Ejercicio 1. Modelo Econométrico

Ejercicio 2. good to great

# Aplicación de shiny

Aplicación de shiny

Actividad 3 en parejas

Ejercicio Panel En Equipos

instalar paquetes y llamar librerias

#install.packages("WDI")
#install.packages("wbstats")
library(WDI)
library(wbstats)
#install.packages("tidyverse")
library(tidyverse)
#install.packages("plm") #paquete para realizar modelos lineales para datos de panel
library(plm)
#install.packages("gplots")
library(gplots)
library(readxl)
library(lmtest)

Paso 1: Generar el conjunto de datos de panel

gdp <- wb_data(country=c("MX","US","CA"), indicator=c("NY.GDP.PCAP.CD",
                        "SM.POP.NETM"), start_date=1950, end_date=2025)

#generar conjunto de datos de panel
panel_1 <- select(gdp, country, date, NY.GDP.PCAP.CD, SM.POP.NETM)
panel_1 <- subset(panel_1, date == 1960 | date == 1970 | date== 1980 | date== 1990 | date==2000 | date==2010 | date==2020)

panel_1 <- pdata.frame(panel_1, index = c("country","date"))

Paso 2: Prueba de Heterogenidad

plotmeans(NY.GDP.PCAP.CD ~ country, main= "Prueba de heterogenidad entre paises para el PIB", data=panel_1)

plotmeans(SM.POP.NETM ~ country, main= "Prueba de heterogenidad entre paises para la migracion neta", data=panel_1)

#si la linea sale casi horizontal, hay poca o nula heterogenidad, por lo que no hay diferencia sitematica que ajustar
#si la linea sale quebrada, sube y baja, hay mucha heterogenidad, por lo que hay que ajustar

Paso 3: Prueba de Efectos fijos y Aleatoris

#Modelo 1. Regresión agrupada (pooled) ##para poca o nula heterogenidad
#asume que no hay heterogenidad no observada
pooled <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="pooling")
summary(pooled)
## Pooling Model
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model = "pooling")
## 
## Balanced Panel: n = 3, T = 7, N = 21
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -21506.0 -10924.8  -3728.9   5274.5  45389.3 
## 
## Coefficients:
##               Estimate Std. Error t-value Pr(>|t|)   
## (Intercept) 1.2873e+04 4.2134e+03  3.0553 0.006511 **
## SM.POP.NETM 1.8616e-02 7.2324e-03  2.5740 0.018588 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7259500000
## Residual Sum of Squares: 5382600000
## R-Squared:      0.25855
## Adj. R-Squared: 0.21952
## F-statistic: 6.62533 on 1 and 19 DF, p-value: 0.018588
#Modelo 2. Efectos fijos (within)
#Cuando las diferencias no observadas son constante en el tiempo
within <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="within")
summary(within)
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model = "within")
## 
## Balanced Panel: n = 3, T = 7, N = 21
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -20886.56  -9903.27   -403.03   3407.39  44059.72 
## 
## Coefficients:
##             Estimate Std. Error t-value Pr(>|t|)
## SM.POP.NETM 0.013921   0.014345  0.9705   0.3454
## 
## Total Sum of Squares:    5256100000
## Residual Sum of Squares: 4980200000
## R-Squared:      0.052492
## Adj. R-Squared: -0.11471
## F-statistic: 0.94181 on 1 and 17 DF, p-value: 0.34542
#Prueba F
pFtest(within, pooled)
## 
##  F test for individual effects
## 
## data:  NY.GDP.PCAP.CD ~ SM.POP.NETM
## F = 0.68685, df1 = 2, df2 = 17, p-value = 0.5166
## alternative hypothesis: significant effects
# Si p-value es menor a 0.05 se prefiere el modelo de efectos fijos

#Modelo 3. Efectos Aleatorios 
#Cuando las diferencias no observadas son aleatorias

