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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-stat0,05:14 = 2,14; t-stat0,05:15 = 2,13; t-stat 0,05:16 F-stat 0.05:3:13 = 3,29; F-stat = 3,63; F-stat0,05;2:14 2,12; 0,05;2:14 = 3,59) Dependent Vanabe: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Coefficient Varabe LOG(CENA) LOG PRODUKTIVITATE) BEZDARBS C R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Variable LOG(CENA) LOG(IKP ATTIECIBA) Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 18 after adjustments С 1.733446 0.316520 -0.011502 7.202105 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Std. Error 0.143014 0.181278 0.003949 0.314418 0.995794 Mean dependent var 0.994823 S.D. dependentvar 0.040174 Akake into criterion 0.020981 Schwarz oferin 32.80553 F-statistic 1.840821 Pobstatistic) Coefficient 1.588128 0.073034 0.718542 0.119047 7,940545 0.087802 Std. Emor t-Statistic +-Statistic Prob *0,05;2:13 Dependent Varable: LOG (ALGA) Method: Least Squares Included observations: 17 after adjustments 12.12084 0.0000 LOG (CENA) 1.682538 1.746049 0.1044 LOG(KP_ATTIECIBA) 0.613199 -2.912980 0.0121 BEZDARBS -0.003104 22.90614 0.0000 C 7.906579 0.994508 Mean dependent var 0.993776 S.D. dependent var 0.048459 Akake into criterion 0.032377 Schwarz criterion 31.34517 F-statistic 1.965028 Prob(F-statisto) Variable 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 Prob. 21.71778 0.0000 6.035777 0.0000 117.4808 0.0000 Variable Coefficient LOG(CENA) BEZDARBS C 7.904990 R-squared 0.588888 Adjusted R-squared -3.149484 S.E. of regression -3.001088 Sum squared resid 1358.142 Log likelihood 0.000000 Durbin-Watson stat Std. Error Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Coefficient 1.974777 0.039330 -0.016842 0.002674 0.037767 7.747829 0.122338 0.246822 13.75320 2.484378 0.005984 -0.518692 0.071659 110.3366 t-Statistic 0.996479 Mean dependent var 0.995666 S.D. dependent var 0.036756 Akake into criterion 0.017563 Schwarz criterion 34.31709 Ftatistic 1.765948 Prob(F-statistic) Std. Emor 0.994807 Mean dependent var 0.994085 S.D. dependent var 0.043013 Akake into criterion 0.025901 Schwarz criterion 31.01477 F-statistic 1.929371 Prob(F-statsto) Prob 0.0000 0.0274 0.6127 0.0000 +-Statistic 50.21103 -8.297883 205.1410 7.959451 0.558335 -3.566716 -3.370666 1226.321 0.000000 Prob. 0.0000 0.0000 0.0000 7.959451 0.558335 -3.295855 -3.148817 1340.984 0.000000 =

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-stat0,05:14 = 2,14; t-stat0,05:15 = 2,13; t-stat 0,05:16 F-stat 0.05:3:13 = 3,29; F-stat = 3,63; F-stat0,05;2:14 2,12; 0,05;2:14 = 3,59) Dependent Vanabe: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Coefficient Varabe LOG(CENA) LOG PRODUKTIVITATE) BEZDARBS C R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Variable LOG(CENA) LOG(IKP ATTIECIBA) Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 18 after adjustments С 1.733446 0.316520 -0.011502 7.202105 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Std. Error 0.143014 0.181278 0.003949 0.314418 0.995794 Mean dependent var 0.994823 S.D. dependentvar 0.040174 Akake into criterion 0.020981 Schwarz oferin 32.80553 F-statistic 1.840821 Pobstatistic) Coefficient 1.588128 0.073034 0.718542 0.119047 7,940545 0.087802 Std. Emor t-Statistic +-Statistic Prob *0,05;2:13 Dependent