Forecasting, Lecture Notes - Managerial Economics, Study notes for Managerial Economics. University of Michigan (MI)

Managerial Economics

Description: FORECASTING, BAROMETRIC MODELS, ECONOMETRIC MODELS, Forecasting Accuracy, Nickel Forecast
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ECN 469: Managerial Economics Professor Mark J. Perry
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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.
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
and the length of time considered. Examples: sales of breakfast cereal or toothpaste vs. new vehicles or
Xmas toys or golf clubs. Construction employment vs. retail. Daily sales of cars versus annual sales
over thirty years.
ECN 469: Managerial Economics Professor Mark J. Perry
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See p. 187 Figure 5.2, plot of annual sales over time. SLS = f (Time). No obvious seasonality or cycle,
minimum random fluctuations are small. We start by estimating a linear trend:
Qt = a + b t,
where Q = annual sales, and t = time trend, where:
YR t___
1990 1
1991 2
1992 3
OLS results: Qt = 98.2 + 8.6 t
Interpretation: As t goes up by one unit (one year), SLS goes up by 8.6 (units or dollars?).
Alternative model: Qt = a + b t + c t2. Interpretation:
If c is insignificant (not different from 0), the trend is linear.
If c is pos and significant, the growth is exponential, SLS grow at an increasing rate.
If c is neg. and significant, SLS grow over time, but at a decreasing rate.
OLS: Qt = 101.8 + 7.0 t + .12 t2
T-statistics indicate that all coefficients are pos and significant, indicating that the quadratic
specification is a better fit, panel b on p. 187.
Forecasting with the OLS equations for YR. 13:
Linear: Q13 = 98.2 + 8.6 (13) = 210.0
Quadratic: Q13 = 101.8 + 7 (13) + .12 (13)2 = 213.08
PROBLEM: Check Station 1 on p. 189:
$50 (1.05)35 =
$50 (1. 06)35 =
Using Time-series Lags:
We can also specify an OLS model using past observations, such as:
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