1. EKONOMETRIKA TERAPAN (Pertemuan #2)
Pengajar:
Dr. Vera Lisna, S.Si, M.Phil

2. MODEL REGRESI LINIER BERGANDA
DATA PANEL
(Single Equation)

3. Metodologi Ekonometrika Berdasarkan Jenis DataCS TS
PANEL
Univariate
Multivariate
Correlation
Regression
Multivariate analysis
Regression
AR, MA
ARMA
ARIMA
ARCH, GARCH
Correlation
Regression
Granger Causality
VAR
ECM, VECM
Pooled
Fixed Effect
Random Effect

4. Some Well-Known Panel Data Sets
Pooled Data
Pooling of TS and CS data
Combination of TS and CS data
Micropanel data
Longitudinal data
a study over time of a variable or group of subjects
Event history analysis
studying the movement over time of subjects through successive states of conditions
Cohort analysis
e.g. following the career path of 1965 graduates of a business school
PANEL DATA
PANEL DATA REGRESSION MODEL

5. Advantages of panel data
Panel data relate to individuals over time  there is a bound to heterogeneity  controlling for individual heterogeneity
By combining TS and CS data  more informative, more variability, less collinearity among the varibles, more df, and more efficiency
Note: df ↑ : distribusi mendekati normal
By studying the repeated CS of observation  Better able to study the dynamics of adjustments
Better able to identify and measure effects that are simply not detectable in pure CS or TS data
Allow us to construct and test more complicated behavioral models than purely CS or TS data
By making data available for several thousand units  minimize bias
PANEL DATA CAN ENRICH EMPIRICAL ANALYSIS

6. Limitations of panel data
Design and data collection problems  includes coverage (incomplete account of individual or period), nonresponse (lack of respondent cooperation or interviewer error), recall (respondent not remembering correctly), frequency of interviewing, interview spacing, etc.
Distortions of measurement errors  due to unclear questions, memory errors, inappropriate informants, misrecording or responses, and interviewer effects
Selectivity problems (due to self –selectivity, nonresponse, and attrition)  data berkurang
Short time-series dimensions
Cross-section dependence  panel unit root tests are suggested to account for CS dependence

7. Jenis-jenis data panel
PANEL DATA
Balanced panel
Unbalanced panel
PANEL DATA
Short panel
Long panel
N > T
T > N

8. Metode estimasi data panel
PANEL DATA
Pooled OLS
LSDV
FE Within Group
REM
Pool all obs
Pool all obs but allow each CS unit to have its own (intercept) dummy varb
Pool all obs, but express each varb in each CS as a deviation from its mean value and then estimates OLS regression on such mean corrected
Pool all obs and assume that the intercept value are a random drawing from a much bigger population of CS data

9. Metode Estimasi Data Panel
PANEL DATA
FEM
REM
PLS
B/W estimator WG
GLS
LSDV
2-way error comp

10. Teori Data Panel Statis
Kelemahan data CS:
Hanya dapat diamati pada satu titik
Contoh analisis pertumbuhan ekonomi: PDRB, investasi, tingkat konsumsi hanya di satu titik  perkembangan ekonomi antar waktu tidak dapat dilihat
Kelemahan data TS:
Variabel-variabel yang diobservasi secara agregat dati suatu uni invidu  estimasi mungkin bias
Kelebihan data panel:
Verbeek (2004):
Kombinasi data S dan TS  jumlah observasi lebih besar
Model data panel  variabel penjelas dilihat dari dua dimensi  parameter yang diestimasi lebih akurat
Hsiao (2004)
Lebih informatif
Mengurangi kolinearitas antar variabel penjelas
Meningkatkan df  meningkatkan efisiensi
Mampu mengontrol heterogenitas individy

11. Teori Data Panel Statis
Dua pendekatan aplikasi data panel:
Fixed effect model (FEM)
Random Effect Model (REM)
Perbedaan FEM dan REM:
Asumsi ada/tidak korelasi antara error (e) dan variabel penjelas (X)
Contoh:
yit = αi + βXit+ εit
Komponen error:
One way error component model:
yit = αi + βXit+ it + uit
Two way error component model:
yit = αi + βXit+ it + it + uit

