tarea nº 8 modelos var (p): pais suiza cointegración · tarea nº 8 modelos var (p): pais suiza...
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Tarea nº 8 MODELOS VAR (p): PAIS SUIZA
cointegración Modelo VAR (p) para 2, 3 y 4
variables.
Estimación del modelo VAR (p) con 2 variables.
Para estimar el modelo VAR (p) para 2 variables, se usara la función de
importaciones keynesiana:
IMPORTACIONES = f(PIB)
Donde:
PIB= Producto Interno Bruto expresado en millones $
Para la estimación del modelo se aplicara EViews, los datos históricos de las
variables elegidas se recabaron de la página:
http://datos.bancomundial.org/pais/suiza.
GRAFICO:
BASE DATOS
AÑO
IMPORTACIONES
(Y) PIB(x)2016 268.657,80 659.850
2015 253.110,30 670.656
2014 275.741,70 702.736
2013 321.508,60 685.104
2012 295.960,90 664.902
2011 208.219,90 696.447
2010 176.280,60 580.607
2009 155.378,10 540.966
2008 183.573,90 552.287
2007 161.180,20 477.784
2006 141.399,50 429.477
2005 126.573,70 407.592
2004 115.799,00 393.038
2003 100.239,00 352.356
2002 87.189,00 301.321
2001 84.102,00 278.821
2000 82.521,00 271.852
1999 79.857,00 289.600
1998 80.094,00 294.750
1997 75.960,00 286.673
1996 78.224,00 329.762
1995 80.152,00 341.958
1994 67.997,00 291.883
1993 60.828,00 263.445
1992 65.723,00 271.053
1991 66.485,00 260.542
1990 69.681,00 257.544
1989 58.194,00 201.666
1988 56.363,00 208.800
1987 50.652,00 192.949
1986 41.051,00 154.151
1985 30.696,00 107.580
1984 29.522,00 106.025
1983 29.192,00 110.993
1982 28.678,00 111.313
1981 30.697,00 108.674
1980 36.341,00 118.714
1979 29.356,00 105.565
1978 23.804,00 93.913
1977 17.940,10 67.153
1976 14.775,40 62.875
1975 13.303,40 60.111
1974 14.445,10 52.432
1973 11.625,60 45.497
1972 8.468,40 33.830
1971 7.191,20 27.583
1970 6.374,20 22.953
1969 5.198,80 20.525
1968 4.442,10 18.943
1967 4.067,30 17.740
1966 3.888,50 16.480
1965 3.642,60 15.347
1964 3.553,90 14.481
1963 3.199,00 13.064
1962 2.969,60 11.880
1961 2.662,70 10.713
1960 2.206,30 9.523
ESTIMACION DEL TEST DE JOHANSEN
Date: 10/14/17 Time: 22:33 Sample: 1960 2016 Included observations: 55 Series: PIB IMPORT Lags interval: 1 to 1
Selected
(0.05 level*) Number of
Cointegrating Relations by
Model Data Trend: None None Linear Linear Quadratic
Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend
Trace 1 1 1 1 2 Max-Eig 1 1 1 1 2
*Critical values based on MacKinnon-Haug-Michelis (1999)
Information Criteria by Rank and
Model Data Trend: None None Linear Linear Quadratic
Rank or No Intercept Intercept Intercept Intercept Intercept No. of CEs No Trend No Trend No Trend Trend Trend
Log Likelihood by Rank (rows) and Model (columns)
0 -1245.354 -1245.354 -1242.257 -1242.257 -1241.128 1 -1236.715 -1236.146 -1233.898 -1231.280 -1230.368 2 -1235.854 -1233.861 -1233.861 -1228.290 -1228.290
Akaike Information Criteria by
Rank (rows) and Model (columns)
0 45.43107 45.43107 45.39117 45.39117 45.42282 1 45.26236 45.27805 45.23267 45.17381* 45.17700 2 45.37650 45.37677 45.37677 45.24692 45.24692
Schwarz Criteria by
Rank (rows) and Model (columns)
0 45.57705 45.57705 45.61015 45.61015 45.71480 1 45.55433* 45.60652 45.59764 45.57528 45.61497 2 45.81446 45.88773 45.88773 45.83087 45.83087
INTERPRETACION
Según las estimaciones realizadas por el test johansen:
Por las pruebas TRACE o MAX se debe buscar la aparición de los vectores de
cointegración en ambas pruebas. En el MODELO de IMPORTACIONES
KEYNESIANAS si aparece un VEC.
