• Author / Uploaded
• MARIANELA GUTIERREZ

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AÑO 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

TRIMESTRE

1990 I II III IV 1991 I II III IV 1992 I II III IV 1993 I II III IV 1994 I II III IV 1995 I II III IV 1996 I II III IV 1997 I II III IV 1998 I II III IV 1999 I II III IV 2000 I

PBI REAL 40440.5083 40316.9357 33934.5107 36800.0454 36289.6567 40194.3657 39444.3286 38925.649 38553.8855 39420.0979 36833.071 39209.9455 38458.7714 41646.5672 40683.6662 41303.9951 43373.6774 46709.949 45094.0649 46865.98 47280.4688 50715.5342 48795.6753 48744.3132 47884.6254 51913.6803 50072.6008 51138.3832 50364.8715 56186.4676 53279.5826 54197.3698 51486.8763 54478.7932 53514.8455 53709.4784 51214.6336 55517.781 53196.0898 56448.2362 54674.8224

IGBVL 0.32154875 pbi 0.8358818 7.71475805 11.0837881 15.3909979 19.1578595 23.6514255 26.5155782 30.896074 34.1549264 37.2935221 41.5589049 46.7511594 51.2155051 54.8261201 57.9662374 61.5029831 63.6176142 65.503346 66.8836046 68.8268966 70.6514897 72.0727469 73.7245881 76.8314148 78.4279416 80.5005821 82.4535761 83.9804243 85.8648727 87.0265406 87.7825737 90.8543631 92.4397629 92.7645732 93.0554909 93.9331218 95.1027304 95.9555783 96.523114 97.5790777

igbvl -0.003 -0.158 0.084 -0.014 0.108 -0.019 -0.013 -0.010 0.022 -0.066 0.065 -0.019 0.083 -0.023 0.015 0.050 0.077 -0.035 0.039 0.009 0.073 -0.038 -0.001 -0.018 0.084 -0.035 0.021 -0.015 0.116 -0.052 0.017 -0.050 0.058 -0.018 0.004 -0.046 0.084 -0.042 0.061 -0.031

1.600 8.229 0.437 0.389 0.245 0.235 0.121 0.165 0.105 0.092 0.114 0.125 0.095 0.070 0.057 0.061 0.034 0.030 0.021 0.029 0.027 0.020 0.023 0.042 0.021 0.026 0.024 0.019 0.022 0.014 0.009 0.035 0.017 0.004 0.003 0.009 0.012 0.009 0.006 0.011

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86

II III IV 2001 I II III IV 2002 I II III IV 2003 I II III IV 2004 I II III IV 2005 I II III IV 2006 I II III IV 2007 I II III IV 2008 I II III IV 2009 I II III IV 2010 I II III IV 2011 I II

58255.5507 54621.7511 54654.5831 51760.367 58431.0641 56119.6473 57268.4974 55137.7412 62307.2268 58404.3545 59923.6247 58249.2701 65202.4881 60551.6835 61589.172 60913.8156 67639.7129 63145.7529 66070.5049 64340.8894 71310.3676 67229.8262 71090.0707 69670.7641 75823.9355 72806.2691 76296.8622 73353.8223 80625.6307 80689.0813 85024.4647 80813.1006 89146.4429 88439.8384 90523.6218 82894.9333 88427.1789 88282.9843 92978.9204 87418.2071 96887.2615 96918.8232 101155.708 94996.2821 102176.042

98.1577883 99.6776139 100.127514 101.072336 100.618151 100.549653 100 99.976359 100.616104 101.22929 101.51584 103.370486 102.796157 103.230017 104.037247 106.2192 107.171675 107.386307 107.658867 108.21 108.76 108.58 109.27 110.92 110.75 110.75 110.51 111.19 112.47 113.85 114.85 117.36 118.88 120.93 122.49 122.97 122.52 122.39 122.79 123.90 124.53 125.28 125.34 127.20 128.16

