ACTIVIDAD 1

# Actividad Sesión 1. Modelo Econometrico

## 1. Formular la pregunta de interés
#Queremos analizar si a mayor ingreso, las personas tienden a gastar más y en qué magnitud. Teniendo como pregunta *¿Cómo afecta el ingreso de una persona a su consumo?*

## 2. Construir un modelo económico
#C=β0+β1YC 
#Donde:
#* C es el consumo de una persona.
#* Y es el ingreso de la persona.
#* β0 es el consumo autónomo (lo que la persona gasta aunque su ingreso sea cero).
#* β1 es la propensión marginal a consumir (qué parte del ingreso adicional se gasta).

## 3. Transformar el modelo económico a econométrico
#Ci=β0+β1Yi+εiC_i
#Donde:
#CiC_i y YiY_i son los valores de consumo e ingreso para la persona ii.
#εi es un término de error que representa otros factores que influyen en el consumo.

# Actividad Sesión 2,3 y 4. Analisis de datos de Panel

##Instalar paquetes y llamar librerías 

#install.packages("WDI")
library(WDI)
#install.packages("wbstats")
library(wbstats)
#install.packages("tidyverse")
library(ggplot2)
#install.packages("plm")
library(plm)

## Obtener la información de 1 país 
PIB_MEX <- wb_data(country= "MX", indicator = "NY.GDP.PCAP.CD",
                   start_date = 1900, end_date = 2025)
summary(PIB_MEX)

##     iso2c              iso3c             country               date     
##  Length:64          Length:64          Length:64          Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##  NY.GDP.PCAP.CD        unit            obs_status          footnote        
##  Min.   :  355.1   Length:64          Length:64          Length:64         
##  1st Qu.: 1427.8   Class :character   Class :character   Class :character  
##  Median : 4006.5   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 5097.1                                                           
##  3rd Qu.: 8905.4                                                           
##  Max.   :13790.0                                                           
##   last_updated       
##  Min.   :2025-01-28  
##  1st Qu.:2025-01-28  
##  Median :2025-01-28  
##  Mean   :2025-01-28  
##  3rd Qu.:2025-01-28  
##  Max.   :2025-01-28

ggplot(PIB_MEX, aes(x= date, y=NY.GDP.PCAP.CD)) + 
  geom_point() +
  geom_line() +
  labs(title="PIB per Capita en México (Current USD$)", x= "Año", y= "Valor")

## Obtener la información de varios país 
PIB_PANEL <- wb_data(country= c("MX", "US", "CA"), indicator = "NY.GDP.PCAP.CD",
                     start_date = 1900, end_date = 2025)
summary(PIB_PANEL)

##     iso2c              iso3c             country               date     
##  Length:192         Length:192         Length:192         Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##  NY.GDP.PCAP.CD        unit            obs_status          footnote        
##  Min.   :  355.1   Length:192         Length:192         Length:192        
##  1st Qu.: 4059.2   Class :character   Class :character   Class :character  
##  Median :10544.4   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :19152.2                                                           
##  3rd Qu.:29010.1                                                           
##  Max.   :82769.4                                                           
##   last_updated       
##  Min.   :2025-01-28  
##  1st Qu.:2025-01-28  
##  Median :2025-01-28  
##  Mean   :2025-01-28  
##  3rd Qu.:2025-01-28  
##  Max.   :2025-01-28

ggplot(PIB_PANEL, aes(x= date, y=NY.GDP.PCAP.CD, color=iso3c)) + 
  geom_point() +
  geom_line() +
  labs(title="PIB per Capita en Norteamérica (Current USD$)", x= "Año", y= "Valor")

## Obtener la información de varios indicadores en varios paises
PIB_VARIOS <- wb_data(country= c("MX", "US", "CA"), indicator = c("NY.GDP.PCAP.CD", "SP.DYN.LE00.IN"),
                      start_date = 1900, end_date = 2025)
summary(PIB_VARIOS)

##     iso2c              iso3c             country               date     
##  Length:192         Length:192         Length:192         Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##                                                                         
##  NY.GDP.PCAP.CD    SP.DYN.LE00.IN 
##  Min.   :  355.1   Min.   :55.02  
##  1st Qu.: 4059.2   1st Qu.:71.11  
##  Median :10544.4   Median :74.36  
##  Mean   :19152.2   Mean   :73.41  
##  3rd Qu.:29010.1   3rd Qu.:77.49  
##  Max.   :82769.4   Max.   :82.22  
##                    NA's   :3