12. FEM - Metode PLS
Menggunakan gabungan seluruh data (pooled)  jumlah observasi = n x t ; n = jumlah unit CS
t = jumlah series
Model: yit = αi + βXit+ εit ; αi = α i
Formula perhitungan:

13. FEM - Metode PLS Kelemahan:
Parameter β bias  arah kemiringan (slope) PLS tidak sejajar dengan garis regresi masing-masing individu (tidak dapat membedakan observasi yang sama pada periode berbeda)
Group 2
α2 + βxit
Group 1
α1 + βxit
Slope bias
xit
yit

14. An illustrative example of panel data
Data are taken from investment theory proposed by Y. Grunfeld (1958: “The Determinants of Corporate Investment”, unpublished Ph.D. thesis)

16. Grunfeld Investment Function
Real value of the firm X2 Y
Real gross investment
Real capital stock X3
Grunfeld Investment Function
Yit = β1 + β2X2it + β3X3it + uit i = 1, 2, 3, 4  CS identifier
t = 1, 2, …, 20  TS identifier ↓
80 observasi
balanced panel
Initial assumptions: 1) Xkit nonstochastic
2) E(uit)  N(0, 2)
Panel data
Balanced
Unbalanced
ti = t I
Not all of ti = t

17. Estimation of Grunfeld Investment Function
Yit = β1 + β2X2it + β3X3it + uit i = 1, 2, 3, 4
t = 1, 2, …, 20
Futher assumptions (intercept, slope, error term):
The intercept and slope coefficients are constant across time and space and the error term captures differences over tima and individuals  bjit = bj k,i,t and not all uit = u
The slope coefficients are constant but the intercept varies over individuals
The slope coefficients are constant but the intercept varies over individuals and time
All coefficients (the intercept and slope) vary over individuals
The intecept and slope coefficient vary over individuals and time

18. 1) ALL COEFFICIENTS CONSTANT ACROSS TIME AND INDIVIDUALS
Dependent Variable: Y?
Method: Pooled Least Squares
Date: 10/24/14 Time: 09:06
Sample:
Included observations: 20
Cross-sections included: 4
Total pool (balanced) observations: 80
Variable
Coefficient
Std. Error
t-Statistic
Prob.   C
0.0357
X1?
0.0000
X2?
R-squared
    Mean dependent var
Adjusted R-squared
    S.D. dependent var
S.E. of regression
    Akaike info criterion
Sum squared resid
    Schwarz criterion
Log likelihood
    Hannan-Quinn criter.
F-statistic
    Durbin-Watson stat
Prob(F-statistic)
𝒀 =−𝟔𝟑.𝟑𝟎𝟒𝟏+𝟎.𝟏𝟏𝟎𝟏𝑿𝟐+𝟎.𝟑𝟎𝟑𝟒𝑿𝟑
se = ( ) (0.0137) (0.0493)
t = ( ) (8.0188) (6.1545)
R2 =
DW =
n = 80
df = n – 3 = 77
All coeffs are indivually statistically signif
All slope coeffs have pos signs
R2 value is high
DW is quite low  perhaps there is autocor

19. Dependent Variable: Y?
Method: Pooled Least Squares
Date: 10/24/14 Time: 09:06
Sample:
Included observations: 20
Cross-sections included: 4
Total pool (balanced) observations: 80
Variable
Coefficient
Std. Error
t-Statistic
Prob.   C
0.0357
X1?
0.0000
X2?
R-squared
    Mean dependent var
Adjusted R-squared
    S.D. dependent var
S.E. of regression
    Akaike info criterion
Sum squared resid
    Schwarz criterion
Log likelihood
    Hannan-Quinn criter.
F-statistic
    Durbin-Watson stat
Prob(F-statistic)
Dependent Variable: Y
Method: Panel Least Squares
Date: 10/24/14 Time: 09:19
Sample:
Periods included: 20
Cross-sections included: 4
Total panel (balanced) observations: 80
Variable
Coefficient
Std. Error
t-Statistic
Prob.   C
0.0357 X1
0.0000 X2
R-squared
    Mean dependent var
Adjusted R-squared
    S.D. dependent var
S.E. of regression
    Akaike info criterion
Sum squared resid
    Schwarz criterion
Log likelihood
    Hannan-Quinn criter.
F-statistic
    Durbin-Watson stat
Prob(F-statistic)