Actividad Individual 5

Llamar librerías

library(WDI)
library(wbstats)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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library(forecast)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(ggplot2)

Pronóstico #1: PIB en México

gdp_data <- wb_data(country = "MX", indicator = "NY.GDP.PCAP.CD", start_date = 1973, end_date = 2022)
# Consultar country en
# Condultar indicator en


PIB <- gdp_data$NY.GDP.PCAP.CD

#Generar serie de tiempo
PIB_st <- ts(data = PIB, start = c(1973), frequency = 1)
PIB_st

## Time Series:
## Start = 1973 
## End = 2021 
## Frequency = 1 
##  [1]  1000.938  1264.358  1499.356  1472.659  1314.020  1598.466  2039.646
##  [8]  3029.887  3812.581  2612.759  2166.461  2507.312  2607.380  1765.185
## [15]  1902.438  2302.027  2759.446  3196.919  3756.890  4272.786  5778.939
## [22]  5976.524  4002.174  4487.292  5370.218  5555.737  6230.696  7232.879
## [29]  7613.177  7650.899  7120.385  7525.465  8321.853  9125.423  9719.920
## [36] 10119.836  8104.908  9399.971 10341.521 10376.058 10865.680 11076.092
## [43]  9753.380  8875.062  9434.386  9857.029 10145.170  8655.001 10045.681
## attr(,"label")
## [1] GDP per capita (current US$)

#Generar pronóstico
modelo_pib <- auto.arima(PIB_st, D=1)
modelo_pib

## Series: PIB_st 
## ARIMA(0,1,0) with drift 
## 
## Coefficients:
##          drift
##       188.4321
## s.e.  112.5901
## 
## sigma^2 = 621419:  log likelihood = -387.76
## AIC=779.52   AICc=779.78   BIC=783.26

pronostico_pib <- forecast(modelo_pib, level=c(95), h=5)
pronostico_pib

##      Point Forecast    Lo 95    Hi 95
## 2022       10234.11 8689.071 11779.15
## 2023       10422.54 8237.526 12607.56
## 2024       10610.98 7934.886 13287.07
## 2025       10799.41 7709.325 13889.49
## 2026       10987.84 7533.023 14442.66

plot(pronostico_pib, main = "Pronóstico a 5 años del PIB en México")

### Información varios países

more_gdp_data <- wb_data(country = c("NG", "HT", "KE"), indicator = "NY.GDP.PCAP.CD", start_date = 1950, end_date = 2022)

ggplot(data = more_gdp_data) + 
  geom_point(mapping = aes(x = date, y = NY.GDP.PCAP.CD, color = country, shape = country)) +
  labs(x="Año", y = "PIB", title = "Comparación del PIB de Nigeria, Haití y Kenia")