Transcript Bab 4 : PhD in Economics, 1998, Dept. of Economics, The University of Queensland, Australia. Post Graduate Diploma in Regional Dev.,1994, Dept. of Economics, The Univ.

Bab 4 :
PhD
in Economics, 1998,
Dept. of Economics, The
University of Queensland,
Australia.
Post
Graduate Diploma in
Regional Dev.,1994, Dept.
of Economics, The Univ. of
Queensland, Australia.
MS
in Rural & Regional
Development Planning,
1986, Graduate School,
Bogor Agricultural
University, Bogor
Peramalan
Permintaan
Lecturer : Muchdie, PhD in Economics
 Peramalan
Kualitatif : Survei, Jajak
Pendapat
 Peramalan Kuantitatif :
Analisis Deret Waktu
 Teknik Penghalusan
 Metode Barometrik
 Model Ekonometrik
 Peramalan Input-Output
 Ringkasan, Pertanyaan Diskusi, Soal-Soal dan
Alamat Situs Internet
 Studi Kasus Gabungan 2 : Mengestimasi dan
Meramalkan Permintaan Listrik di Amerika Serikat

Survey
Techniques
 Planned
Plant and Equipment Spending
 Expected Sales and Inventory Changes
 Consumers’ Expenditure Plans
Opinion
Polls
 Business
Executives
 Sales Force
 Consumer Intentions
Secular
Trend
 Long-Run
Cyclical
Increase or Decrease in Data
Fluctuations
 Long-Run
Cycles of Expansion and
Contraction
Seasonal
Variation
 Regularly
Irregular
Occurring Fluctuations
or Random Influences
 Linear
Trend:
St = S0 + b t
b = Growth per time period
 Constant Growth Rate
St = S0 (1 + g)t
g = Growth rate
 Estimation of Growth Rate
lnSt = lnS0 + t ln(1 + g)
Ratio to Trend Method
Actual
Trend Forecast
Ratio =
Seasonal
Adjustment
Adjusted
Forecast
=
=
Average of Ratios for
Each Seasonal Period
Trend
Forecast
Seasonal
Adjustment
Ratio to Trend Method:
Example Calculation for Quarter 1
Trend Forecast for 1996.1 = 11.90 + (0.394)(17) = 18.60
Seasonally Adjusted Forecast for 1996.1 = (18.60)(0.8869) = 16.50
Year
1992.1
1993.1
1994.1
1995.1
Trend
Forecast
Actual
12.29
11.00
13.87
12.00
15.45
14.00
17.02
15.00
Seasonal Adjustment =
Ratio
0.8950
0.8652
0.9061
0.8813
0.8869
Forecast is the average of data from w periods
prior to the forecast data point.
w
Ft  
i 1
At i
w
Forecast is the weighted average of of the
forecast and the actual value from the prior
period.
Ft 1  wAt  (1  w)Ft
0  w 1
Measures the Accuracy of a Forecasting
Method
RMSE 
(A  F )
t
n
t
2
National
Bureau of Economic Research
Department of Commerce
Leading Indicators
Lagging Indicators
Coincident Indicators
Composite Index
Diffusion Index
Single Equation Model of the Demand For Cereal (Good X)
QX = a0 + a1PX + a2Y + a3N + a4PS + a5PC + a6A + e
QX = Quantity of X
PS = Price of Muffins
PX = Price of Good X
PC = Price of Milk
Y = Consumer Income
A = Advertising
N = Size of Population
e = Random Error
Multiple Equation Model of GNP
Ct  a1  b1GNPt  u1t
It  a2  b2 t 1  u2t
GNPt  Ct  It  Gt
Reduced Form Equation
Gt
a1  a2 b2 t 1
GNPt 

 b1 
1  b1
1
1  b1
Three-Sector Input-Output Flow Table
Producing Industry
Supplying
Industry
A
B
C
Value Added
Total
A
20
80
40
60
200
B
60
90
30
120
300
C
30
20
10
40
100
Final
Demand
90
110
20
220
Total
200
300
100
220
Direct Requirements Matrix
Direct
Requirements
=
Input Requirements
Column Total
Producing Industry
Supplying
Industry
A
B
C
A
0.1
0.4
0.2
B
0.2
0.3
0.1
C
0.3
0.2
0.1
Total Requirements Matrix
Producing Industry
Supplying
Industry
A
B
C
A
1.47
0.96
0.43
B
0.51
1.81
0.31
C
0.60
0.72
1.33
Total
Requirements
Matrix
1.47
0.96
0.43
0.51
1.81
0.31
Final
Total
Demand Demand
Vector
Vector
0.60
0.72
1.33
90
110
20
=
200
300
100
Revised Input-Output Flow Table
Producing Industry
Supplying
Industry
A
B
C
A
22
88
43
B
62
93
31
C
31
21
10
Final
Demand
100
110
20
Total
215
310
104