ECN 469: Managerial Economics Professor Mark J. Perry

- 1 -

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

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

- 2 -

FITTING A SIMPLE TREND

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

etc...

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:

##### Document information

Uploaded by:
myboy

Views: 4155

Downloads :
2

Address:
Economics

University:
University of Michigan (MI)

Subject:
Managerial Economics

Upload date:
12/10/2011