0.065 -0.062 0.001 -0.053 0.129 -0.040 0.020 -0.037 0.130 -0.063 0.026 -0.028 0.119 -0.071 0.017 -0.011 0.110 -0.066 0.046 -0.026 0.108 -0.057 0.057 -0.020 0.088 -0.040 0.048 -0.039 0.099 0.001 0.054 -0.050 0.103 -0.008 0.024 -0.084 0.067 -0.002 0.053 -0.060 0.108 0.000 0.044 -0.061 0.076

0.006 0.015 0.005 0.009 -0.004 -0.001 -0.005 0.000 0.006 0.006 0.003 0.018 -0.006 0.004 0.008 0.021 0.009 0.002 0.003 0.005 0.005 -0.002 0.006 0.015 -0.002 0.000 -0.002 0.006 0.011 0.012 0.009 0.022 0.013 0.017 0.013 0.004 -0.004 -0.001 0.003 0.009 0.005 0.006 0.000 0.015 0.008

87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106

III IV 2012 I II III IV 2013 I II III IV 2014 I II III IV 2015 I II III IV 2016 I II

102605.532 107274.127 100668.84 107960.879 109624.763 113018.504 105426.474 114687.553 115427.296 120824.273 110661.147 116848.923 117541.018 122228.618 112702.443 120615.441 121452.06 128027.359 117757.091 125123.434

129.95 131.28 132.58 133.287076 134.81345 134.756059 136.015179 136.980108 138.629804 138.609653 140.609675 141.705446 142.425851 143.078513 144.856367 146.728777 147.986947 149.371004 151.086756 151.631894

0.004 0.046 -0.062 0.072 0.015 0.031 -0.067 0.088 0.006 0.047 -0.084 0.056 0.006 0.040 -0.078 0.070 0.007 0.054 -0.080 0.063

0.014 0.010 0.010 0.005 0.011 0.000 0.009 0.007 0.012 0.000 0.014 0.008 0.005 0.005 0.012 0.013 0.009 0.009 0.011 0.004

Chart Title 9.000 8.000 7.000 6.000 5.000 4.000 3.000 2.000 1.000 0.000 -1.000

1

6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 01 1

PRACTICA EN CLASE BUSTINZA MIRANDA MARIELA FLORES LOPEZ ALEJANDRA 1. FORMATO DE TIEMPO gen TRIMESTRE = _n+197 format trimestre %tq tsset trimestre ,quaterly 2. ESTACIONALIDAD

-.2

-.1

pbi 0

.1

.2

tw tsline pbi

2000q1

2005q1

3. AUTOCORRELOGRAMA

elations of pbi 0.00 0.50

1.00

ac pbi

2010q1 2015q1 TRIMESTRE

2020q1

2025q1

Autocorrelations of pbi -0.50 0.00 0.50 -1.00

3. AUTOCORRELACION PARCIAL 0

10

20 Lag

30

40

pac pbiBartlett's formula for MA(q) 95% confidence bands pac igbvl 4. TEST DE ESTACIONALIDAD dfuller pbi pperron pbi

Según el test de dfuller y pperron ambas variables son estacionar

5. COINTEGRACION vecrank pbi igbvl , trend(constant) lag(4) max

Si tiene cointegracion porque el trace statistic es mayor al 0.05 esto tambien dice cuentas ecuaciones de correccion genera , en este caso se genera 1 ecuacion de correccion de errores 6. REZAGOS varsoc pbi igbvl , maxlag(4)

7. CREAR MODELO VEC , PORQUE SI TIENE COINTEGRACION

vec pbi igbvl, trend(constant) lags(4) Vector error-correction model Sample:

2001q1 - 2026q1

Log likelihood = Det(Sigma_ml) = Equation D_pbi

Number of obs AIC HQIC SBIC

547.6362 6.69e-08 Parms 8

RMSE

R-sq

chi2

P>chi2

.026982

0.9387

1423.885

0.0000

= = = =

101 -10.50765 -10.32945 -10.06748

Vector error-correction model Sample:

2001q1 - 2026q1

Log likelihood = Det(Sigma_ml) = Equation

Number of obs AIC HQIC SBIC

547.6362 6.69e-08 Parms

D_pbi D_igbvl

8 8

Coef.

