Ekonomi Manajerial dalam Perekonomian Global Bab 4
Estimasi Permintaan
Pokok Bahasan : Estimasi Permintaan • Masalah Identifikasi • Pendekatan Penelitian Pemasaran untuk Estimasi Permintaan • Analisis Regresi – Regresi Sederhana – Regresi Berganda
• Masalah dalam Analisis Regresi • Mengestimasi Permintaan Regresi
Pokok Bahasan: Ramalan Permintaan • Peramalan Kualitatif : – Survei & Jajak Pendapat
• Peramalan Kuantitatif : – Analisis Deret Waktu – Teknik Penghalusan – Metode Barometrik – Model Ekonometrik – Model Input-Output • Ringkasan, Pertanyaan Diskusi, Soal-Soal dan Alamat Situs Internet
Masalah Identifikasi
Observasi Harga-Quantitas TIDAK SECARA LANGSUNG menghasilkan kurva Permintaan dari suatu komoditas
Estimasi Permintaan: Pendekatan Riset Pemasaran • Survei Konsumen : mensurvei konsumen bgm reaksi terhadap jumlah yg diminta jika ada perubahan harga, pendapatan, dll menggunakan kuisioner
• Penelitian Observasi : pengumpulan informasi ttg preferensi konsumen dengan mengamati bagaimana mereka membeli dan menggunakan produk
• Klinik Konsumen : eksperimen lab dimana partisipan diberi sejumlah uang tertentu dan diminta membelanjakannya dalam suatu toko simulasi dan mengamati bagaimana reaksi mereka jika terjadi perubahan harga, pendapatan, selera, dll
• Eksperimen Pasar : mirip klinik konsumen, tetapi dilaksanakan di pasar yang sesungguhnya
Analisis Regresi Scatter Diagram Year
X
Y
1
10
44
2
9
40
3
11
42
4
12
46
5
11
48
6
12
52
7
13
54
8
13
58
9
14
56
10
15
60
Persamaan Regresi : Y = a + bX
Analisis Regresi • Garis Regresi : Line of Best Fit. • Garis Regresi : meminimunkan jumlah dari simpangan kuadrat pada sumbu vertikal (et) dari setiap titik pada garis regresi tersebut. • Metode OLS (Ordinary Least Squares): metode jumlah kuadrat terkecil.
Menggambarkan Garis Regresi
et
Yt
Yˆt
Analisis Regresi Sederhana Metode : OLS Model:
Yt
a
bX t
ˆ Yt
aˆ
ˆ bX t
Yt
ˆ Yt
et
et
Metode OLS Tujuan: menentukan kemiringan (slope) dan intercept yang meminimumkan jumlah simpangan kuadrat (sum of the squared errors). n
n 2 t
e t 1
(Yt t 1
2 ˆ Yt )
n
(Yt t 1
2 ˆ aˆ bX t )
Metode OLS Prosedur Estimasi : n
ˆ b
(Xt
X )(Yt Y )
t 1 n
(Xt t 1
aˆ Y
ˆ bX
X)
2
Metode OLS Contoh Estimasi Yt
Time
Xt
1 2 3 4 5 6 7 8 9 10
10 9 11 12 11 12 13 13 14 15 120
44 40 42 46 48 52 54 58 56 60 500
n
n 10
120
X t 1
Xt n
120 12 10
Y t
-6 -10 -8 -4 -2 2 4 8 6 10
500
t 1
Yt 1 n
Yt Y
n
Yt
n
X -2 -3 -1 0 -1 0 1 1 2 3
n
Xt t 1
n
Xt
(Xt
X )2
( Xt
X )(Yt Y ) 106
30
t 1
n
500 10
50
t 1
( Xt
X )(Yt Y )
4 9 1 0 1 0 1 1 4 9 30
12 30 8 0 2 0 4 8 12 30 106
106 bˆ 30
X )2
( Xt
3.533
aˆ 50 (3.533)(12) 7.60
Metode OLS Contoh Estimasi n
X
n 10
t 1
n
n
n
Xt
120
t 1
Yt
500
t 1
n 2
(Xt
X)
30
( Xt
X )(Yt Y ) 106
t 1 n t 1
Y t
Xt n
Yt 1 n
106 ˆ b 30
120 12 10 500 10
50
3.533
aˆ 50 (3.533)(12) 7.60
Uji Signifikansi Standard Error of the Slope Estimate
sbˆ
2 ˆ (Yt Y )
(n k ) ( X t X )
2 t
e 2
(n k ) ( X t X )
2
Uji Signifikansi Contoh Perhitungan Yˆt
Yt Yˆt
(Yt Yˆt )2
Time
Xt
Yt
1
10
44
42.90
1.10
1.2100
4