##Heterogeneidad Variación entre individuos

#install.packages("gplots")
library(gplots)

plotmeans(NY.GDP.PCAP.CD ~ country, main = "Heterogenidad entre paises", xlab = "Pais" , ylab = "PIB per Capita", data= PIB_VARIOS)

## Warning in arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped

## Warning in arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped

# *Interpretación*: 
# 1) Alta heterogenidad: Si los puntos estan muy separados entre paises 
# 2) Baja heterogenidad: Si los puntos estan cerca uno de otos
# En este caso EUA y Canada tiene un PIB per capita mayor que México, mostrando alta heterogenidad entre paises 

## Modelos de efectos fijos y aleatorios

###Paso 1, Convertir la base de datos a formato de panel
datos_panel  <-pdata.frame(PIB_PANEL, index = c("country", "date"))


### Modelo de efectos fijos 
modelo_efectos_fijos <- plm(NY.GDP.PCAP.CD ~ date, data=datos_panel, model= "within")


summary(modelo_efectos_fijos)

## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ date, data = datos_panel, model = "within")
## 
## Balanced Panel: n = 3, T = 64, N = 192
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -22869.42  -3713.59   -740.79   4417.57  22788.54 
## 
## Coefficients:
##           Estimate Std. Error t-value  Pr(>|t|)    
## date1961    19.689   7891.777  0.0025 0.9980133    
## date1962    93.003   7891.777  0.0118 0.9906159    
## date1963   182.117   7891.777  0.0231 0.9816255    
## date1964   329.256   7891.777  0.0417 0.9667868    
## date1965   493.812   7891.777  0.0626 0.9502057    
## date1966   705.548   7891.777  0.0894 0.9289037    
## date1967   836.074   7891.777  0.1059 0.9157965    
## date1968  1051.287   7891.777  0.1332 0.8942375    
## date1969  1278.661   7891.777  0.1620 0.8715461    
## date1970  1483.079   7891.777  0.1879 0.8512361    
## date1971  1757.600   7891.777  0.2227 0.8241196    
## date1972  2139.145   7891.777  0.2711 0.7867884    
## date1973  2652.616   7891.777  0.3361 0.7373364    
## date1974  3306.205   7891.777  0.4189 0.6759711    
## date1975  3736.686   7891.777  0.4735 0.6366822    
## date1976  4425.604   7891.777  0.5608 0.5759388    
## date1977  4698.806   7891.777  0.5954 0.5526405    
## date1978  5234.634   7891.777  0.6633 0.5083487    
## date1979  6060.354   7891.777  0.7679 0.4439640    
## date1980  7072.576   7891.777  0.8962 0.3718573    
## date1981  8188.133   7891.777  1.0376 0.3014655    
## date1982  7987.390   7891.777  1.0121 0.3134224    
## date1983  8523.654   7891.777  1.0801 0.2821751    
## date1984  9312.706   7891.777  1.1801 0.2402027    
## date1985  9796.257   7891.777  1.2413 0.2167918    
## date1986  9909.818   7891.777  1.2557 0.2115431    
## date1987 10895.002   7891.777  1.3806 0.1698612    
## date1988 12362.836   7891.777  1.5665 0.1197288    
## date1989 13585.668   7891.777  1.7215 0.0876150 .  
## date1990 14316.347   7891.777  1.8141 0.0720442 .  
## date1991 14759.335   7891.777  1.8702 0.0637741 .  
## date1992 14990.000   7891.777  1.8994 0.0597918 .  
## date1993 15667.517   7891.777  1.9853 0.0492832 *  
## date1994 16091.651   7891.777  2.0390 0.0435376 *  
## date1995 15978.167   7891.777  2.0247 0.0450159 *  
## date1996 16773.055   7891.777  2.1254 0.0355067 *  
## date1997 17769.387   7891.777  2.2516 0.0260772 *  
## date1998 18030.354   7891.777  2.2847 0.0240026 *  
## date1999 19236.904   7891.777  2.4376 0.0161811 *  
## date2000 20835.037   7891.777  2.6401 0.0093360 ** 
## date2001 21096.198   7891.777  2.6732 0.0085083 ** 
## date2002 21538.969   7891.777  2.7293 0.0072554 ** 
## date2003 23202.118   7891.777  2.9400 0.0039054 ** 
## date2004 25366.654   7891.777  3.2143 0.0016609 ** 
## date2005 27852.977   7891.777  3.5294 0.0005823 ***
## date2006 30232.924   7891.777  3.8309 0.0002003 ***
## date2007 32408.252   7891.777  4.1066 7.172e-05 ***
## date2008 33394.731   7891.777  4.2316 4.431e-05 ***
## date2009 30291.171   7891.777  3.8383 0.0001950 ***
## date2010 33440.081   7891.777  4.2373 4.333e-05 ***
## date2011 35778.148   7891.777  4.5336 1.331e-05 ***
## date2012 36526.334   7891.777  4.6284 9.027e-06 ***
## date2013 37214.927   7891.777  4.7157 6.286e-06 ***
## date2014 37345.549   7891.777  4.7322 5.866e-06 ***
## date2015 35011.917   7891.777  4.4365 1.971e-05 ***
## date2016 34666.237   7891.777  4.3927 2.348e-05 ***
## date2017 36493.760   7891.777  4.6243 9.182e-06 ***
## date2018 38068.376   7891.777  4.8238 3.990e-06 ***
## date2019 38902.406   7891.777  4.9295 2.543e-06 ***
## date2020 37056.865   7891.777  4.6956 6.833e-06 ***
## date2021 42836.438   7891.777  5.4280 2.815e-07 ***
## date2022 46436.696   7891.777  5.8842 3.387e-08 ***
## date2023 48123.578   7891.777  6.0979 1.218e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    5.061e+10
## Residual Sum of Squares: 1.1771e+10
## R-Squared:      0.76742
## Adj. R-Squared: 0.64743
## F-statistic: 6.59909 on 63 and 126 DF, p-value: < 2.22e-16

