7.1. Ejemplo 1

uu = "https://raw.githubusercontent.com/vmoprojs/DataLectures/master/CAEMP.DAT"
datos = read.csv(url(uu),sep=",",header=T)
d2s = ts(datos,st=1962,fr=4)
plot(d2s)

## Interpretar Basado en al prueba de Dickey Fuller la serie no es estacionaria ademas la función de autocorrelación muestra un descenso suabe en los 12 primeros rezagos.

7.1.1. Estacinalidad

acf(d2s)

library(tseries)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
adf.test(d2s)
## 
##  Augmented Dickey-Fuller Test
## 
## data:  d2s
## Dickey-Fuller = -2.6391, Lag order = 5, p-value = 0.3106
## alternative hypothesis: stationary
d2sd=diff(d2s)
acf(d2sd)

adf.test(d2sd)
## Warning in adf.test(d2sd): p-value smaller than printed p-value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  d2sd
## Dickey-Fuller = -4.0972, Lag order = 5, p-value = 0.01
## alternative hypothesis: stationary

El P-valor de la prueba de Dickey Fuller indica que la serie es estacionario, por que este es menor que alfa=5%

7.1.3. Modelo ARIMA

acf(d2sd)

pacf(d2sd)

m1=arima(d2s,order = c(0,1,2))
m1$aic
## [1] 488.5535
m2=arima(d2s,order = c(1,1,0))
m2$aic
## [1] 485.4119
m3=arima(d2s,order = c(1,1,2))
m3$aic
## [1] 489.1799

De los tres modelos presentes se escoje aquel que represente el valor mas bajo del indicador de AIC en este caso el modelo 2 (aic=485.41)

7.1.4. Pronóstico

d2sp=predict(m2,3)
d2sp
## $pred
##          Qtr1     Qtr2     Qtr3
## 1996 92.17202 92.24426 92.27748
## 
## $se
##          Qtr1     Qtr2     Qtr3
## 1996 1.437907 2.544385 3.499883

7.1.5. Esportación de datos

str(d2s)
##  Time-Series [1:136, 1] from 1962 to 1996: 83.1 82.8 84.6 85.4 86.2 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr "caemp"
inicio=1962.
final=1996.5
fecha=seq(inicio,final,by=0.25)
fecha
##   [1] 1962.00 1962.25 1962.50 1962.75 1963.00 1963.25 1963.50 1963.75 1964.00
##  [10] 1964.25 1964.50 1964.75 1965.00 1965.25 1965.50 1965.75 1966.00 1966.25
##  [19] 1966.50 1966.75 1967.00 1967.25 1967.50 1967.75 1968.00 1968.25 1968.50
##  [28] 1968.75 1969.00 1969.25 1969.50 1969.75 1970.00 1970.25 1970.50 1970.75
##  [37] 1971.00 1971.25 1971.50 1971.75 1972.00 1972.25 1972.50 1972.75 1973.00
##  [46] 1973.25 1973.50 1973.75 1974.00 1974.25 1974.50 1974.75 1975.00 1975.25
##  [55] 1975.50 1975.75 1976.00 1976.25 1976.50 1976.75 1977.00 1977.25 1977.50
##  [64] 1977.75 1978.00 1978.25 1978.50 1978.75 1979.00 1979.25 1979.50 1979.75
##  [73] 1980.00 1980.25 1980.50 1980.75 1981.00 1981.25 1981.50 1981.75 1982.00
##  [82] 1982.25 1982.50 1982.75 1983.00 1983.25 1983.50 1983.75 1984.00 1984.25
##  [91] 1984.50 1984.75 1985.00 1985.25 1985.50 1985.75 1986.00 1986.25 1986.50
## [100] 1986.75 1987.00 1987.25 1987.50 1987.75 1988.00 1988.25 1988.50 1988.75
## [109] 1989.00 1989.25 1989.50 1989.75 1990.00 1990.25 1990.50 1990.75 1991.00
## [118] 1991.25 1991.50 1991.75 1992.00 1992.25 1992.50 1992.75 1993.00 1993.25
## [127] 1993.50 1993.75 1994.00 1994.25 1994.50 1994.75 1995.00 1995.25 1995.50
## [136] 1995.75 1996.00 1996.25 1996.50
length(fecha)
## [1] 139
desempleo=c(d2s,d2sp,1)
length(desempleo)
## [1] 139
data=c(rep("real",136),rep("pronostico",3))
length(data)
## [1] 139
#datosd=data.frame(fecha,desempleo,data)