RMSE

R-sq

chi2

P>chi2

.026982 .010542

0.9387 0.7892

1423.885 348.2403

0.0000 0.0000

Std. Err.

z

= = = =

101 -10.50765 -10.32945 -10.06748

P>|z|

[95% Conf. Interval]

D_pbi

ESTA VA AL FINAL

_ce1 L1.

-.3609974

.0957694

-3.77

0.000

-.548702

-.1732928

pbi LD. L2D. L3D.

-.7098756 -.715852 -.8200001

.0849971 .078098 .0536035

-8.35 -9.17 -15.30

0.000 0.000 0.000

-.8764668 -.8689213 -.9250611

-.5432845 -.5627827 -.7149391

igbvl LD. L2D. L3D.

-.1660359 -.0404011 -.0201617

.1822757 .0072439 .0048585

-0.91 -5.58 -4.15

0.362 0.000 0.000

-.5232898 -.0545989 -.0296842

.191218 -.0262033 -.0106393

_cons

.0007013

.0027953

0.25

0.802

-.0047774

.00618

_ce1 L1.

-.3589814

.0374179

-9.59

0.000

-.4323191

-.2856437

pbi LD. L2D. L3D.

.2237435 .1049359 .092785

.033209 .0305135 .0209433

6.74 3.44 4.43

0.000 0.001 0.000

.158655 .0451305 .0517368

.288832 .1647413 .1338332

igbvl LD. L2D. L3D.

-.3394587 .0074967 .0001226

.0712166 .0028303 .0018983

-4.77 2.65 0.06

0.000 0.008 0.949

-.4790408 .0019495 -.003598

-.1998767 .0130439 .0038431

_cons

-.0007052

.0010922

-0.65

0.518

-.0028458

.0014353

D_igbvl

EN LOS VAR O VEC EL P-VALUE SE ANALIZA EN FORMA CONJUNTA PARA TODOS LOS COEFICIENTES CON EL TEST DE WALT VAR BASICO varbasic pbi igbvl, lags(1/4) varbasic, igbvl, igbvl .03 .02 .01 0 -.01

varbasic, igbvl, pbi

varbasic, igbvl, igbvl

varbasic, igbvl, pbi

varbasic, pbi, igbvl

varbasic, pbi, pbi

.03 .02 .01 0 -.01

.03 .02 .01 0 -.01 0

2

4

6

8

0

2

4

6

8

step 95% CI

orthogonalized irf

Graphs by irfname, impulse variable, and response variable

LA PRIMERA VARIABLE ES EL IMPULSO LA SEGUNDA VARIABLE ES LA RESPUESTA varirf graph irf, impulse(pbi) response(igbvl)

varbasic, pbi, igbvl .1

0

-.1

-.2 0

2

4

6

step 95% CI

impulse-response function (irf)

Graphs by irfname, impulse variable, and response variable

ESTE ES EL QUE INTERESA

8

FUNCION IMPULSO RESPUESTA EXAMEN irf graph irf, lstep(0) ustep(10)

INTERPRETAR

PRONOSTICO PARA 10 PERIODOS =(10)

varbasic, igbvl, igbvl

varbasic, igbvl, pbi

varbasic, pbi, igbvl

varbasic, pbi, pbi

1

.5

0

-.5

1

.5

0

-.5 0

2

4

6

8

0

2

4

6

step 95% CI

impulse-response function (irf)

Graphs by irfname, impulse variable, and response variable

TEST DE GRANGER - PARA SABER LA CAUSALIDAD vargranger . vargranger Granger causality Wald tests Equation

Excluded

chi2

pbi pbi

igbvl ALL

25.06 25.06

4 4

0.000 0.000

igbvl igbvl

pbi ALL

26.801 26.801

4 4

0.000 0.000

HO: VAR1 no causa var2-causalidad -cuando prob>0.05 HI1: VAR1 SI CAUSA VAR 2

df Prob > chi2

8

REGLA : SI P-VALUE >0.05 SE ACEPTA LA HIPOTESIS NULA (NO HAY CAUSALIDAD) Se observa que en ambos casos el p value es menor al 0.05 lo que significa que el pbi si causa al indice general y el indice general si causa en el pbi .