Otro paso es verificar las tablas inferiores de johansen, para conocer cuántos
rezagos se tiene según akaike y schwarz.
Según el criterio de AKAIKE presenta el modelo 1 rezago. Además también se
determina que el modelo es lineal tiene un intercepto y una tendencia.
VECTOR DE CORRECCION
Vector Error Correction Estimates
Date: 10/14/17 Time: 22:35
Sample (adjusted): 1962 2016
Included observations: 55 after adjustments
Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 IMPORT(-1) 1.000000
PIB(-1) -0.525350
(0.04698)
[-11.1833]
@TREND(60) 1927.629
(606.344)
[ 3.17910]
C -5416.359 Error Correction: D(IMPORT) D(PIB) CointEq1 -0.540950 0.006512
(0.11272) (0.23039)
[-4.79920] [ 0.02826]
D(IMPORT(-1)) 0.418796 0.353987
(0.11976) (0.24477)
[ 3.49707] [ 1.44618]
D(PIB(-1)) -0.061602 0.037635
(0.08269) (0.16902)
[-0.74495] [ 0.22267]
C 3666.262 9735.247
(1981.08) (4049.21)
[ 1.85063] [ 2.40423] R-squared 0.408931 0.054070
Adj. R-squared 0.374162 -0.001573
Sum sq. resids 8.65E+09 3.62E+10
S.E. equation 13026.05 26624.45
F-statistic 11.76143 0.971737
Log likelihood -597.0740 -636.3924
Akaike AIC 21.85724 23.28699
Schwarz SC 22.00323 23.43298
Mean dependent 4836.275 11802.49
S.D. dependent 16465.76 26603.54 Determinant resid covariance (dof adj.) 1.11E+17
Determinant resid covariance 9.55E+16
Log likelihood -1231.280
Akaike information criterion 45.17381
Schwarz criterion 45.57528
Estimation Proc: =============================== EC(D,1) 1 1 IMPORT PIB VAR Model: =============================== D(IMPORT) = A(1,1)*(B(1,1)*IMPORT(-1) + B(1,2)*PIB(-1) + B(1,3)*@TREND(60) + B(1,4)) + C(1,1)*D(IMPORT(-1)) + C(1,2)*D(PIB(-1)) + C(1,3) D(PIB) = A(2,1)*(B(1,1)*IMPORT(-1) + B(1,2)*PIB(-1) + B(1,3)*@TREND(60) + B(1,4)) + C(2,1)*D(IMPORT(-1)) + C(2,2)*D(PIB(-1)) + C(2,3) VAR Model - Substituted Coefficients: =============================== D(IMPORT) = - 0.540949674548*( IMPORT(-1) - 0.525350076844*PIB(-1) + 1927.62876968*@TREND(60) - 5416.35908422 ) + 0.418795685633*D(IMPORT(-1)) - 0.0616015674647*D(PIB(-1)) + 3666.26211627 D(PIB) = 0.00651166532226*( IMPORT(-1) - 0.525350076844*PIB(-1) + 1927.62876968*@TREND(60) - 5416.35908422 ) + 0.353986658097*D(IMPORT(-1)) + 0.0376350160967*D(PIB(-1)) + 9735.24691709
INTERPRETACIÓN
Aplicando el vector de corrección ya se tiene el modelo corregido del VAR mediante
la cointegracion para transformar el modelo en un modelo estacionario.
INTERPRETACION R CUADRADO: el r cuadrado explica la bondad de ajuste de
las variables importaciones y el PIB las cuales son altamente significativas, por lo
cual el modelo explica el 40,89 % y el 5,40% de la variación total de las variables de
estudio.
