Contabilidad Trimestral Cantabria: sector industrial file · Web viewContabilidad Trimestral...
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Contabilidad Trimestral Cantabria: sector industrial Francisco Parra 13 de Noviembre de 2018 Lectura y tratamiento de datos En indust_m1995.xls están los datos de los indicadores de frecuencia mensual, en indust_T1995.xls los indicadores de frecuencia trimestral, y en CRAC.xls los datos de la serie anual de la contabilidad regional de Cantabria que se quiere trimestralizar. # Datos mensuales excel1 <- read.csv ( "indust_m1995.csv" , sep= ";" , header = TRUE ) excel3 <- read.csv ( "indust_T1995.csv" , sep= ";" , header = TRUE ) excel4<- read.csv ( "CRAC.csv" , sep= ";" , header = TRUE ) Creamos objetos ts de las series de Exportaciones (EXPORT), Ocupados (OCUP) en la industria (cifras de la seguridad social), tendencia de la Cartera de Pedidos (TPEDIDOS), Indice de Precios Industriales (IPRI), Indice de producción Industrial (IPI), y Ocupados (EPA) en la Industria en la Encuesta de Población Activa. EXPORT= ts ( na.omit (excel1 $ EXPORT), frequency = 12 , start= c ( 1995 , 1 )) OCUP= ts ( na.omit (excel1 $ OCUP), frequency = 12 , start= c ( 1995 , 1 )) TPEDIDO= ts ( na.omit (excel1 $ TPEDIDO), frequency = 12 , start= c ( 1995 , 1 )) IPRI= ts ( na.omit (excel1 $ IPRI), frequency = 12 , start= c ( 1995 , 1 )) IPI= ts ( na.omit (excel1 $ IPI), frequency = 12 , start= c ( 1995 , 1 )) EPA= ts ( na.omit (excel3 $ EPA), frequency = 4 , start= c ( 1995 , 1 )) Convertimos las series de indicadores de frecuencia mensual a trimestral: EXPORT= aggregate.ts (EXPORT, nfrequency = 4 ,mean) OCUP= aggregate.ts (OCUP, nfrequency = 4 ,mean) TPEDIDO= aggregate.ts (TPEDIDO, nfrequency = 4 ,mean)
Transcript of Contabilidad Trimestral Cantabria: sector industrial file · Web viewContabilidad Trimestral...
Contabilidad Trimestral Cantabria: sector industrialFrancisco Parra
13 de Noviembre de 2018
Lectura y tratamiento de datosEn indust_m1995.xls están los datos de los indicadores de frecuencia mensual, en indust_T1995.xls los indicadores de frecuencia trimestral, y en CRAC.xls los datos de la serie anual de la contabilidad regional de Cantabria que se quiere trimestralizar.
# Datos mensualesexcel1 <-read.csv("indust_m1995.csv", sep=";",header = TRUE)excel3 <-read.csv("indust_T1995.csv", sep=";",header = TRUE)excel4<-read.csv("CRAC.csv", sep=";",header = TRUE)
Creamos objetos ts de las series de Exportaciones (EXPORT), Ocupados (OCUP) en la industria (cifras de la seguridad social), tendencia de la Cartera de Pedidos (TPEDIDOS), Indice de Precios Industriales (IPRI), Indice de producción Industrial (IPI), y Ocupados (EPA) en la Industria en la Encuesta de Población Activa.