#Método walhus
walhus <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="random", random.method = "walhus")
summary(walhus)
## Oneway (individual) effect Random Effect Model 
##    (Wallace-Hussain's transformation)
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model = "random", 
##     random.method = "walhus")
## 
## Balanced Panel: n = 3, T = 7, N = 21
## 
## Effects:
##                     var   std.dev share
## idiosyncratic 278418900     16686     1
## individual            0         0     0
## theta: 0
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -21506.0 -10924.8  -3728.9   5274.5  45389.3 
## 
## Coefficients:
##               Estimate Std. Error z-value Pr(>|z|)   
## (Intercept) 1.2873e+04 4.2134e+03  3.0553 0.002248 **
## SM.POP.NETM 1.8616e-02 7.2324e-03  2.5740 0.010054 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7259500000
## Residual Sum of Squares: 5382600000
## R-Squared:      0.25855
## Adj. R-Squared: 0.21952
## Chisq: 6.62533 on 1 DF, p-value: 0.010054
#Método amemiya
amemiya <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="random", random.method = "amemiya")
summary(amemiya)
## Oneway (individual) effect Random Effect Model 
##    (Amemiya's transformation)
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model = "random", 
##     random.method = "amemiya")
## 
## Balanced Panel: n = 3, T = 7, N = 21
## 
## Effects:
##                     var   std.dev share
## idiosyncratic 276675480     16634     1
## individual            0         0     0
## theta: 0
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -21506.0 -10924.8  -3728.9   5274.5  45389.3 
## 
## Coefficients:
##               Estimate Std. Error z-value Pr(>|z|)   
## (Intercept) 1.2873e+04 4.2134e+03  3.0553 0.002248 **
## SM.POP.NETM 1.8616e-02 7.2324e-03  2.5740 0.010054 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7259500000
## Residual Sum of Squares: 5382600000
## R-Squared:      0.25855
## Adj. R-Squared: 0.21952
## Chisq: 6.62533 on 1 DF, p-value: 0.010054
#Método nerlove
nerlove <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="random", random.method = "nerlove")
summary(nerlove)
## Oneway (individual) effect Random Effect Model 
##    (Nerlove's transformation)
## 
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model = "random", 
##     random.method = "nerlove")
## 
## Balanced Panel: n = 3, T = 7, N = 21
## 
## Effects:
##                     var   std.dev share
## idiosyncratic 237150411     15400 0.864
## individual     37271843      6105 0.136
## theta: 0.31
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -20850.0  -9773.4  -2826.2   3450.7  45608.0 
## 
## Coefficients:
##               Estimate Std. Error z-value Pr(>|z|)  
## (Intercept) 1.3174e+04 5.8290e+03  2.2601  0.02382 *
## SM.POP.NETM 1.7563e-02 9.0595e-03  1.9386  0.05255 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    6.21e+09
## Residual Sum of Squares: 5184500000
## R-Squared:      0.16513
## Adj. R-Squared: 0.12119
## Chisq: 3.75814 on 1 DF, p-value: 0.052551
#Comparar la r2 ajustada de los 3 métodos y elegir el que tenga el mayor r2 ese lo ponemos en la prueba de hausman