VECTOR DE CORRECCIÓN DEL MODELO
Vector Autoregression Estimates
Date: 10/14/17 Time: 23:12
Sample (adjusted): 1961 2016
Included observations: 56 after adjustments Standard errors in ( ) & t-statistics in [ ]
IMPORT PIB IMPORT(-1) 0.657065 -0.125998
(0.09675) (0.17654)
[ 6.79161] [-0.71372]
PIB(-1) 0.143544 1.063684
(0.03647) (0.06656)
[ 3.93552] [ 15.9820]
C -3739.885 5943.353
(3136.33) (5722.99)
[-1.19244] [ 1.03851] R-squared 0.970542 0.985508
Adj. R-squared 0.969430 0.984961
Sum sq. resids 1.12E+10 3.72E+10
S.E. equation 14519.30 26493.91
F-statistic 873.0868 1802.088
Log likelihood -614.5800 -648.2604
Akaike AIC 22.05643 23.25930
Schwarz SC 22.16493 23.36780
Mean dependent 77405.89 244374.2
S.D. dependent 83042.59 216041.7 Determinant resid covariance (dof adj.) 1.39E+17
Determinant resid covariance 1.25E+17
Log likelihood -1261.157
Akaike information criterion 45.25561
Schwarz criterion 45.47261
Estimation Proc: =============================== LS 1 1 IMPORT PIB @ C VAR Model: =============================== IMPORT = C(1,1)*IMPORT(-1) + C(1,2)*PIB(-1) + C(1,3) PIB = C(2,1)*IMPORT(-1) + C(2,2)*PIB(-1) + C(2,3) VAR Model - Substituted Coefficients: =============================== IMPORT = 0.657065409444*IMPORT(-1) + 0.143543674596*PIB(-1) - 3739.8854945 PIB = - 0.125998256771*IMPORT(-1) + 1.06368383931*PIB(-1) + 5943.35344513
INTERPRETACIÓN
Realizando una comparación del modelo del VAR sin corregir y el modelo
corregido se puede apreciar diferencias significativas en R-cuadrado donde existe
alta variabilidad en la presentación de resultados finales.
Estimación del modelo VAR (p)
con 4 variables
Función de Pass – Through
Para estimar el modelo VAR (1) para 4 variables, se usara la función de Pass –
Through:
𝑇𝐶 = 𝐼𝑁𝐹𝐿𝐴𝐶𝐼𝑂𝑁 + 𝐴𝑃𝐸𝑅𝑇𝑈𝑅𝐴 𝐶𝑂𝑀𝐸𝑅𝐶𝐼𝐴𝐿 + 𝐼𝑃𝐶
• PIB (Producto Interno Bruto)
• Inflación (%)
• IPC (Índice de precios al consumidor)
• Apertura Comercial (Importaciones + Exportaciones)/PIB
Para la estimación del modelo se aplicara EViews, los datos históricos de las
variables elegidas se recabaron de la página:
http://datos.bancomundial.org/pais/suiza
BASE DE DATOS
TEST DE JOHANSEN
Date: 10/14/17 Time: 22:57 Sample: 1960 2016 Included observations: 55 Series: TC INFLACION APERTURA_COMERCIAL IPC Lags interval: 1 to 1
Selected (0.05 level*) Number of Cointegrating
Relations by Model
Data Trend: None None Linear Linear Quadratic
Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend
Trace 1 1 1 1 2 Max-Eig 1 0 1 0 0
*Critical values based on MacKinnon-Haug-Michelis (1999)
Information Criteria by Rank
and Model Data Trend: None None Linear Linear Quadratic
Rank or No Intercept Intercept Intercept Intercept Intercept No. of CEs No Trend No Trend No Trend Trend Trend
Log Likelihood by Rank (rows)
and Model (columns)
0 294.3655 294.3655 296.0805 296.0805 298.0046 1 307.5018 308.4101 310.0765 310.1768 311.9097 2 314.9985 316.5344 317.2618 322.7332 324.3940 3 317.1737 320.5874 321.2995 328.1058 329.5540 4 317.1820 322.7503 322.7503 329.5790 329.5790
Akaike Information
Criteria by Rank (rows) and
Model (columns) 0 -10.12238 -10.12238 -10.03929 -10.03929 -9.963804 1 -10.30916 -10.30582 -10.25733 -10.22461 -10.17854 2 -10.29085 -10.27398 -10.22770 -10.35393* -10.34160 3 -10.07904 -10.09409 -10.08362 -10.22203 -10.23833 4 -9.788436 -9.845467 -9.845467 -9.948328 -9.948328
Schwarz Criteria by Rank (rows)
and Model (columns)
0 -9.538429* -9.538429* -9.309352 -9.309352 -9.087877 1 -9.433229 -9.393399 -9.235411 -9.166198 -9.010633 2 -9.122952 -9.033082 -8.913811 -8.967048 -8.881720 3 -8.619164 -8.524717 -8.477750 -8.506670 -8.486473 4 -8.036581 -7.947624 -7.947624 -7.904498 -7.904498
INTERPRETACION Según las estimaciones realizadas por el test johansen:
Por las pruebas TRACE o MAX se debe buscar la aparición de los vectores de
cointegración en ambas pruebas. En el MODELO de PASTROUGH si aparece un
VEC.