### Modelo de efectos aleatorios
modelo_efectos_aleatorios <- plm(NY.GDP.PCAP.CD ~ date, data=datos_panel, model= "random")
summary(modelo_efectos_aleatorios)

## Oneway (individual) effect Random Effect Model 
##    (Swamy-Arora's transformation)
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ date, data = datos_panel, model = "random")
## 
## Balanced Panel: n = 3, T = 64, N = 192
## 
## Effects:
##                     var   std.dev share
## idiosyncratic  93420218      9665 0.375
## individual    155441504     12468 0.625
## theta: 0.9035
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -24225.08  -3320.91   -892.17   5059.72  23751.53 
## 
## Coefficients:
##              Estimate Std. Error z-value  Pr(>|z|)    
## (Intercept)  1873.296   9107.904  0.2057 0.8370424    
## date1961       19.689   7891.777  0.0025 0.9980093    
## date1962       93.003   7891.777  0.0118 0.9905973    
## date1963      182.117   7891.777  0.0231 0.9815890    
## date1964      329.256   7891.777  0.0417 0.9667208    
## date1965      493.812   7891.777  0.0626 0.9501065    
## date1966      705.548   7891.777  0.0894 0.9287617    
## date1967      836.074   7891.777  0.1059 0.9156280    
## date1968     1051.287   7891.777  0.1332 0.8940250    
## date1969     1278.661   7891.777  0.1620 0.8712866    
## date1970     1483.079   7891.777  0.1879 0.8509338    
## date1971     1757.600   7891.777  0.2227 0.8237590    
## date1972     2139.145   7891.777  0.2711 0.7863449    
## date1973     2652.616   7891.777  0.3361 0.7367774    
## date1974     3306.205   7891.777  0.4189 0.6752578    
## date1975     3736.686   7891.777  0.4735 0.6358628    
## date1976     4425.604   7891.777  0.5608 0.5749430    
## date1977     4698.806   7891.777  0.5954 0.5515726    
## date1978     5234.634   7891.777  0.6633 0.5071370    
## date1979     6060.354   7891.777  0.7679 0.4425272    
## date1980     7072.576   7891.777  0.8962 0.3701483    
## date1981     8188.133   7891.777  1.0376 0.2994785    
## date1982     7987.390   7891.777  1.0121 0.3114828    
## date1983     8523.654   7891.777  1.0801 0.2801120    
## date1984     9312.706   7891.777  1.1801 0.2379796    
## date1985     9796.257   7891.777  1.2413 0.2144858    
## date1986     9909.818   7891.777  1.2557 0.2092195    
## date1987    10895.002   7891.777  1.3806 0.1674170    
## date1988    12362.836   7891.777  1.5665 0.1172207    
## date1989    13585.668   7891.777  1.7215 0.0851607 .  
## date1990    14316.347   7891.777  1.8141 0.0696648 .  
## date1991    14759.335   7891.777  1.8702 0.0614537 .  
## date1992    14990.000   7891.777  1.8994 0.0575059 .  
