Perform estimation and forecasting for the data in the historical period, 1/2001 – 12/2010

Research Paper Instructions

  1. Define the chosen variable
  • New one-family houses sold (NHS)
  1. Collect the data for the variable
  • Data source
  • economagic.com or other sources
  • Monthly data and not seasonally adjusted (NSA)
  • Time periodJanuary 1975 to the present
  1. Enter the data in Excel according to ForecastX format
  • 1/1/1975 for Jan. 1975 and 2/1/1975 for Feb. 1975
  1. Plot the graph for whole series
  • ForecastX — Preview
  1. ACF for the whole series
  • ForecastX – Analyze
  • Identify the trend
  1. ACF for the first differenced series
  • ForecastX – Analyze – Differencing – Non-Seasonal >1
  • Identify seasonality
  1. Decide the sample period for model selection
  • Historical period: 1/1/1975-12/1/2010
  • Holdout period: 1/1/2011-12/1/2011
  1. Select models according to the data pattern
  • Table 2.1, p. 58
  • Time-series models: Modified naïve model, Winters’ exponential smoothing, Time-series Decomposition, and ARIMA
  • Regression mode:
  • NHS = f (IR, DPI, dummy variables)—need data for IR (30-year mortgage rate), DPI(disposable personal income) and dummy variables (for seasonality)
  1. Perform estimation and forecasting for the data in the historical period, 1/2001 – 12/2010
  • ForecastX – Forecast Method – choose models
  • ForecastX – Statistics — RMSE
  • For regression model, standard report shows the forecasts for independent variables
  1. Compare MAPEs and RMSEs of different models for the historical and holdout periods
  2. Perform ex-ante forecast – 11/2011-12/2012
  • Use the whole series, 1/2001 – 10/2011
  • Either choose the best model or combine two models
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