Otro paso es verificar las tablas inferiores de johansen, para conocer cuántos
rezagos se tiene según akaike y schwarz.
Según el criterio de AKAIKE presenta el modelo 2 rezago. Además también se
determina que el modelo es lineal tiene un intercepto y una tendencia.
VECTOR DE CORRECCION PARA CORREGIR EL MODELO Vector Error Correction Estimates
Date: 10/14/17 Time: 23:09
Sample (adjusted): 1963 2016
Included observations: 54 after adjustments
Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 TC(-1) 1.000000
INFLACION(-1) -8.866641
(23.9513)
[-0.37019]
APERTURA_COMERCIAL(
-1) -65.06912
(19.3994)
[-3.35418]
IPC(-1) -0.105568
(0.05725)
[-1.84400]
@TREND(60) 0.365053
(0.10354)
[ 3.52558]
C -6.120676
Error Correction: D(TC) D(INFLACION) D(APERTURA_COMERCIAL) D(IPC)
CointEq1 -0.051676 0.003822 0.006716 0.159599
(0.03301) (0.00257) (0.00177) (0.13716)
[-1.56547] [ 1.48469] [ 3.78438] [ 1.16356]
D(TC(-1)) 0.395425 -0.019219 0.009370 -1.040804
(0.15091) (0.01177) (0.00811) (0.62708)
[ 2.62019] [-1.63305] [ 1.15482] [-1.65976]
D(TC(-2)) -0.059457 -0.004144 -0.011316 -0.356324
(0.15829) (0.01234) (0.00851) (0.65774)
[-0.37562] [-0.33574] [-1.32969] [-0.54174]
D(INFLACION(-1)) -0.169758 -0.373998 0.042834 -6.300626
(2.58473) (0.20156) (0.13897) (10.7401)
[-0.06568] [-1.85550] [ 0.30824] [-0.58665]
D(INFLACION(-2)) 2.898013 -0.255266 -0.120101 -3.392963
(2.00764) (0.15656) (0.10794) (8.34215)
[ 1.44349] [-1.63047] [-1.11267] [-0.40673]
D(APERTURA_COMERCI
AL(-1)) -0.994882 -0.310790 0.128777 -24.38474
(2.58352) (0.20147) (0.13890) (10.7350)
[-0.38509] [-1.54263] [ 0.92711] [-2.27151]
D(APERTURA_COMERCI
AL(-2)) -1.228089 -0.571730 -0.104967 -25.84163
(2.45133) (0.19116) (0.13179) (10.1858)
[-0.50099] [-2.99087] [-0.79645] [-2.53704]
D(IPC(-1)) -0.017633 -0.000328 0.000148 0.697874
(0.05092) (0.00397) (0.00274) (0.21157)
[-0.34631] [-0.08262] [ 0.05412] [ 3.29851]
D(IPC(-2)) -0.026229 -0.001388 0.006144 0.166712
(0.05427) (0.00423) (0.00292) (0.22551)
[-0.48329] [-0.32797] [ 2.10578] [ 0.73926]
C 0.021521 0.001535 -0.006734 0.155761
(0.05117) (0.00399) (0.00275) (0.21260)
[ 0.42061] [ 0.38466] [-2.44803] [ 0.73264] R-squared 0.184921 0.380326 0.377332 0.651961
Adj. R-squared 0.018200 0.253574 0.249968 0.580771
Sum sq. resids 1.648724 0.010026 0.004766 28.46632
S.E. equation 0.193574 0.015095 0.010407 0.804339
F-statistic 1.109166 3.000561 2.962630 9.158076
Log likelihood 17.57985 155.3490 175.4298 -59.33560
Akaike AIC -0.280735 -5.383298 -6.127028 2.567985
Schwarz SC 0.087595 -5.014967 -5.758698 2.936316
Mean dependent -0.062735 -0.000880 0.002184 1.337397
