What steps should be taken (mention all of them) in order to successfully specify/estimate a regression equation?
What steps should be taken (mention all of them) in order to successfully specify/estimate a regression equation?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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What steps should be taken (mention all of them) in order to successfully specify/estimate a regression equation?
Transcribed Image Text:Below 4 equations for calculation of wages in Latvia (ALGA, EUR)
are given, depending on productivity (PRODUKTIVITATE, thsd
EUR), price level (CENA), unemployment rate (BEZDARBS, %) and
ratio of GDP of Latvia and the EU (IKP_ATTIECIBA). Which
equation would you use, why (for justification use all the
indicators known from the lectures)? (t-stat = 2,14; t-
F-stat 0,05;3;13
stat 0,05;15
= 2,13; t-stat0,05;16 = 2,12;
= 3,29; F-stat 0,05;2;13
= 3,63; F-stat 0,05;2;14 = 3,59)
Transcribed Image Text:Dependent Vanabe: LOG(ALGA)
Method: Least Squares
Included observations: 17 after adjustments
Vanable
LOG(CENA)
LOG PRODUKTIVITATE)
BEZDARBS
C
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
Coefficient
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log Ikelihood
Durbin-Watson stat
1.733446 0.143014
0.316520 0.181278
-0.011502
7.202105
0.003949
0.314418
Dependent Variable: LOG(ALGA)
Method: Least Squares
Included observations: 17 after adjustments
Variable
LOG(CENA)
BEZDARBS
C
Std. Error
Coefficient
0.995794 Mean dependent var
S.D. dependentvar
0.994823
0.040174 Akake into criterion
0.020981 Schwarz ofterion
32.80553 F-statistic
1.840821 Prob (F-statistic)
1.974777
-0.016842
7.747829
t-Statistic
Std. Emor
12.12084
1.746049
-2.912980
22.90614
t-Statistic
0.039330
50.21103
0.002674 -8.297883
0.037767
205.1410
0.994807 Mean dependent var
0.994065 S.D. dependent var
0.043013 Akaike info criterion
0.025901 Schwarz criterion
31.01477 F-statistic
1.929371 Prob(F-statistic)
Prob
0.0000
0.1044
0.0121
0.0000
Dependent Varable: LOG(ALGA)
Method: Least Squares
Included observations: 17 after adjustments
Prob.
7.959451 R-squared
0.558335 Adjusted R-squared
-3.388885 S.E. of regression
-3.192835 Sum squared resid
1025.827 Log Ikelihood
0.000000 Duroin-Watson stat
0.0000
0.0000
0.0000
Variable
7.959451
0.558335
-3.295855
-3.148817
1340.984
0.000000
LOG (CENA)
LOG(KP_ATTIECIBA)
BEZDARBS
C
Coefficient
Std. Error
1.682538
0.613199
-0.003104 0.005984
7.906579 0.071659
0.996479
0.995666
0.036756
0.017563
34.31709
1.765948
0.122338
0.246822
t-Statistic
13.75320
2.484378
-0.518692
110.3366
Mean dependent var
S.D. dependent var
Akake into criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
Prob.
Dependent Variable: LOG(ALGA)
Method: Least Squares
Included observations: 18 after adjustments
Variable
0.0000
LOG(CENA)
1.588128
0.0274 LOG(IKP_ATTIECIBA) 0.718542
0.6127
C
7.940545
0.0000
Coefficient
7.959451 R-squared
0.558335 Adjusted R-squared
-3.566716 S.E. of regression
-3.370666 Sum squared resid
1226.321 Log likelihood
0.000000 Durbin-Watson stat
Std. Emor
+-Statistic
0.073034 21.71778 0.0000
0.119047 6.035777 0.0000
0.087802 117.4806 0.0000
Prob.
0.994508 Mean dependent var
0.993778 S.D. dependent var
0.048459 Akake into criterion
0.032377 Schwarz criterion
31.34517 F-statistic
1.965028 Prob(F-statistic)
7.904990
0.588888
-3.149484
-3.001068
1358.142
0.000000
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