Varable: LOG (ALGA) Method: Least Squares Included observations: 17 after adjustments 12.12084 0.0000 LOG (CENA) 1.682538 1.746049 0.1044 LOG(KP_ATTIECIBA) 0.613199 -2.912980 0.0121 BEZDARBS -0.003104 22.90614 0.0000 C 7.906579 0.994508 Mean dependent var 0.993776 S.D. dependent var 0.048459 Akake into criterion 0.032377 Schwarz criterion 31.34517 F-statistic 1.965028 Prob(F-statisto) Variable 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 Prob. 21.71778 0.0000 6.035777 0.0000 117.4808 0.0000 Variable Coefficient LOG(CENA) BEZDARBS C 7.904990 R-squared 0.588888 Adjusted R-squared -3.149484 S.E. of regression -3.001088 Sum squared resid 1358.142 Log likelihood 0.000000 Durbin-Watson stat Std. Error Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Coefficient 1.974777 0.039330 -0.016842 0.002674 0.037767 7.747829 0.122338 0.246822 13.75320 2.484378 0.005984 -0.518692 0.071659 110.3366 t-Statistic 0.996479 Mean dependent var 0.995666 S.D. dependent var 0.036756 Akake into criterion 0.017563 Schwarz criterion 34.31709 Ftatistic 1.765948 Prob(F-statistic) Std. Emor 0.994807 Mean dependent var 0.994085 S.D. dependent var 0.043013 Akake into criterion 0.025901 Schwarz criterion 31.01477 F-statistic 1.929371 Prob(F-statsto) Prob 0.0000 0.0274 0.6127 0.0000 +-Statistic 50.21103 -8.297883 205.1410 7.959451 0.558335 -3.566716 -3.370666 1226.321 0.000000 Prob. 0.0000 0.0000 0.0000 7.959451 0.558335 -3.295855 -3.148817 1340.984 0.000000 =

MATLAB: An Introduction with Applications
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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Econometrics

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-stat0,05;14 = 2,14; t-stat0,05;15 = 2,13; t-stat0,05;16 = 2,12;                            F-stat0,05;3;13 = 3,29; F-stat0,05;2;13 = 3,63; F-stat0,05;2;14 = 3,59)

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-stat0,05;14 = 2,14; t-stat 0.05:15 = 2,13; t-stat 0,05:16 = 3,29; F-stat0,05;2:13 = 3,63; F-stat ¹0,05;2:14 = 3,59) 2,12; F-stat0,05:3:13 Dependent Vanabe: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Varable LOG(CENA) LOG PRODUKTIVITATE) BEZDARBS C R-squared Adjusted R-squared S.E. of regression Sum squared resid Log Ikelhood Durbin-Watson stat Variable LOG(CENA) LOG(IKP_ATTIECIBA) C Coefficient R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 18 after adjustments 1.733446 0.143014 0.316520 0.181278 -0.011502 0.003949 7.202105 0.314418 Std. Error Coefficient 0.995794 Mean dependent var 0.994823 S.D. dependentvar 0.040174 Akake into criterion 0.020981 Schwarz omerion 32.80553 1.840821 Pobstatistic) 1.588128 0.718542 7.940545 Std. Emor t-Statistic 0.073034 0.119047 0.087802 12.12084 1.746049 -2.912980 22.90614 +-Statistic Prob 0.994508 Mean dependent var 0.993776 S.D. dependent var 0.048459 Akake into criterion 0.032377 Schwarz criterion 31.34517 F-statistic 1.965028 Prob(F-statistic) Dependent Varable: LOG (ALGA) Method: Least Squares Included observations: 17 after adjustments Prob. 21.71778 0.0000 8.035777 0.0000 117.4808 0.0000 Variable 0.0000 LOG (CENA) 0.1044 LOG(KP_ATTIECIBA) 0.0121 0.0000 BEZDARBS C 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 Durpin-Watson stat Variable LOG(CENA) BEZDARBS C Coefficient 7.904990 R-squared 0.588888 Adjusted R-squared -3.149484 S.E. of regression -3.001088 