EXPORT=ts(na.omit(excel1$EXPORT),frequency = 12,start=c(1995,1))OCUP=ts(na.omit(excel1$OCUP),frequency = 12,start=c(1995,1))TPEDIDO=ts(na.omit(excel1$TPEDIDO),frequency = 12,start=c(1995,1))IPRI=ts(na.omit(excel1$IPRI),frequency = 12,start=c(1995,1))IPI=ts(na.omit(excel1$IPI),frequency = 12,start=c(1995,1))EPA=ts(na.omit(excel3$EPA),frequency = 4,start=c(1995,1))
Convertimos las series de indicadores de frecuencia mensual a trimestral:
EXPORT=aggregate.ts(EXPORT,nfrequency = 4,mean)OCUP=aggregate.ts(OCUP,nfrequency = 4,mean)TPEDIDO=aggregate.ts(TPEDIDO,nfrequency = 4,mean)IPRI=aggregate.ts(IPRI,nfrequency = 4,mean)IPI=aggregate.ts(IPI,nfrequency = 4,mean)
Deflactamos la serie de exportaciones por el índice de precios industriales.
EXPORT=EXPORT*100/IPRI
DesestacionalizamosObtenemos las series de tendencia, estacionalidad con descomponer, con modelo aditivo.
library(descomponer)#EXPORTEXPORT.s
=ts(descomponer(EXPORT,4,1)$datos$TDST,frequency=4,start=c(1995,1))EXPORT.sa=ts(descomponer(EXPORT,4,1)$datos$ST,frequency=4,start=c(1995,1))EXPORT.t=ts(descomponer(EXPORT,4,1)$datos$TD,frequency=4,start=c(1995,1))plot(EXPORT)lines(EXPORT.s,col="red")lines(EXPORT.t,col="blue")
#OCUPOCUP.s=ts(descomponer(OCUP,4,1)$datos$TDST,frequency=4,start=c(1995,1))OCUP.sa=ts(descomponer(OCUP,4,1)$datos$ST,frequency=4,start=c(1995,1))OCUP.t=ts(descomponer(OCUP,4,1)$datos$TD,frequency=4,start=c(1995,1))plot(OCUP)lines(OCUP.s,col="red")lines(OCUP.t,col="blue")
#TPEDIDOTPEDIDO.s=ts(descomponer(TPEDIDO,4,1)$datos$TDST,frequency=4,start=c(1995,1))TPEDIDO.sa=ts(descomponer(TPEDIDO,4,1)$datos$ST,frequency=4,start=c(1995,1))TPEDIDO.t=ts(descomponer(TPEDIDO,4,1)$datos$TD,frequency=4,start=c(1995,1))plot(TPEDIDO)lines(TPEDIDO.s,col="red")lines(TPEDIDO.t,col="blue")
#IPIIPI.s=ts(descomponer(IPI,4,1)$datos$TDST,frequency=4,start=c(1995,1))IPI.sa=ts(descomponer(IPI,4,1)$datos$ST,frequency=4,start=c(1995,1))IPI.t=ts(descomponer(IPI,4,1)$datos$TD,frequency=4,start=c(1995,1))plot(IPI)lines(IPI.s,col="red")lines(IPI.t,col="blue")
#EPAEPA.s=ts(descomponer(EPA,4,1)$datos$TDST,frequency=4,start=c(1995,1))EPA.sa=ts(descomponer(EPA,4,1)$datos$ST,frequency=4,start=c(1995,1))EPA.t=ts(descomponer(EPA,4,1)$datos$TD,frequency=4,start=c(1995,1))plot(EPA)lines(EPA.s,col="red")lines(EPA.t,col="blue")
Pronosticos de la serie desestacionalizadaCon tslm de forecast pronosticamos: tendencias y estacionalidades. Prolongamos la serie original (o) con el pronostico de tendencia y estacionalidad, la serie de tendencia y estacionalidad (f) con el pronostico de tendencia y estacionalidad, y la serie de tendencia (t) con el pronóstico de tendencia.
