Transcript Ekonomi Manajerial dalam Perekonomian Global Bab 5 : Peramalan Permintaan Bahan Kuliah Program Pascasarjana-UHAMKA Program Studi Magister Manajemen Dosen : Dr.

Ekonomi Manajerial
dalam Perekonomian Global
Bab 5 :
Peramalan Permintaan
Bahan Kuliah
Program Pascasarjana-UHAMKA
Program Studi Magister Manajemen
Dosen : Dr. Muchdie, PhD in Economics
Jam Konsultasi : Sabtu, 10.00-12.00
Telp : 0818-0704-5737
Pokok Bahasan
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Peramalan Kualitatif : Survei & Jajak Pendapat
Peramalan Kuantitatif : Analisis Deret Waktu
Peramalan Kuantitatif : Teknik Penghalusan
Peramalan Kuantitatif : Metode Barometrik
Peramalan Kuantitatif : Model Ekonometrik
Peramalan Kuantitatif : Peramalan Input-Output
Ringkasan, Pertanyaan Diskusi, Soal-Soal dan Alamat
Situs Internet
• Studi Kasus Gabungan 2 : Mengestimasi dan
Meramalkan Permintaan Listrik di Amerika Serikat
Qualitative Forecasts
• Survey Techniques
– Planned Plant and Equipment Spending
– Expected Sales and Inventory Changes
– Consumers’ Expenditure Plans
• Opinion Polls
– Business Executives
– Sales Force
– Consumer Intentions
Time-Series Analysis
• Secular Trend
– Long-Run Increase or Decrease in Data
• Cyclical Fluctuations
– Long-Run Cycles of Expansion and Contraction
• Seasonal Variation
– Regularly Occurring Fluctuations
• Irregular or Random Influences
Trend Projection
• Linear Trend:
S t = 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)
Seasonal Variation
Ratio to Trend Method
Actual
Trend Forecast
Ratio =
Seasonal
Adjustment
Adjusted
Forecast
=
=
Average of Ratios for
Each Seasonal Period
Trend
Forecast
Seasonal
Adjustment
Seasonal Variation
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
Moving Average Forecasts
Forecast is the average of data from w periods prior to the forecast
data point.
w
At i
Ft  
i 1 w
Exponential Smoothing
Forecasts
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
Root Mean Square Error
Measures the Accuracy of a Forecasting
Method
RMSE 
2
(
A

F
)
 t t
n
Barometric Methods
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National Bureau of Economic Research
Department of Commerce
Leading Indicators
Lagging Indicators
Coincident Indicators
Composite Index
Diffusion Index
Econometric Models
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
Econometric Models
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
Input-Output Forecasting
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
Input-Output Forecasting
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
Input-Output Forecasting
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
Input-Output Forecasting
Total
Requirements
Matrix
1.47
0.96
0.43
0.51
1.81
0.31
0.60
0.72
1.33
Final
Total
Demand Demand
Vector
Vector
90
110
20
=
200
300
100
Input-Output Forecasting
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