## date1993    15667.517   7891.777  1.9853 0.0471115 *  
## date1994    16091.651   7891.777  2.0390 0.0414460 *  
## date1995    15978.167   7891.777  2.0247 0.0429023 *  
## date1996    16773.055   7891.777  2.1254 0.0335546 *  
## date1997    17769.387   7891.777  2.2516 0.0243455 *  
## date1998    18030.354   7891.777  2.2847 0.0223303 *  
## date1999    19236.904   7891.777  2.4376 0.0147856 *  
## date2000    20835.037   7891.777  2.6401 0.0082883 ** 
## date2001    21096.198   7891.777  2.6732 0.0075134 ** 
## date2002    21538.969   7891.777  2.7293 0.0063470 ** 
## date2003    23202.118   7891.777  2.9400 0.0032817 ** 
## date2004    25366.654   7891.777  3.2143 0.0013076 ** 
## date2005    27852.977   7891.777  3.5294 0.0004166 ***
## date2006    30232.924   7891.777  3.8309 0.0001277 ***
## date2007    32408.252   7891.777  4.1066 4.016e-05 ***
## date2008    33394.731   7891.777  4.2316 2.320e-05 ***
## date2009    30291.171   7891.777  3.8383 0.0001239 ***
## date2010    33440.081   7891.777  4.2373 2.262e-05 ***
## date2011    35778.148   7891.777  4.5336 5.799e-06 ***
## date2012    36526.334   7891.777  4.6284 3.685e-06 ***
## date2013    37214.927   7891.777  4.7157 2.409e-06 ***
## date2014    37345.549   7891.777  4.7322 2.221e-06 ***
## date2015    35011.917   7891.777  4.4365 9.143e-06 ***
## date2016    34666.237   7891.777  4.3927 1.119e-05 ***
## date2017    36493.760   7891.777  4.6243 3.759e-06 ***
## date2018    38068.376   7891.777  4.8238 1.408e-06 ***
## date2019    38902.406   7891.777  4.9295 8.245e-07 ***
## date2020    37056.865   7891.777  4.6956 2.658e-06 ***
## date2021    42836.438   7891.777  5.4280 5.699e-08 ***
## date2022    46436.696   7891.777  5.8842 4.000e-09 ***
## date2023    48123.578   7891.777  6.0979 1.074e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    5.0797e+10
## Residual Sum of Squares: 1.1958e+10
## R-Squared:      0.76459
## Adj. R-Squared: 0.64873
## Chisq: 415.742 on 63 DF, p-value: < 2.22e-16

### Prueba de Hausman
phtest(modelo_efectos_fijos, modelo_efectos_aleatorios)

## 
##  Hausman Test
## 
## data:  NY.GDP.PCAP.CD ~ date
## chisq = 3.8736e-13, df = 63, p-value = 1
## alternative hypothesis: one model is inconsistent

# Como el p-value es mayor a 0.05, usamos el modelo de efectos aletorios.

Aplicación de Shiny (Ejemplo en Clase y Ejercicio sesion 3 por mesas)

Link shiny app:

## Codigo ejercicio 3 por mesas 

#Instalar paquetes y llamar librerias 
#install.packages("WDI")
library(WDI)
#install.packages("wbstats")
library(wbstats)
#install.packages("tidyverse")
library(ggplot2)
#install.packages("gplots")
library(gplots)