ASUMIENDO QUE SI TIENE CAUSALIDAD EL SEGUIENTE PASO ES : SIGNIFICANCIA CONJUNTA TEST DE WALT VEREMOS SI C2 C3 C4 C5 DE FORMA CONJUNTA INFLUYEN EN EL PBI (VARIABLES DEPENDIENTE) PARA ESO SE APLICA EL WALD TEST HIPOTESIS H0: C(1)=C(2)=C(3)=C(4) = 0 H1: C(1)=C(2)=C(3)=C(4) =/ 0

SIGNIFICA QUE LOS REZAGOS NO INFLUYEN EN FORMA CONJ SIGNIFICA QUE SON DIFERENTES DE CERO ENTONCES ESTOS R

TEST DE WALD var pbi lgbvl,lags(1/4) varwle

Si la PROB es mayor al 0.05 Si la PROB es menor al 0.05

TEST DE NORMALIDAD

PROB>CHI2 ES MAYOR QUE

EN ESTE CASO NO HAY AUTO

HETEROSCEDASTICIDAD

CORRECCION SI NO HAY CAUSALIDAD Eliminar observaciones de 1 año eliminar observaciones de 5 años Eliminar 10 años si no se corrige se hace la primera diferencia despues la segunda diferencia tercera diferencia

CTICA EN CLASE

ZA MIRANDA MARIELA ES LOPEZ ALEJANDRA

0

2

igbvl 4

6

8

tw tsline igbvl

2000q1

2025q1

orrelations of igbvl 0.00 0.10

0.20

ac igbvl

2005q1

2010q1 2015q1 TRIMESTRE

2020q1

2025q1

0. Autocorrelations of igbvl -0.10 0.00 0.10 -0.20 0

40

10

20 Lag

Bartlett's formula for MA(q) 95% confidence bands

dfuller igbvl pperron igbvl

iables son estacionarias ya que P-values es menor al 0.05

30

40

8. VAR

var pbi igbvl, lags(1/4) Vector autoregression 101 -10.50765 -10.32945 -10.06748

Sample: 2001q1 - 2026q1 Log likelihood = 555.1985 FPE = 8.23e-08 Det(Sigma_ml) = 5.76e-08 Equation pbi

Parms 9

Number of obs AIC HQIC SBIC RMSE .025467

R-sq

chi2

P>chi2

0.8190

457.1199

0.0000

= = = =

101 -10.63759 -10.44892 -10.17153

101 -10.50765 -10.32945 -10.06748

Sample: 2001q1 - 2026q1 Log likelihood = 555.1985 FPE = 8.23e-08 Det(Sigma_ml) = 5.76e-08 Equation

Parms

pbi igbvl

9 9

Coef.

Interval]

Number of obs AIC HQIC SBIC RMSE .025467 .01056

Std. Err.

R-sq

chi2

P>chi2

0.8190 0.9450

457.1199 1735.552

0.0000 0.0000

z

P>|z|

= = = =

101 -10.63759 -10.44892 -10.17153

[95% Conf. Interval]

pbi -.1732928

-.5432845 -.5627827 -.7149391

.191218 -.0262033 -.0106393

pbi L1. L2. L3. L4.

-.296802 -.2203563 -.3191746 .5796968

.0785471 .0774789 .0772731 .0810801

-3.78 -2.84 -4.13 7.15

0.000 0.004 0.000 0.000

-.4507516 -.3722121 -.4706271 .4207829

-.1428525 -.0685005 -.1677221 -2,87 CE t-1 .7386108

igbvl L1. L2. L3. L4.