library(forecast)#EXPORTfit1 <- auto.arima(EXPORT.sa)fit2 <- auto.arima(EXPORT.t)plot(forecast(fit1, h=20))
plot(forecast(fit2, h=20))
fit3 = forecast(fit1, h=20)fit4 = forecast(fit2, h=20)EXPORT.f=ts(c(EXPORT.s,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))EXPORT.o=ts(c(EXPORT,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))EXPORT.t=ts(c(EXPORT.t,fit4$mean),frequency = 4,start=c(1995,1))plot(EXPORT.o)lines(EXPORT.f,col="blue")lines(EXPORT.t,col="red")
#OCUPfit1 <- auto.arima(OCUP.sa)fit2 <- auto.arima(OCUP.t)plot(forecast(fit1, h=20))
plot(forecast(fit2, h=20))
fit3 = forecast(fit1, h=20)fit4 = forecast(fit2, h=20)OCUP.f=ts(c(OCUP.s,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))OCUP.o=ts(c(OCUP,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))OCUP.t=ts(c(OCUP.t,fit4$mean),frequency = 4,start=c(1995,1))plot(OCUP.o)lines(OCUP.f,col="blue")lines(OCUP.t,col="red")
# TPEDIDOfit1 <- auto.arima(TPEDIDO.sa)fit2 <- auto.arima(TPEDIDO.t)plot(forecast(fit1, h=20))
fit3 = forecast(fit1, h=20)fit4 = forecast(fit2, h=20)TPEDIDO.f=ts(c(TPEDIDO.s,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))TPEDIDO.o=ts(c(TPEDIDO,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))TPEDIDO.t=ts(c(TPEDIDO.t,fit4$mean),frequency = 4,start=c(1995,1))plot(TPEDIDO)lines(TPEDIDO.f,col="blue")lines(TPEDIDO.t,col="red")
#IPIfit1 <- auto.arima(IPI.sa)fit2 <- auto.arima(IPI.t)plot(forecast(fit1, h=20))
fit3 = forecast(fit1, h=20)fit4 = forecast(fit2, h=20)IPI.f=ts(c(IPI.s,c(fit3$mean)+c(fit4$mean)),frequency = 4,start=c(1995,1))IPI.o=ts(c(IPI,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))IPI.t=ts(c(IPI.t,fit4$mean),frequency = 4,start=c(1995,1))plot(IPI)lines(IPI.f,col="blue")lines(IPI.t,col="red")
# EPAfit1 <- auto.arima(EPA.sa)fit2 <- auto.arima(EPA.t)plot(forecast(fit1, h=20))
fit3 = forecast(fit1, h=20)fit4 = forecast(fit2, h=20)EPA.f=ts(c(EPA.s,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))EPA.o=ts(c(EPA,fit3$mean+fit4$mean),frequency = 4,start=c(1995,1))EPA.t=ts(c(EPA.t,fit4$mean),frequency = 4,start=c(1995,1))plot(EPA)lines(EPA.f,col="blue")lines(EPA.t,col="red")
Elaboración de indices sintéticosObtenemos los indicadores en frecuencias anuales desde las frecuencias trimestrales.
