CHAPTER 5 - FORECASTING
Case Study:
World's largest nickel mining company (30% market share) with nickel mines in Canada
does 10 yr. forecast in 1990 for: world nickel sales, nickel prices, competition and its own market
share, production costs (labor, extraction, transportation, etc.). Based on forecast, it invests $1B in
Guatemala and Indonesia to take advantage of expected, growing market for nickel. Was investment
profitable? Depends on forecast – will take 7-10 years to assess investment, based on actual outcomes
vs. forecast.
Nickel case illustrates the importance of forecasting. Remember: successful firms are constantly
engaged in long term strategic planning, even 10 years in future, which involves forecasting. Example:
FDA approval.
FORECASTING WITH TIME-SERIES MODELS
Using historical, time-series data to forecast (predict) the future, i.e. extrapolate past trends into the
future.
Examples:
Using time-series data for GDP, auto sales, interest rates, stock prices to predict the
future. Possible limitation of time-series forecast?
Decomposing Time-Series Patterns (Behavior over time):
Four categories: Trend, Cycles, Seasonality, Random shocks
1. Trend (secular component).
Long-run trend in a variable, e.g. real GDP has grown at 3% rate
since WWII, CPI by about 4%, M1 by 5%, S&P500 by 12%, etc. Could decrease: no. of farmers,
manufacturing share of economy, union membership, etc. See p. 185 Figure 5.1.
2. Cycle.
Cyclical movement around the trend, e.g. the 10 business cycles in the U.S. since WWII.
3. Seasonal patterns,
depending on the time of year or "season." Seasonal variation around either the
trend or cycle. Examples of seasonality?
Economic data are either: a) NSA (not seasonally adjusted) or b) SA (seasonally adjusted).
4. Random fluctuations, or shocks,
i.e. unpredictable, irregular, unexplained variation. Even the
most accurate, sophisticated economic model cannot account for, or predict, random fluctuations. The
"errors" (predicted - actual) from a regression are partly due to random fluctuations.
The importance of the trends, cycles, seasonality and randomness depends on the time-series variable