2
9
40
39.37
0.63
0.3969
9
3
11
42
46.43
-4.43
19.6249
1
4
12
46
49.96
-3.96
15.6816
0
5
11
48
46.43
1.57
2.4649
1
6
12
52
49.96
2.04
4.1616
0
7
13
54
53.49
0.51
0.2601
1
8
13
58
53.49
4.51
20.3401
1
9
14
56
57.02
-1.02
1.0404
4
10
15
60
60.55
-0.55
0.3025
9
65.4830
30
n
n
et2 t 1
t 1
(Yt Yˆt )2
n
65.4830
(Xt t 1
X )2
30
et
sbˆ
et2
(Yt Yˆ )2 (n k )
(Xt
X)
2
X )2
( Xt
65.4830 (10 2)(30)
0.52
Uji Signifikansi Contoh Perhitungan n
n
et2 t 1
(Yt Yˆt )2
65.4830
t 1
n
(Xt
X )2
30
t 1
sbˆ
(Yt (n k )
2 ˆ Y)
(Xt
X)
2
65.4830 (10 2)(30)
0.52
Uji Signifikansi Perhitungan : t-Statistic
t
bˆ sbˆ
3.53 0.52
6.79
Derajat Bebas = (n-k) = (10-2) = 8
Critical Value at 5% level =2.306
Uji Signifikansi Decomposition of Sum of Squares Total Variation = Explained Variation + Unexplained Variation
(Yt Y )
2
2 ˆ (Y Y )
(Yt
2 ˆ Yt )
Uji Signifikansi Decomposition of Sum of Squares
Uji Signifikansi Koefisien Determinasi
R
2
Explained Variation TotalVariation
R
2
373.84 440.00
0.85
2 ˆ (Y Y )
(Yt Y )
2
Uji Signifikansi Koefisien Korelasi
r
ˆ R withthe signof b 2
1 r 1
r
0.85
0.92
Analisis Regresi Berganda Model:
Y
a b1 X 1 b2 X 2
bk ' X k '
Analisis Regresi Berganda Adjusted Coefficient of Determination
R
2
(n 1) 1 (1 R ) (n k ) 2
Analisis Regresi Berganda Analysis of Variance and F Statistic
F
Explained Variation /(k 1) Unexplained Variation /(n k ) 2
F
R /( k 1) 2 (1 R ) /(n k )
Masalah-Masalah dalam Analisis Regresi • Multicollinearity: Dua atau lebih variabel bebas mempunyai korelasi yang sangat kuat. • Heteroskedasticity: Variance of error term is not independent of the Y variable. • Autocorrelation: Consecutive error terms are correlated.
Durbin-Watson Statistic Uji Autocorrelation n
(et d
et 1 ) 2
t 2 n
et2 t 1
If d=2, autocorrelation is absent.
Langkah-Langkah Estimasi Permintaan dengan Regresi • Spesifikasi Model dengan Cara Mengidentifikasi Variabel-Variabel, misalnya : Qd = f (Px, I, Py, A, T) • Pengumpulan Data • Spesifikasi Bentuk Persamaan Permintaan Linier : Qd = A - a1Px + a2 I + a3 Py + a4 A + a5 T Pangkat : Qd = A(Px)b(Py)c • Estimasi Nilai-Nilai Parameter • Pengujian Hasil
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: 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)
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
Trend Forecast Year Actual 1992.1 12.29 11.00 1993.1 13.87 12.00 1994.1 15.45 14.00 1995.1 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
Ft i 1
At i w
Exponential Smoothing Forecasts Forecast is the weighted average of of the forecast and the actual value from the prior period.
Ft
1
wAt
0 w 1
(1 w) Ft
Root Mean Square Error Measures the Accuracy of a Forecasting Method
RMSE
( At Ft ) n
2
Barometric Methods • • • • • • •
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
It
a2 b2
GNPt
Ct
It
t 1
u1t u2 t
Gt
Reduced Form Equation
GNPt
a1 a2 1 b1
b2
t 1
1
b1
Gt 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 Demand Vector
90 110 20
Total Demand Vector =
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