S.D. dependent 0.195360 0.017472 0.012017 1.242264 Determinant resid covariance (dof adj.) 2.37E-10
Determinant resid covariance 1.04E-10
Log likelihood 314.0523
Akaike information criterion -9.964899
Schwarz criterion -8.307413
Estimation Proc:
=============================== EC(D,1) 1 2 TC INFLACION APERTURA_COMERCIAL IPC VAR Model: =============================== D(TC) = A(1,1)*(B(1,1)*TC(-1) + B(1,2)*INFLACION(-1) + B(1,3)*APERTURA_COMERCIAL(-1) + B(1,4)*IPC(-1) + B(1,5)*@TREND(60) + B(1,6)) + C(1,1)*D(TC(-1)) + C(1,2)*D(TC(-2)) + C(1,3)*D(INFLACION(-1)) + C(1,4)*D(INFLACION(-2)) + C(1,5)*D(APERTURA_COMERCIAL(-1)) + C(1,6)*D(APERTURA_COMERCIAL(-2)) + C(1,7)*D(IPC(-1)) + C(1,8)*D(IPC(-2)) + C(1,9) D(INFLACION) = A(2,1)*(B(1,1)*TC(-1) + B(1,2)*INFLACION(-1) + B(1,3)*APERTURA_COMERCIAL(-1) + B(1,4)*IPC(-1) + B(1,5)*@TREND(60) + B(1,6)) + C(2,1)*D(TC(-1)) + C(2,2)*D(TC(-2)) + C(2,3)*D(INFLACION(-1)) + C(2,4)*D(INFLACION(-2)) + C(2,5)*D(APERTURA_COMERCIAL(-1)) + C(2,6)*D(APERTURA_COMERCIAL(-2)) + C(2,7)*D(IPC(-1)) + C(2,8)*D(IPC(-2)) + C(2,9) D(APERTURA_COMERCIAL) = A(3,1)*(B(1,1)*TC(-1) + B(1,2)*INFLACION(-1) + B(1,3)*APERTURA_COMERCIAL(-1) + B(1,4)*IPC(-1) + B(1,5)*@TREND(60) + B(1,6)) + C(3,1)*D(TC(-1)) + C(3,2)*D(TC(-2)) + C(3,3)*D(INFLACION(-1)) + C(3,4)*D(INFLACION(-2)) + C(3,5)*D(APERTURA_COMERCIAL(-1)) + C(3,6)*D(APERTURA_COMERCIAL(-2)) + C(3,7)*D(IPC(-1)) + C(3,8)*D(IPC(-2)) + C(3,9) D(IPC) = A(4,1)*(B(1,1)*TC(-1) + B(1,2)*INFLACION(-1) + B(1,3)*APERTURA_COMERCIAL(-1) + B(1,4)*IPC(-1) + B(1,5)*@TREND(60) + B(1,6)) + C(4,1)*D(TC(-1)) + C(4,2)*D(TC(-2)) + C(4,3)*D(INFLACION(-1)) + C(4,4)*D(INFLACION(-2)) + C(4,5)*D(APERTURA_COMERCIAL(-1)) + C(4,6)*D(APERTURA_COMERCIAL(-2)) + C(4,7)*D(IPC(-1)) + C(4,8)*D(IPC(-2)) + C(4,9) VAR Model - Substituted Coefficients: =============================== D(TC) = - 0.051676496843*( TC(-1) - 8.86664102347*INFLACION(-1) - 65.0691192478*APERTURA_COMERCIAL(-1) - 0.105567839989*IPC(-1) + 0.365053332758*@TREND(60) - 6.12067620874 ) + 0.395424635041*D(TC(-1)) - 0.0594572904333*D(TC(-2)) - 0.169757553392*D(INFLACION(-1)) + 2.89801333151*D(INFLACION(-2)) - 0.994881538238*D(APERTURA_COMERCIAL(-1)) - 1.22808931233*D(APERTURA_COMERCIAL(-2)) - 0.0176331374443*D(IPC(-1)) - 0.0262294345678*D(IPC(-2)) + 0.021520829525 D(INFLACION) = 0.00382187734797*( TC(-1) - 8.86664102347*INFLACION(-1) - 65.0691192478*APERTURA_COMERCIAL(-1) - 0.105567839989*IPC(-1) + 0.365053332758*@TREND(60) - 6.12067620874 ) - 0.0192186737757*D(TC(-1)) - 0.0041443646352*D(TC(-2)) - 0.373998322958*D(INFLACION(-1)) - 0.255265567053*D(INFLACION(-2)) - 0.310789806375*D(APERTURA_COMERCIAL(-1)) - 