EXPORT.A=aggregate.ts(EXPORT.f,nfrequency = 1,mean)OCUP.A=aggregate.ts(OCUP.f,nfrequency = 1,mean)TPEDIDO.A=aggregate.ts(TPEDIDO.f,nfrequency = 1,mean)IPI.A=aggregate.ts(IPI.f,nfrequency = 1,mean)EPA.A=aggregate.ts(EPA.f,nfrequency = 1,mean)
Creamos varios data frame con las series anuales para el periodo 2000-2019. En cada data frame ha
fin=2019# SELECCION BASEINDUS_CAN.A=data.frame(EXPORT=window(EXPORT.A,start=c(2000),end=c(fin)),OCUP=window(OCUP.A,start=c(2000),end=c(fin)),IPI=window(IPI.A,start=c(2000),end=c(fin)))INDUS_CAN.A.ts=ts(INDUS_CAN.A,start=c(2000))INDUS_CAN.A.tasa=diff(INDUS_CAN.A.ts)/INDUS_CAN.A.tscolnames(INDUS_CAN.A.tasa)=c("EXPORT","OCUP","IPI")# SELECCION 1INDUS_CAN.A.1=data.frame(EXPORT=window(EXPORT.A,start=c(2000),end=c(fin)),OCUP=window(OCUP.A,start=c(2000),end=c(fin)),TPEDIDO=window(TPEDIDO.A,start=c
(2000),end=c(fin)),IPI=window(IPI.A,start=c(2000),end=c(fin)))INDUS_CAN.A.1.ts=ts(INDUS_CAN.A.1,start=c(2000))INDUS_CAN.A.1.tasa=diff(INDUS_CAN.A.1.ts)/INDUS_CAN.A.1.tscolnames(INDUS_CAN.A.1.tasa)=c("EXPORT","OCUP","TPEDIDO","IPI")# SELECCION 2INDUS_CAN.A.2=data.frame(EXPORT=window(EXPORT.A,start=c(2000),end=c(fin)),IPI=window(IPI.A,start=c(2000),end=c(fin)),EPA=window(EPA.A,start=c(2000),end=c(fin)))INDUS_CAN.A.2.ts=ts(INDUS_CAN.A.2,start=c(2000))INDUS_CAN.A.2.tasa=diff(INDUS_CAN.A.2.ts)/INDUS_CAN.A.2.tscolnames(INDUS_CAN.A.2.tasa)=c("EXPORT","IPI","EPA")# SELECCION 3INDUS_CAN.A.3=data.frame(OCUP=window(OCUP.A,start=c(2000),end=c(fin)),IPI=window(IPI.A,start=c(2000),end=c(fin)))INDUS_CAN.A.3.ts=ts(INDUS_CAN.A.3,start=c(2000))INDUS_CAN.A.3.tasa=diff(INDUS_CAN.A.3.ts)/INDUS_CAN.A.3.tscolnames(INDUS_CAN.A.3.tasa)=c("OCUP","IPI")
Leemos la serie anual del Indice de Volumen del VAB de la industria de la CRE y obtenemos la tasas de crecimento anual:
VAB=ts(na.omit(excel4$VAB2_Co),start=c(2000))VAB.tasa=diff(VAB)/VAB
Indices con Análisis factorialElaboramos un indicador de sintisis con la selección básica de indicadores, utilizando la libreria “rela”, se correlaciona el indicador obtenido con la tasa anual del crecimiento del VAB en la industria, y se proyecta la serie anual del VAB hasta 2019.
library(rela)res <- paf(as.matrix(INDUS_CAN.A.tasa))summary(res) # Obtenemos el KMO con MSA, para determinar el número de factores
## $KMO## [1] 0.48095## ## $MSA## MSA## EXPORT 0.44810## OCUP 0.48373## IPI 0.48815## ## $Bartlett## [1] 10.405## ## $Communalities## Initial Communalities Final Extraction
## EXPORT 0.14632 0.060978## OCUP 0.40067 0.192226## IPI 0.46941 1.990338## ## $Factor.Loadings## [,1]## EXPORT 0.24694## OCUP 0.43844## IPI 1.41079## ## $RMS## [1] 0.0056725
# calculamos el chi-square del Bartlett's sphericity test, la comunalidad y# pesos de los factores.barplot(res$Eigenvalues[,1]) # Representamos los eigenvalues.
resv <- varimax(res$Factor.Loadings) # Rotación Varimax print(resv)
## [,1]## EXPORT 0.24694## OCUP 0.43844## IPI 1.41079
#barplot(sort(colSums(loadings(resv)^2),decreasing=TRUE)) # Gráfico utilizando los pesos rotados.
