the project including the data used.  The data should appear in an Excel table at the end of the report.  Include the reference of the data source in the Appendix where the data is shown.


Use 12 point New Times Roman double spaced type style for your document.  Use upper case centered type for your title and upper/lower case centered type for your major headings (Executive Summary, Introduction, etc.) and upper/lower case left justified for any subheadings.


The project report should be sent to me as an uploaded Word document at our eCollege site or as an email attachment. Do not send the data is a separate Excel or Minitab file—they must be in an inserted (attached) Excel table.   Do not send raw Minitab files in the project. You should include copied and pasted Minitab results and graphs in the body of your project paper.


Other hints:  Do not use possessive terms in the report – remove “my” and “I” from the formal report.  Make sure that you read over, spell check and grammar check the project.  You must use the type style and heading format that is mentioned above.  Do not include charts or graphs that are “stranded” without explanatory headings and statements.  This project is not a Minitab exercise.  It is a forecasting production project that requires written narration.


Include a cover page with the Title of your study company, the course title (Economics 309 Section #), the date and your name.  Place my name below yours as your instructor for the course.  Upload the finished project with a filename that has your first initial and last name embedded in it.  The format for the upload should be (first initial, last name, Eco309Sp2014Project).


This project report will contribute a maximum of 20 Points of the Final Course Grade.  Remember this is a formal report and you will be graded not only on the required forecast elements but how you organize and report the results of your findings.  Be clear and concise. DO NOT waste your time and overload the project report with work that is not requested or not relevant.

[1] Please remember that there is NOT one right way of preparing a paper. Be precise, explain everything in detail and be structured. The outline is ONLY to remind you about the required parts; its arrangement is completely up to you.