0.571730019819*D(APERTURA_COMERCIAL(-2)) - 0.000328035880539*D(IPC(-1)) - 0.00138806184488*D(IPC(-2)) + 0.00153478789444 D(APERTURA_COMERCIAL) = 0.00671641279231*( TC(-1) - 8.86664102347*INFLACION(-1) - 65.0691192478*APERTURA_COMERCIAL(-1) - 0.105567839989*IPC(-1) + 0.365053332758*@TREND(60) - 6.12067620874 ) + 0.00937000140813*D(TC(-1)) - 0.0113163206735*D(TC(-2)) + 0.0428343868178*D(INFLACION(-1)) - 0.120100570799*D(INFLACION(-2)) + 0.128776620805*D(APERTURA_COMERCIAL(-1)) - 0.104967470472*D(APERTURA_COMERCIAL(-2)) + 0.000148143946376*D(IPC(-1)) + 0.00614446776695*D(IPC(-2)) - 0.00673419323136 D(IPC) = 0.159598669479*( TC(-1) - 8.86664102347*INFLACION(-1) - 65.0691192478*APERTURA_COMERCIAL(-1) - 0.105567839989*IPC(-1) + 0.365053332758*@TREND(60) - 6.12067620874 ) - 1.04080417826*D(TC(-1)) - 0.356324110363*D(TC(-2)) - 6.30062570625*D(INFLACION(-1)) - 3.39296300366*D(INFLACION(-2)) - 24.3847446181*D(APERTURA_COMERCIAL(-1)) - 25.841626375*D(APERTURA_COMERCIAL(-2)) + 0.697874026019*D(IPC(-1)) + 0.166711991125*D(IPC(-2)) + 0.155761250398
VAR
Vector Autoregression Estimates
Date: 10/14/17 Time: 23:10
Sample (adjusted): 1962 2016
Included observations: 55 after adjustments
Standard errors in ( ) & t-statistics in [ ]
TC INFLACION APERTURA_C
OMERCIAL IPC TC(-1) 1.254868 -0.019840 0.013128 -1.158921
(0.13970) (0.01110) (0.00783) (0.57290)
[ 8.98243] [-1.78809] [ 1.67701] [-2.02290]
TC(-2) -0.374261 0.018248 -0.009013 0.695193
(0.14700) (0.01168) (0.00824) (0.60282)
[-2.54600] [ 1.56298] [-1.09427] [ 1.15323]
INFLACION(-1) -0.716391 0.462525 0.267132 -2.859139
(2.55341) (0.20280) (0.14308) (10.4712)
[-0.28056] [ 2.28071] [ 1.86705] [-0.27305]
INFLACION(-2) -0.560721 -0.102778 0.170746 0.986459
(1.76632) (0.14029) (0.09897) (7.24343)
[-0.31745] [-0.73263] [ 1.72517] [ 0.13619]
APERTURA_COMERCIAL(
-1) 0.481192 -0.333782 0.722168 -28.88001
(2.51555) (0.19979) (0.14096) (10.3159)
[ 0.19129] [-1.67065] [ 5.12336] [-2.79955]
APERTURA_COMERCIAL(
-2) -1.386437 0.060700 -0.001845 2.875831
(2.74402) (0.21794) (0.15376) (11.2529)
[-0.50526] [ 0.27852] [-0.01200] [ 0.25556]
IPC(-1) -0.024049 0.000319 -0.002422 1.476926
(0.04759) (0.00378) (0.00267) (0.19516)
[-0.50533] [ 0.08452] [-0.90813] [ 7.56768]
IPC(-2) 0.019794 -0.000471 0.003084 -0.480604
(0.04799) (0.00381) (0.00269) (0.19679)
[ 0.41249] [-0.12348] [ 1.14682] [-2.44227]
C 0.561339 0.025318 -0.062380 1.651554
(0.38455) (0.03054) (0.02155) (1.57699)
[ 1.45972] [ 0.82895] [-2.89496] [ 1.04728] R-squared 0.979354 0.661272 0.910742 0.999254
Adj. R-squared 0.975764 0.602363 0.895218 0.999124