scores <- scale(as.matrix(INDUS_CAN.A.tasa)) %*% as.matrix(resv) # Pesos factoriales, para obtener los indices tambien se puede # Intervencionesd13=c(rep(0,12),1,rep(0,(17-13)))d14=c(rep(0,13),1,rep(0,(17-14)))# Modelo 1: Estimacion MCOindice1=ts(scores[,1],start=c(2001))indice1.1=window(indice1,end=c(2017))mod1=lm(VAB.tasa~0+indice1.1)summary(mod1)
## ## Call:## lm(formula = VAB.tasa ~ 0 + indice1.1)## ## Residuals:## Min 1Q Median 3Q Max ## -0.0552 0.0088 0.0289 0.0395 0.0815 ## ## Coefficients:## Estimate Std. Error t value Pr(>|t|) ## indice1.1 0.02700 0.00579 4.66 0.00026 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ## Residual standard error: 0.0416 on 16 degrees of freedom## Multiple R-squared: 0.576, Adjusted R-squared: 0.549 ## F-statistic: 21.7 on 1 and 16 DF, p-value: 0.00026
plot(mod1)
# Graficoplot(VAB.tasa)lines(ts(mod1$fitted,start=c(2001)),col="red")
# Pronósticosindice1.1=window(indice1,start=c(2018),end=c(2019))nuevos=data.frame(indice1.1)VAB.tasa.fa=ts(predict(mod1,nuevos,interval="prediction"),start=c(2018),end=c(2019))VAB.tasa.fa
## Time Series:## Start = 2018 ## End = 2019 ## Frequency = 1 ## fit lwr upr## 2018 -0.030867 -0.12008 0.058351## 2019 -0.067016 -0.16024 0.026211
VAB.2018=window(VAB,start=c(2017),end=c(2017))*c((1+window(VAB.tasa.fa[,"fit"],start=c(2018),end=c(2018))))VAB.2019=VAB.2018*c((1+window(VAB.tasa.fa[,"fit"],start=c(2019),end=c(2019))))VAB1=ts(c(VAB,VAB.2018,VAB.2019),start=c(2000))plot(VAB1)
Indices con el método de Granger y NewboldElaboramos un indicador de síntesis para la industria utilizando el método deGranger y Newbold y la selección básica de indicadores,se correlaciona el indicador obtenido con la tasa anual del crecimiento del VAB en la industria, y se proyecta la serie anual del VAB hasta 2019 :
# Indicesd1=1/sd(lm(VAB.tasa~window(INDUS_CAN.A.tasa[,"EXPORT"],end=c(2017)))$residuals)sd2=1/sd(lm(VAB.tasa~window(INDUS_CAN.A.tasa[,"OCUP"],end=c(2017)))$residuals)sd3=1/sd(lm(VAB.tasa~window(INDUS_CAN.A.tasa[,"IPI"],end=c(2017)))$residuals)sdT=sd1+sd2+sd3indice2=(sd1/sdT)*INDUS_CAN.A.tasa[,"EXPORT"]+(sd2/sdT)*INDUS_CAN.A.tasa[,"OCUP"]+(sd3/sdT)*INDUS_CAN.A.tasa[,"IPI"]indice2=ts(indice2,start=c(2001))indice2.1=window(indice2,end=c(2017))# Estimacion MCOmod2=lm(VAB.tasa~0+indice2.1)#mod2=lm(VAB.tasa~0+indice2.1)summary(mod2)
## ## Call:## lm(formula = VAB.tasa ~ 0 + indice2.1)
## ## Residuals:## Min 1Q Median 3Q Max ## -0.0437 0.0092 0.0452 0.0539 0.0785 ## ## Coefficients:## Estimate Std. Error t value Pr(>|t|) ## indice2.1 1.026 0.312 3.29 0.0046 **## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ## Residual standard error: 0.0493 on 16 degrees of freedom## Multiple R-squared: 0.404, Adjusted R-squared: 0.366 ## F-statistic: 10.8 on 1 and 16 DF, p-value: 0.00461
plot(mod2)
# Graficoplot(VAB.tasa)lines(ts(mod2$fitted,start=c(2001)),col="red")
# Pronósticosindice2.1=window(indice2,start=c(2018),end=c(2019))nuevos=data.frame(indice2.1)VAB.tasa.f=ts(predict(mod2,nuevos,interval="prediction"),start=c(2018),end=c(2019))VAB.tasa.f
## Time Series:## Start = 2018 ## End = 2019 ## Frequency = 1 ## fit lwr upr## 2018 -0.036495 -0.14360 0.070611## 2019 -0.036095 -0.14314 0.070954
VAB.2018=window(VAB,start=c(2017),end=c(2017))*c((1+window(VAB.tasa.f[,"fit"],start=c(2018),end=c(2018))))VAB.2019=VAB.2018*c((1+window(VAB.tasa.f[,"fit"],start=c(2019),end=c(2019))))VAB2=ts(c(VAB,VAB.2018,VAB.2019),start=c(2000))plot(VAB2)
TrimestralizarTrimestralizamos la serie original con el el método de Granger y Newbold, utilizando la libreria tempdisagg, y el método de Chow-Lee.