phtest(walhus, within)
## 
##  Hausman Test
## 
## data:  NY.GDP.PCAP.CD ~ SM.POP.NETM
## chisq = 0.14364, df = 1, p-value = 0.7047
## alternative hypothesis: one model is inconsistent
#Si el p-value en <0.05, usamos Efectos Fijos
patentes <- read_excel("C:\\Users\\admin\\Downloads\\PATENT 3.xls")
summary(patentes)
##      cusip            merger           employ            return       
##  Min.   :   800   Min.   :0.0000   Min.   :  0.085   Min.   :-73.022  
##  1st Qu.:368514   1st Qu.:0.0000   1st Qu.:  1.227   1st Qu.:  5.128  
##  Median :501116   Median :0.0000   Median :  3.842   Median :  7.585  
##  Mean   :514536   Mean   :0.0177   Mean   : 18.826   Mean   :  8.003  
##  3rd Qu.:754688   3rd Qu.:0.0000   3rd Qu.: 15.442   3rd Qu.: 10.501  
##  Max.   :878555   Max.   :1.0000   Max.   :506.531   Max.   : 48.675  
##                                    NA's   :21        NA's   :8        
##     patents         patentsg           stckpr              rnd           
##  Min.   :  0.0   Min.   :   0.00   Min.   :  0.1875   Min.   :   0.0000  
##  1st Qu.:  1.0   1st Qu.:   1.00   1st Qu.:  7.6250   1st Qu.:   0.6847  
##  Median :  3.0   Median :   4.00   Median : 16.5000   Median :   2.1456  
##  Mean   : 22.9   Mean   :  27.14   Mean   : 22.6270   Mean   :  29.3398  
##  3rd Qu.: 15.0   3rd Qu.:  19.00   3rd Qu.: 29.2500   3rd Qu.:  11.9168  
##  Max.   :906.0   Max.   :1063.00   Max.   :402.0000   Max.   :1719.3535  
##                                    NA's   :2                             
##     rndeflt             rndstck              sales                sic      
##  Min.   :   0.0000   Min.   :   0.1253   Min.   :    1.222   Min.   :2000  
##  1st Qu.:   0.4788   1st Qu.:   5.1520   1st Qu.:   52.995   1st Qu.:2890  
##  Median :   1.4764   Median :  13.3532   Median :  174.065   Median :3531  
##  Mean   :  19.7238   Mean   : 163.8234   Mean   : 1219.601   Mean   :3333  
##  3rd Qu.:   8.7527   3rd Qu.:  74.5625   3rd Qu.:  728.964   3rd Qu.:3661  
##  Max.   :1000.7876   Max.   :9755.3516   Max.   :44224.000   Max.   :9997  
##                      NA's   :157         NA's   :3                         
##       year     
##  Min.   :2012  
##  1st Qu.:2014  
##  Median :2016  
##  Mean   :2016  
##  3rd Qu.:2019  
##  Max.   :2021  
## 
str(patentes)
## tibble [2,260 × 13] (S3: tbl_df/tbl/data.frame)
##  $ cusip   : num [1:2260] 800 800 800 800 800 800 800 800 800 800 ...
##  $ merger  : num [1:2260] 0 0 0 0 0 0 0 0 0 0 ...
##  $ employ  : num [1:2260] 9.85 12.32 12.2 11.84 12.99 ...
##  $ return  : num [1:2260] 5.82 5.69 4.42 5.28 4.91 ...
##  $ patents : num [1:2260] 22 34 31 32 40 60 57 77 38 5 ...
##  $ patentsg: num [1:2260] 24 32 30 34 28 33 53 47 64 70 ...
##  $ stckpr  : num [1:2260] 47.6 57.9 33 38.5 35.1 ...
##  $ rnd     : num [1:2260] 2.56 3.1 3.27 3.24 3.78 ...
##  $ rndeflt : num [1:2260] 2.56 2.91 2.8 2.52 2.78 ...
##  $ rndstck : num [1:2260] 16.2 17.4 19.6 21.9 23.1 ...
##  $ sales   : num [1:2260] 344 436 535 567 631 ...
##  $ sic     : num [1:2260] 3740 3740 3740 3740 3740 3740 3740 3740 3740 3740 ...
##  $ year    : num [1:2260] 2012 2013 2014 2015 2016 ...
sum(is.na(patentes))#NA´s en la base de datos
## [1] 191
sapply(patentes, function(x) sum(is.na(x))) #NA´s por variable
##    cusip   merger   employ   return  patents patentsg   stckpr      rnd 
##        0        0       21        8        0        0        2        0 
##  rndeflt  rndstck    sales      sic     year 
##        0      157        3        0        0
patentes$employ[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$return[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$stckpr[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$rndstck[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$sales[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
summary(patentes)
##      cusip            merger           employ            return       
##  Min.   :   800   Min.   :0.0000   Min.   :  0.085   Min.   :-73.022  
##  1st Qu.:368514   1st Qu.:0.0000   1st Qu.:  1.242   1st Qu.:  5.128  
##  Median :501116   Median :0.0000   Median :  3.893   Median :  7.585  
##  Mean   :514536   Mean   :0.0177   Mean   : 18.826   Mean   :  8.003  
##  3rd Qu.:754688   3rd Qu.:0.0000   3rd Qu.: 16.034   3rd Qu.: 10.501  
##  Max.   :878555   Max.   :1.0000   Max.   :506.531   Max.   : 48.675  
##                                                      NA's   :8        
##     patents         patentsg           stckpr              rnd           
##  Min.   :  0.0   Min.   :   0.00   Min.   :  0.1875   Min.   :   0.0000  
##  1st Qu.:  1.0   1st Qu.:   1.00   1st Qu.:  7.6250   1st Qu.:   0.6847  
##  Median :  3.0   Median :   4.00   Median : 16.5000   Median :   2.1456  
##  Mean   : 22.9   Mean   :  27.14   Mean   : 22.6270   Mean   :  29.3398  
##  3rd Qu.: 15.0   3rd Qu.:  19.00   3rd Qu.: 29.2500   3rd Qu.:  11.9168  
##  Max.   :906.0   Max.   :1063.00   Max.   :402.0000   Max.   :1719.3535  
##                                    NA's   :2                             
##     rndeflt             rndstck              sales                sic      
##  Min.   :   0.0000   Min.   :   0.1253   Min.   :    1.222   Min.   :2000  
##  1st Qu.:   0.4788   1st Qu.:   5.1520   1st Qu.:   52.995   1st Qu.:2890  
##  Median :   1.4764   Median :  13.3532   Median :  174.065   Median :3531  
##  Mean   :  19.7238   Mean   : 163.8234   Mean   : 1219.601   Mean   :3333  
##  3rd Qu.:   8.7527   3rd Qu.:  74.5625   3rd Qu.:  728.964   3rd Qu.:3661  
##  Max.   :1000.7876   Max.   :9755.3516   Max.   :44224.000   Max.   :9997  
##                      NA's   :157         NA's   :3                         
##       year     
##  Min.   :2012  
##  1st Qu.:2014  
##  Median :2016  
##  Mean   :2016  
##  3rd Qu.:2019  
##  Max.   :2021  
## 
sum(is.na(patentes))
## [1] 170
boxplot(patentes$cusip, horizontal=TRUE)