Sum sq. resids 1.617114 0.010201 0.005077 27.19513
S.E. equation 0.187496 0.014891 0.010506 0.768894
F-statistic 272.7561 11.22528 58.66973 7699.271
Log likelihood 18.94235 158.2558 177.4414 -58.67350
Akaike AIC -0.361540 -5.427482 -6.125141 2.460855
Schwarz SC -0.033067 -5.099010 -5.796669 2.789327
Mean dependent 2.132837 0.025851 -0.009945 69.58160
S.D. dependent 1.204362 0.023615 0.032456 25.97776 Determinant resid covariance (dof adj.) 1.92E-10
Determinant resid covariance 9.40E-11
Log likelihood 322.7503
Akaike information criterion -10.42728
Schwarz criterion -9.113394
=============================== LS 1 2 TC INFLACION APERTURA_COMERCIAL IPC @ C VAR Model: =============================== TC = C(1,1)*TC(-1) + C(1,2)*TC(-2) + C(1,3)*INFLACION(-1) + C(1,4)*INFLACION(-2) + C(1,5)*APERTURA_COMERCIAL(-1) + C(1,6)*APERTURA_COMERCIAL(-2) + C(1,7)*IPC(-1) + C(1,8)*IPC(-2) + C(1,9) INFLACION = C(2,1)*TC(-1) + C(2,2)*TC(-2) + C(2,3)*INFLACION(-1) + C(2,4)*INFLACION(-2) + C(2,5)*APERTURA_COMERCIAL(-1) + C(2,6)*APERTURA_COMERCIAL(-2) + C(2,7)*IPC(-1) + C(2,8)*IPC(-2) + C(2,9) APERTURA_COMERCIAL = C(3,1)*TC(-1) + C(3,2)*TC(-2) + C(3,3)*INFLACION(-1) + C(3,4)*INFLACION(-2) + C(3,5)*APERTURA_COMERCIAL(-1) + C(3,6)*APERTURA_COMERCIAL(-2) + C(3,7)*IPC(-1) + C(3,8)*IPC(-2) + C(3,9) IPC = C(4,1)*TC(-1) + C(4,2)*TC(-2) + C(4,3)*INFLACION(-1) + C(4,4)*INFLACION(-2) + C(4,5)*APERTURA_COMERCIAL(-1) + C(4,6)*APERTURA_COMERCIAL(-2) + C(4,7)*IPC(-1) + C(4,8)*IPC(-2) + C(4,9) VAR Model - Substituted Coefficients: =============================== TC = 1.25486790604*TC(-1) - 0.374260901058*TC(-2) - 0.716391090754*INFLACION(-1) - 0.560721191185*INFLACION(-2) + 0.481191772824*APERTURA_COMERCIAL(-1) - 1.38643660524*APERTURA_COMERCIAL(-2) - 0.0240488151613*IPC(-1) + 0.0197939965564*IPC(-2) + 0.56133870291 INFLACION = - 0.0198398751671*TC(-1) + 0.0182479995505*TC(-2) + 0.462524812119*INFLACION(-1) - 0.102778114939*INFLACION(-2) - 0.333782166796*APERTURA_COMERCIAL(-1) + 0.0606999420677*APERTURA_COMERCIAL(-2) + 0.00031947044009*IPC(-1) - 0.000470602216406*IPC(-2) + 0.0253178596413 APERTURA_COMERCIAL = 0.0131277536043*TC(-1) - 0.00901342281425*TC(-2) + 0.267132127341*INFLACION(-1) + 0.170745887495*INFLACION(-2) + 0.722167865053*APERTURA_COMERCIAL(-1) - 0.00184485433106*APERTURA_COMERCIAL(-2) - 0.00242169825401*IPC(-1) + 0.0030836384426*IPC(-2) - 0.0623802476415 IPC = - 1.15892070525*TC(-1) + 0.695193382558*TC(-2) - 2.85913941415*INFLACION(-1) + 0.986459327364*INFLACION(-2) - 28.880005907*APERTURA_COMERCIAL(-1) + 2.87583081727*APERTURA_COMERCIAL(-2) + 1.47692634905*IPC(-1) - 0.480604429139*IPC(-2) + 1.65155421914