EC313 Midterm II

Information

• Time: 60 Minutes.
  • All questions equally weighted.
• Instructions:
  • Carry out tasks using R in RStudio.
  • Save tasks in either .R or .Rmd file, email to j.j.reade@reading.ac.uk.
    • Add comments to your code to explain your steps.
  • Email an .R file with all your commands (from History window), along with any downloaded data files, to me also.

Tasks

1. Direct download task:
   1. Download data from quantmod or Quandl data on the FTSE 100 share price index. Plot your resulting data.
   2. Create at least two models (e.g. ARIMA, exponential smoothing, time series decomposition). Which models appear best and why?
   3. Select an appropriate training period and select the best forecast model for each variable.
   4. Create forecasts for the FTSE closing price for the remaining Fridays this term (weeks 8–11) and submit at http://goo.gl/forms/ovduCxfyo6 (or see below).
2. Indirect data collection task:
   1. Collect data on the popularity of David Cameron from Google Trends, and the Halifax House Price Index (3m/YoY) from http://www.fxstreet.com/economic-calendar/, import the data into R and plot the series (note you need a Google account to download the Google Trends data).
   2. Use the isat function from the gets package to carry out both outlier and break detection on each variable, and implement these variables into an autoregressive model.
   3. Run at one alternative type of model for each variable and comment on each.
   4. Determine the best forecast model for David Cameron’s popularity, and generate forecasts for David Cameron’s popularity this week (week beginning 2nd March) and the remaining weeks of term at http://goo.gl/forms/ovduCxfyo6 (or see below).
   5. Determine the best forecast model for the Halifax House Price Index for February, and submit also at http://goo.gl/forms/ovduCxfyo6 (or see below).