library(tempdisagg)
## ## Attaching package: 'tempdisagg'
## The following object is masked from 'package:descomponer':## ## td
indice.o=(sd1/sdT)*scale(window(EXPORT.o,start=c(2000,1),end=c(2018,1)))+(sd2/sdT)*scale(window(OCUP.o,start=c(2000,1),end=c(2018,1)))+(sd3/sdT)*scale(window(IPI.o,start=c(2000,1),end=c(2018,1)))indice.o=ts(indice.o,frequency=4,start=c(2000,1))mod.o=td(VAB1~indice.o,method = "chow-lin-maxlog")
## Warning in td(VAB1 ~ indice.o, method = "chow-lin-maxlog"): High frequency## series shorter than low frequency. Low frequency values until 2017 are## used.
summary(mod.o)
## ## Call:## td(formula = VAB1 ~ indice.o, method = "chow-lin-maxlog")## ## Residuals:## Min 1Q Median 3Q Max ## -620869 -283813 211697 293350 564010 ## ## Coefficients:## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 571100 133106 4.29 0.00056 ***## indice.o 54629 13523 4.04 0.00095 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ## 'chow-lin-maxlog' disaggregation with 'sum' conversion## 18 low-freq. obs. converted to 73 high-freq. obs.## Adjusted R-squared: 0.474 AR1-Parameter: 0.994
plot(mod.o)
resultado.o=predict(mod.o)resultado.o
## Qtr1 Qtr2 Qtr3 Qtr4## 2000 402401 431736 374071 431340
## 2001 428531 469977 420409 450129## 2002 472462 476538 445370 487996## 2003 484545 497483 461965 507379## 2004 498886 530916 502865 538611## 2005 536836 587814 543134 575463## 2006 594945 615292 558224 620167## 2007 636735 655711 634375 668724## 2008 666067 706978 682217 649379## 2009 613285 618799 615913 631341## 2010 628039 653872 632904 655550## 2011 663299 676980 636796 636336## 2012 650990 677295 612885 590079## 2013 587791 589862 549974 565730## 2014 609743 616864 606075 619083## 2015 614624 634361 624760 608014## 2016 607021 659252 596201 614829## 2017 667555 695972 670766 688266## 2018 702379
plot(resultado.o)
Trimestralizamos la serie de tendencia, y la serie de tendencia y estacionalidad, utilizando la libreria tempdisagg, y el método de Chow-Lee, representamos los resultados finales:
indice.f=(sd1/sdT)*scale(window(EXPORT.f,start=c(2000,1),end=c(2018,1)))+(sd2/sdT)*scale(window(OCUP.f,start=c(2000,1),end=c(2018,1)))+(sd3/
sdT)*scale(window(IPI.f,start=c(2000,1),end=c(2018,1)))indice.f=ts(indice.f,frequency=4,start=c(2000,1))mod.f=td(VAB1~indice.f,method = "chow-lin-maxlog")
## Warning in td(VAB1 ~ indice.f, method = "chow-lin-maxlog"): High frequency## series shorter than low frequency. Low frequency values until 2017 are## used.