Sum squared resid 1358.142 Log likelihood 0.000000 Durbin-Watson stat 1.682538 0.613199 -0.003104 7.906579 Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Std. Error 0.122338 0.246822 0.005984 0.071659 Coefficient Mean dependent var S.D. dependent var 0.996479 0.995666 0.036756 Akalke info criterion 0.017563 Schwarz criterion 34.31709 F-statistic 1.765948 Prob(F-statistic) 1.974777 -0.016842 7.747829 t-Statistic Std. Emor 0.039330 0.002874 0.037767 13.75320 0.0000 2.484378 0.0274 -0.518692 0.6127 110.3366 0.0000 +-Statistic Prob 7.959451 0.558335 -3.566716 -3.370666 1226.321 0.000000 50.21103 -8.297883 205.1410 0.994807 Mean dependent var 0.994065 S.D. dependent var 0.043013 Akake into criterion 0.025901 Schwarz criterion 31.01477 F-statistic 1.929371 Prob(F-statsto) Prob. 0.0000 0.0000 0.0000 7.959451 0.558335 -3.295855 -3.148817 1340.984 0.000000 =
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-stat0,05;14 = 2,14; t-stat 0.05:15 = 2,13; t-stat 0,05:16 = 3,29; F-stat0,05;2:13 = 3,63; F-stat ¹0,05;2:14 = 3,59) 2,12; F-stat0,05:3:13 Dependent Vanabe: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Varable LOG(CENA) LOG PRODUKTIVITATE) BEZDARBS C R-squared Adjusted R-squared S.E. of regression Sum squared resid Log Ikelhood Durbin-Watson stat Variable LOG(CENA) LOG(IKP_ATTIECIBA) C Coefficient R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 18 after adjustments 1.733446 0.143014 0.316520 0.181278 -0.011502 0.003949 7.202105 0.314418 Std. Error Coefficient 0.995794 Mean dependent var 0.994823 S.D. dependentvar 0.040174 Akake into criterion 0.020981 Schwarz omerion 32.80553 1.840821 Pobstatistic) 1.588128 0.718542 7.940545 Std. Emor t-Statistic 0.073034 0.119047 0.087802 12.12084 1.746049 -2.912980 22.90614 +-Statistic Prob 0.994508 Mean dependent var 0.993776 S.D. dependent var 0.048459 Akake into criterion 0.032377 Schwarz criterion 31.34517 F-statistic 1.965028 Prob(F-statistic) Dependent Varable: LOG (ALGA) Method: Least Squares Included observations: 17 after adjustments Prob. 21.71778 0.0000 8.035777 0.0000 117.4808 0.0000 Variable 0.0000 LOG (CENA) 0.1044 LOG(KP_ATTIECIBA) 0.0121 0.0000 BEZDARBS C 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 Durpin-Watson stat Variable LOG(CENA) BEZDARBS C Coefficient 7.904990 R-squared 0.588888 Adjusted R-squared -3.149484 S.E. of regression -3.001088 Sum squared resid 1358.142 Log likelihood 0.000000 Durbin-Watson stat 1.682538 0.613199 -0.003104 7.906579 Dependent Variable: LOG(ALGA) Method: Least Squares Included observations: 17 after adjustments Std. Error 0.122338 0.246822 0.005984 0.071659 Coefficient Mean dependent var S.D. dependent var 0.996479 0.995666 0.036756 Akalke info criterion 0.017563 Schwarz criterion 34.31709 F-statistic 1.765948 Prob(F-statistic) 1.974777 -0.016842 7.747829 t-Statistic Std. Emor 0.039330 0.002874 0.037767 13.75320 0.0000 2.484378 0.0274 -0.518692 0.6127 110.3366 0.0000 +-Statistic Prob 7.959451 0.558335 -3.566716 -3.370666 1226.321 0.000000 50.21103 -8.297883 205.1410 0.994807 Mean dependent var 0.994065 S.D. dependent var 0.043013 Akake into criterion 0.025901 Schwarz criterion 31.01477 F-statistic 1.929371 Prob(F-statsto) Prob. 0.0000 0.0000 0.0000 7.959451 0.558335 -3.295855 -3.148817 1340.984 0.000000 =
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