Time-Series Forecasting and Index Numbers


This chapter discusses the general use of forecasting in business, several tools that are available for making business forecasts, the nature of time-series data, and the role of index numbers in business, thereby enabling you to:

  1. Differentiate among various measurements of forecasting error, including mean absolute deviation and mean square error, in order to assess which forecasting method to use
  2. Describe smoothing techniques for forecasting models, including naïve, simple average, moving average, weighted moving average, and exponential smoothing
  3. Determine trend in time-series data by using linear regression trend analysis, quadratic model trend analysis, and Holt's two-parameter exponential smoothing method
  4. Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method
  5. Test for autocorrelation using the Durbin-Watson test, overcoming it by adding independent variables and transforming variables and taking advantage of it with autoregression
  6. Differentiate among simple index numbers, unweighted aggregate price index numbers, weighted aggregate price index numbers, Laspeyres price index numbers, and Paasche price index numbers by defining and calculating each


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