Transcript Business and Economic Forecasting

Business and Economic
Forecasting
Mohammad Arief
1
Overview
Demand Forecasting is a critical
managerial activity
Quantitative
Gives the precise
amount or
percentage
Qualitative
Gives the expected
direction Up, down,
or about the same
2
THE SIGNIFICANCE OF
FORECASTING
Uncertainty
Conditions
limited
Predicting changes in cost,
price, sales, and interest rates
Accurate forecasting can help develop
strategies to promote profitable trends
and to avoid unprofitable ones
3
SELECTING A FORECASTING
TECHNIQUE
The forecasting technique used in any
particular situation depends on a number of
factors.
a. Hierarchy of Forecasts
b. Criteria Used to Select a Forecasting
Technique
c. Evaluating the Accuracy of Forecasting
Models
4
Hierarchy of Forecasts
• The highest level of economic aggregation that is
normally forecast is that of the national economy
(GDP, interest rates, inflation, etc).
» Sectors of the economy (durable
goods)
 Industry forecasts (all auto
manufacturers)
> Firm forecasts (Ford Motor Company)
» Product forecasts (The Ford Focus)
5
Forecasting Criteria
The choice of a particular forecasting method
depends on several criteria:
1. costs of the forecasting method compared with
its gains
2. complexity of the relationships among
variables
3. time period involved
4. accuracy needed in forecast
5. lead time between receiving information and
the decision to be made
6
Accuracy of Forecasting
• The accuracy of a forecasting model is measured by how
close the actual variable, Y, ends up to the forecasting
^
variable, Y.
^
• Forecast error is the difference. (Y - Y)
• Models differ in accuracy, which is often based on the
square root of the average squared forecast error over a
series of N forecasts and actual figures
• Called a root mean square error, RMSE.
» RMSE =
{  (Y - Y)^2 / N
}
The smaller the value of the RMSE, the greater the
accuracy
7
ALTERNATIVE FORECASTING
TECHNIQUES
1.
2.
3.
4.
5.
6.
Deterministic trend analysis
Smoothing techniques
Barometric indicators
Survey and opinion-polling techniques
Macroeconometric models
Stochastic time-series analysis
8
1. Deterministic trend analysis
a. Time-series data,
A sequence of the values of an economic variable at different points in time.
b. Cross-sectional data
An array of the values of an economic variable observed at the same time,
like the data collected in a census across many individuals in the population.
Secular trends
These are long-run trends that cause changes in an
economic data series
Cyclical variations
These are major expansions and contractions in an economic
series that are usually greater than a year in duration
Seasonal effects
Seasonal variations during a year tend to be more or less
consistent from year to year.
Random fluctuations
an economic series may be influenced by
random factors that are unpredictable
9
Secular trends (Trend Sekuler)
• Forecasting model trend sekuler dilakukan
dengan menarik garis secara kasar atau
serampang mengikuti kecenderungan
permintaan yang terjadi secara siklus dari
tahun ke tahun.
10
Cyclical variations (Fluktuasi
Siklus)
• Siklus perubahan atau naik turunnya volume
permintaan selama tahun-tahun yang telah lalu dan
yang akan dating,kita tarik kecenderungannya tentu
disebabkan atau dipengaruhi oleh sejumlah faktor
yang secara periodik dan tetap harus ada atau terjadi
selam periode tahunan yang akan datang.
• Biasanya siklus bisa kita duga sebelumnya bahwa
dengan datangnya permintaan yang meningkat pada
periode tertentu sudah bisa kita prediksi kejadiannya.
11
Seasonal effects
(Metode Variasi Musim)
• Melakukan prakiraan volume permintaan
konsumen di waktu-waktu yang akan datang dapat
didasarkan pada gelombang musiman yang
melekat pada kultur budaya atau kebiasaan dari
masyarakat.
• Tetapi dapat juga karena faktor sifat dan keadaan
alam yang melekat pada iklim atau cuaca.
Misalnya produksi musim semi, gugur, dan musim
hujan bahkan musim kemarau.
12
Random fluctuations (Fluktuasi
Siklus)
• Siklus perubahan atau naik turunnya volume
permintaan selama tahun-tahun yang telah lalu dan
yang akan dating,kita tarik kecenderungannya tentu
disebabkan atau dipengaruhi oleh sejumlah faktor
yang secara periodik dan tetap harus ada atau terjadi
selam periode tahunan yang akan datang
• Biasanya siklus bisa kita duga sebelumnya bahwa
dengan datangnya permintaan yang meningkat pada
periode tertentu sudah bisa kita prediksi kejadiannya.
13
Secular, Cyclical,
Seasonal, and Random
Fluctuations in Time
Series Data
14
Elementary Time Series Models
for Economic Forecasting
NO Trend
1. Naïve Forecast
^
Yt+1 = Yt
»
»
»
Method best when there is
no trend, only random
error
Graphs of sales over time
with and without trends
When trending down, the
Naïve predicts too high






time
Trend





time
15
2. Naïve Forecast With
Adjustments for Secular Trends
Y
^ t+1 = Yt + (Yt - Yt-1 )
» This equation begins with last period’s
forecast, Yt.
» Plus an ‘adjustment’ for the change in the
amount between periods Yt and Yt-1.
» When the forecast is trending up, this
adjustment works better than the pure
naïve forecast method #1.
16
3. Linear and
Linear Trend Growth
4. Constant growth rate
Uses a Semi-log Regression
• Used when trend has a
constant amount of
change
Yt = a + b•T, where
Yt are the actual
observations and
T is a numerical time
variable
• Used when trend is a
constant percentage rate
Log Yt = a + b•T,
where b is the
continuously
compounded growth rate
17
2. SMOOTHING TECHNIQUES
• Smoothing techniques are another type of
forecasting model, which assumes that a
repetitive underlying pattern can be found in
the historical values of a variable that is
being forecast.
• Smoothing techniques work best when a
data series tends to change slowly from one
period to the next with few turning points.
Yt+1 = [Yt + Yt-1 + Yt-2]/3
18
Qualitative Forecasting
3. Barometric Techniques
Direction of sales can be indicated by other variables.
PEAK
Motor Control Sales
peak
Index of Capital Goods
TIME
4 Months
Example: Index of Capital Goods is a “leading indicator”
There are also lagging indicators and coincident indicators
19
Qualitative Forecasting
4. Surveys and Opinion Polling
Techniques
Common Survey Problems
New Products have NO
historical data — Surveys
can assess interest in new
ideas.
• Sample bias—
» telephone, magazine
• Biased questions—
» advocacy surveys
• Ambiguous questions
• Respondents may lie on
questionnaires
20
Qualitative Forecasting
Expert Opinion
The average forecast from several experts
is a Consensus Forecast.
»
»
»
»
Mean
Median
Mode
Proportion positive or negative
21
5. Econometric Models
• Specify the variables in the model
• Estimate the parameters
» single equation or perhaps several stage methods
»Qd = a + b•P + c•I + d•Ps + e•Pc
• But forecasts require estimates for future prices,
future income, etc.
• Often combine econometric models with time series
estimates of the independent variable.
» Garbage in
Garbage out
22
6. Stochastic Time Series
• A little more advanced methods incorporate into time
series the fact that economic data tends to drift
yt = a + byt-1 + et
• In this series, if a is 0 and b is 1, this is the naïve model.
When a is 0, the pattern is called a random walk.
• When a is positive, the data drift. The Durbin-Watson
statistic will generally show the presence of
autocorrelation, or AR(1), integrated of order one.
• One solution to variables that drift, is to use first
differences.
23