SOLUCIÓN DEL EXAMEN PARCIAL DE ECONOMETRIA I

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José Ángel Morales Ávila
hace 7 años
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1 UNIVERSIDAD NACIONAL DE PIURA FACULTAD DE ECONOMIA SOLUCIÓN DEL EXAMEN PARCIAL DE ECONOMETRIA I 1º El investigador especifica los modelos siguientes: MODELO 1: IMP(t) = a + b IMP(t-1) + c IPM(t) + u(t) MODELO 2: IMP(t) = a + b(0) PBI(t) + b(1) PBI(t-1) b(9) PBI(t-9) + c IPM(t) + u(t) MODELO 3: IMP(t) = a + b PBI(t)^c + d IPM(t) + u(t) se le pide: 1.1. Estimar el modelo 1. (4 puntos) Sample (adjusted): Included observations: 59 after adjustments C IPM IMP(-1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.35E+08 Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) mod1h = mod1rho*sqr(mod1t/(1-mod1t*mod1vb3)) = 2, Sample: Included observations: 59 Autocorrelation Partial Correlation AC PAC Q-Stat Prob. ***. *** * mod1qbp1 = 59* ^2 = 11,64397 mod1qbp2 = 59*( ^ ^2) = 13,34296 Breusch-Godfrey Serial Correlation LM Test: F-statistic Probability Obs*R-squared Probability Dependent Variable: RESID C IPM

2 2 IMP(-1) RESID(-1) R-squared Mean dependent var 4.93E-13 Breusch-Godfrey Serial Correlation LM Test: F-statistic Probability Obs*R-squared Probability Dependent Variable: RESID C IPM IMP(-1) RESID(-1) RESID(-2) R-squared Mean dependent var 4.93E-13 BVI R1-4128,224 R2 169,3952 R3-0, Method: Two-Stage Least Squares Sample (adjusted): Included observations: 59 after adjustments Instrument list: C IPM IPM(-1) C IPM IMP(-1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Sum squared resid 6.88E+08 F-statistic Durbin-Watson stat Prob(F-statistic) Estimar el modelo 2. (4 puntos) Sample (adjusted): Included observations: 51 after adjustments

3 3 C IPM PDL PDL PDL PDL PDL PDL PDL E R-squared Mean dependent var Sample (adjusted): Included observations: 51 after adjustments C IPM PDL PDL PDL PDL PDL PDL 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 F-statistic Durbin-Watson stat Prob(F-statistic) Lag Distribution of PBI i Coefficient Std. Error t-statistic. * * * * * * * * * * Sum of Lags Estimar el modelo 3 considerando: a=-3000, b= , c=2, d=80, aplicando mínimos cuadrados no lineales

4 4 y máxima verosimilitud. (3 puntos) Sample: Included observations: 60 Convergence achieved after 6 iterations IMP=C(1)+C(2)*PBI^C(3)+C(4)*IPM Coefficient Std. Error t-statistic Prob. C(1) C(2) 1.79E E C(3) C(4) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.92E+08 Schwarz criterion Log likelihood Durbin-Watson stat System: MOD3MV Estimation Method: Full Information Maximum Likelihood (Marquardt) Sample: Included observations: 60 Total system (balanced) observations 60 Convergence achieved after 51 iterations Coefficient Std. Error z-statistic Prob. C(1) C(2) 5.28E E C(3) C(4) Log Likelihood Determinant residual covariance Equation: IMP=C(1)+C(2)*PBI^C(3)+C(4)*IPM Observations: 60 R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Sum squared resid 1.86E+08 Durbin-Watson stat Obtener los multiplicadores del modelo 1 y 2. (3 puntos) MODELO 1: IMP(t) = a + b IMP(t-1) + c IPM(t) + u(t) M. I. IPM = c = M. D. 1R IPM = bc = ( ) = M. D. 2R IPM = b^2 c = ( )^2 =

5 5 M. T. IPM = = MODELO 2: IMP(t) = a + b(0) PBI(t) + b(1) PBI(t-1) b(9) PBI(t-9) + c IPM(t) + u(t) M. I. IPM = c = M. I. PBI = b(0) = M. D. 1R PBI = b(1) = M. D. 2R PBI = b(2) = M. D. 9R PBI = b(9) = M. T. PBI = Seleccione la mejor estimación, justifique su respuesta. (3 puntos) 2º Comente y fundamente su respuesta. (3 puntos) Todos los modelos dinámicos requieren de la verificación de autocorelación para determinar el método de estimación adecuado.

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