# Obtener la información de 1 país 
AGRICULTURA_MEX <- wb_data(country= "MX", indicator = "NV.AGR.TOTL.ZS",
                           start_date = 1960, end_date = 2025)
summary(AGRICULTURA_MEX)

##     iso2c              iso3c             country               date     
##  Length:64          Length:64          Length:64          Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##                                                                         
##  NV.AGR.TOTL.ZS       unit            obs_status          footnote        
##  Min.   : 2.943   Length:64          Length:64          Length:64         
##  1st Qu.: 3.216   Class :character   Class :character   Class :character  
##  Median : 4.580   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 6.059                                                           
##  3rd Qu.: 7.995                                                           
##  Max.   :13.149                                                           
##  NA's   :5                                                                
##   last_updated       
##  Min.   :2025-01-28  
##  1st Qu.:2025-01-28  
##  Median :2025-01-28  
##  Mean   :2025-01-28  
##  3rd Qu.:2025-01-28  
##  Max.   :2025-01-28  
## 

ggplot(AGRICULTURA_MEX, aes(x= date, y= NV.AGR.TOTL.ZS)) + 
  geom_point() +
  geom_line() +
  labs(title="Agricultura como % del PIB en México", x= "Año", y= "Porcentaje del PIB")

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_line()`).

# Obtener la información de varios países 
AGRICULTURA_PANEL <- wb_data(country= c("MX", "US", "CA"), indicator = "NV.AGR.TOTL.ZS",
                             start_date = 1960, end_date = 2025)
summary(AGRICULTURA_PANEL)

##     iso2c              iso3c             country               date     
##  Length:192         Length:192         Length:192         Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##                                                                         
##  NV.AGR.TOTL.ZS        unit            obs_status          footnote        
##  Min.   : 0.8326   Length:192         Length:192         Length:192        
##  1st Qu.: 1.5774   Class :character   Class :character   Class :character  
##  Median : 3.0280   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 3.9749                                                           
##  3rd Qu.: 5.5139                                                           
##  Max.   :13.1492                                                           
##  NA's   :84                                                                
##   last_updated       
##  Min.   :2025-01-28  
##  1st Qu.:2025-01-28  
##  Median :2025-01-28  
##  Mean   :2025-01-28  
##  3rd Qu.:2025-01-28  
##  Max.   :2025-01-28  
## 

ggplot(AGRICULTURA_PANEL, aes(x= date, y= NV.AGR.TOTL.ZS, color= iso3c)) + 
  geom_point() +
  geom_line() +
  labs(title="Agricultura como % del PIB en Norteamérica", x= "Año", y= "Porcentaje del PIB")

## Warning: Removed 84 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 84 rows containing missing values or values outside the scale range
## (`geom_line()`).

# Obtener la información de varios indicadores en varios países
AGRICULTURA_VARIOS <- wb_data(country= c("MX", "US", "CA"), 
                              indicator = c("NV.AGR.TOTL.ZS", "AG.LND.AGRI.ZS"),
                              start_date = 1960, end_date = 2025)
summary(AGRICULTURA_VARIOS)

##     iso2c              iso3c             country               date     
##  Length:192         Length:192         Length:192         Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##                                                                         
##  AG.LND.AGRI.ZS   NV.AGR.TOTL.ZS   
##  Min.   : 6.371   Min.   : 0.8326  
##  1st Qu.: 6.970   1st Qu.: 1.5774  
##  Median :46.532   Median : 3.0280  
##  Mean   :35.038   Mean   : 3.9749  
##  3rd Qu.:50.286   3rd Qu.: 5.5139  
##  Max.   :54.888   Max.   :13.1492  
##  NA's   :6        NA's   :84

# CLASE 3 
# Heterogeneidad
# Variación entre países

plotmeans(NV.AGR.TOTL.ZS ~ iso3c, main = "Heterogeneidad en la Agricultura (% PIB)", 
          xlab = "País" , ylab = "Agricultura como % del PIB", data= AGRICULTURA_VARIOS)

## Warning in arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped

## Warning in arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped

## Warning in arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped

## Warning in arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
## zero-length arrow is of indeterminate angle and so skipped

# Interpretación: 
# Alta heterogeneidad: Si los puntos están muy separados entre países. 
# Baja heterogeneidad: Si los puntos están cerca unos de otros.