summary(mod.f)
## ## Call:## td(formula = VAB1 ~ indice.f, method = "chow-lin-maxlog")## ## Residuals:## Min 1Q Median 3Q Max ## -627386 -284234 193718 282567 563244 ## ## Coefficients:## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 572728 129078 4.44 0.00041 ***## indice.f 47328 14180 3.34 0.00418 ** ## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ## 'chow-lin-maxlog' disaggregation with 'sum' conversion## 18 low-freq. obs. converted to 73 high-freq. obs.## Adjusted R-squared: 0.374 AR1-Parameter: 0.992
plot(mod.f)
indice.T=(1/sd1)/(1/sdT)*scale(window(EXPORT.t,start=c(2000,1),end=c(2018,1)))+(1/sd2)/(1/sdT)*scale(window(OCUP.t,start=c(2000,1),end=c(2018,1)))+(1/sd3)/(1/sdT)*scale(window(IPI.t,start=c(2000,1),end=c(2018,1)))indice.T=ts(indice.T,frequency=4,start=c(2000,1))mod.T=td(VAB1~indice.T,method = "chow-lin-maxlog")
## Warning in td(VAB1 ~ indice.T, method = "chow-lin-maxlog"): High frequency## series shorter than low frequency. Low frequency values until 2017 are## used.
summary(mod.T)
## ## Call:## td(formula = VAB1 ~ indice.T, method = "chow-lin-maxlog")## ## Residuals:## Min 1Q Median 3Q Max ## -610818 -238857 200111 250276 525486 ## ## Coefficients:## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 572562 113503 5.04 0.00012 ***## indice.T 2920 1149 2.54 0.02183 *
## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ## 'chow-lin-maxlog' disaggregation with 'sum' conversion## 18 low-freq. obs. converted to 73 high-freq. obs.## Adjusted R-squared: 0.243 AR1-Parameter: 0.989
plot(mod.T)
resultado.f=predict(mod.f)resultado.f
## Qtr1 Qtr2 Qtr3 Qtr4## 2000 394205 424333 398569 422440## 2001 430120 456045 428605 454276## 2002 463633 488724 455792 474217## 2003 478645 502402 472810 497515## 2004 504752 532269 504008 530250## 2005 543689 574499 548730 576328## 2006 585440 612609 582403 608175## 2007 621429 658078 640137 675901## 2008 689286 707837 655823 651696## 2009 630779 636465 594453 617642## 2010 628601 660117 629885 651762## 2011 653951 673582 634051 651827## 2012 652141 664313 609231 605565## 2013 581557 588461 547510 575828
## 2014 596395 631735 600722 622911## 2015 624377 643352 599977 614054## 2016 608652 631480 600591 636580## 2017 662558 701021 669518 689463## 2018 683355
resultado.T=predict(mod.T)resultado.T
## Qtr1 Qtr2 Qtr3 Qtr4## 2000 396560 406159 414758 422071## 2001 429338 437186 446380 456142## 2002 464589 470251 472958 474569## 2003 478263 483297 490718 499094## 2004 505238 513315 521682 531044## 2005 543832 555320 566736 577359## 2006 585229 593036 600650 609712## 2007 622543 639051 658034 675917## 2008 688345 687759 674797 653742## 2009 630894 616503 613037 618904## 2010 629452 640514 648341 652058## 2011 653269 653163 653338 653641## 2012 652594 644170 628202 606285## 2013 581080 567741 566985 577551## 2014 596937 611400 619965 623462## 2015 623946 622363 619758 615693## 2016 608601 610596 620407 637698## 2017 662921 679754 688931 690954## 2018 686142
plot(resultado.o)lines(resultado.T,col="red")lines(resultado.f,col="blue")