-.2433839 .0532529 .0118629 .0171675

.1954612 .1678013 .004656 .0044511

-1.25 0.32 2.55 3.86

0.213 0.751 0.011 0.000

-.6264807 -.2756316 .0027373 .0084434

.139713 .3821374 .0209884 .0258916

_cons

.0192612

.0044547

4.32

0.000

.01053

.0279923

pbi L1. L2. L3. L4.

-.1130888 -.0977907 .0089294 -.0692267

.0325691 .0321262 .0320409 .0336194

-3.47 -3.04 0.28 -2.06

0.001 0.002 0.780 0.039

-.1769231 -.1607569 -.0538696 -.1351195

-.0492545 -.0348245 .0717283 -.0033339

igbvl L1. L2. L3. L4.

.3726397 .3540514 -.0065529 .000171

.0810469 .0695779 .0019306 .0018456

4.60 5.09 -3.39 0.09

0.000 0.000 0.001 0.926

.2137907 .2176812 -.0103368 -.0034464

.5314887 .4904216 -.0027691 .0037884

_cons

.0066147

.0018471

3.58

0.000

.0029944

.010235

.00618 igbvl

-.2856437

.288832 .1647413 .1338332

-.1998767 .0130439 .0038431 .0014353

ODOS LOS COEFICIENTES

MODELO VAR INTERPRETACION Como afecta el pbi al igbvl Se nots una leve influencia

)

El crecimiento de la economia no tiene mucha insidencia en el indice general de bolsa de valores , y el efecto que tiene termina en el periodo 5. Como afecta el IGBVL al PBI Si sube la bolsa de valores porque cae el PBI , al llevar dinero a la bolsa se deja de consumir lo que ocasiona que el pbi se frene.

8

varirf graph irf, impulse(igbvl) response(pbi) varbasic, igbvl, pbi .5

0

-.5

0

2

4

6

step

8

95% CI

impulse-response function (irf)

Graphs by irfname, impulse variable, and response variable

8

PARA 10 PERIODOS =(10)

EXAMEN

LO PRINCIPAL DE UN VAR ES LA CAUSALIDAD SI NO HAY CAUSALIDAD EL MODELO ESTA MAL

que el pbi si

ABLES DEPENDIENTE)

NFLUYEN EN FORMA CONJUNTA EN LA VARIABLE DEPENDIENTE E CERO ENTONCES ESTOS REZAGOS 0 COEFICIENTES SI INFLUYEN

i la PROB es mayor al 0.05 se acepta la hipotesis nula (no hay influencia , no son significativos) i la PROB es menor al 0.05 se acepta la hipotesis alternativa ( hay influencia , son significativos)

ESTE SE INTERPRETA , SE ACPETA LA HIPOTESIS ALTERNATIVA YA QUE

ESO QUIERE DECIR QUE SON SIGNIFICATIVOS DE FORMA CONJUNTA

ROB>CHI2 ES MAYOR QUE 0.05 NO HAY AUTOCORRELACION

N ESTE CASO NO HAY AUTOCORRELACION

un modelo var no tiene que tener heteroscedasticidad ni causalidad

PROB>CHI2 ES MAYOR QUE 0.05 NO HAY HETEROSCEDASTICIDAD HAY HEROSCEDASTICIDAD , ES POR ESO QUE TIENE CAUSALIDAD EL MODELO ESTA MAL

101 -10.63759 -10.44892 -10.17153

101 -10.63759 -10.44892 -10.17153

Interval]

-.1428525 -.0685005 -.1677221 .7386108

.139713 .3821374 .0209884 .0258916 .0279923

-.0492545 -.0348245 .0717283 -.0033339

.5314887 .4904216 -.0027691 .0037884 .010235

ne termina en

dinero a la bolsa

rf)

8