boxplot(patentes$merger, horizontal=TRUE)

boxplot(patentes$employ, horizontal=TRUE)

boxplot(patentes$patents, horizontal=TRUE)

boxplot(patentes$return, horizontal=TRUE)

boxplot(patentes$patentsg, horizontal=TRUE)

boxplot(patentes$stckpr, horizontal=TRUE)

boxplot(patentes$rnd, horizontal=TRUE)

boxplot(patentes$rndeflt, horizontal=TRUE)

boxplot(patentes$rndstck, horizontal=TRUE)

boxplot(patentes$sales, horizontal=TRUE)

boxplot(patentes$sic, horizontal=TRUE)

boxplot(patentes$year, horizontal=TRUE)

patentes$year <- patentes$year -40
summary(patentes)
##      cusip            merger           employ            return       
##  Min.   :   800   Min.   :0.0000   Min.   :  0.085   Min.   :-73.022  
##  1st Qu.:368514   1st Qu.:0.0000   1st Qu.:  1.242   1st Qu.:  5.128  
##  Median :501116   Median :0.0000   Median :  3.893   Median :  7.585  
##  Mean   :514536   Mean   :0.0177   Mean   : 18.826   Mean   :  8.003  
##  3rd Qu.:754688   3rd Qu.:0.0000   3rd Qu.: 16.034   3rd Qu.: 10.501  
##  Max.   :878555   Max.   :1.0000   Max.   :506.531   Max.   : 48.675  
##                                                      NA's   :8        
##     patents         patentsg           stckpr              rnd           
##  Min.   :  0.0   Min.   :   0.00   Min.   :  0.1875   Min.   :   0.0000  
##  1st Qu.:  1.0   1st Qu.:   1.00   1st Qu.:  7.6250   1st Qu.:   0.6847  
##  Median :  3.0   Median :   4.00   Median : 16.5000   Median :   2.1456  
##  Mean   : 22.9   Mean   :  27.14   Mean   : 22.6270   Mean   :  29.3398  
##  3rd Qu.: 15.0   3rd Qu.:  19.00   3rd Qu.: 29.2500   3rd Qu.:  11.9168  
##  Max.   :906.0   Max.   :1063.00   Max.   :402.0000   Max.   :1719.3535  
##                                    NA's   :2                             
##     rndeflt             rndstck              sales                sic      
##  Min.   :   0.0000   Min.   :   0.1253   Min.   :    1.222   Min.   :2000  
##  1st Qu.:   0.4788   1st Qu.:   5.1520   1st Qu.:   52.995   1st Qu.:2890  
##  Median :   1.4764   Median :  13.3532   Median :  174.065   Median :3531  
##  Mean   :  19.7238   Mean   : 163.8234   Mean   : 1219.601   Mean   :3333  
##  3rd Qu.:   8.7527   3rd Qu.:  74.5625   3rd Qu.:  728.964   3rd Qu.:3661  
##  Max.   :1000.7876   Max.   :9755.3516   Max.   :44224.000   Max.   :9997  
##                      NA's   :157         NA's   :3                         
##       year     
##  Min.   :1972  
##  1st Qu.:1974  
##  Median :1976  
##  Mean   :1976  
##  3rd Qu.:1979  
##  Max.   :1981  
##

Paso 1: Generar el conjunto de datos de panel

#generar conjunto de datos de panel

panel_patentes <- pdata.frame(patentes, index = c("cusip","year"))

Paso 2: Prueba de Heterogenidad

plotmeans(patents ~ cusip, main= "Prueba de heterogenidad entre empresas para sus patentes", data=panel_patentes)

#Como la linea sale quebrada, sube y baja, hay mucha heterogenidad, por lo que hay que ajustar

Paso 3: Prueba de Efectos fijos y Aleatoris

#Modelo 1. Regresión agrupada (pooled) ##para poca o nula heterogenidad
#asume que no hay heterogenidad no observada
pooled_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="pooling")
summary(pooled_patentes)
## Pooling Model
## 
## Call:
## plm(formula = patents ~ merger + employ + return + stckpr + rnd + 
##     sales + sic, data = panel_patentes, model = "pooling")
## 
## Unbalanced Panel: n = 226, T = 6-10, N = 2252
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -320.73823  -10.11533    0.96527    7.41046  433.49935 
## 
## Coefficients:
##                Estimate  Std. Error t-value Pr(>|t|)    
## (Intercept) -4.5823e-01  5.2868e+00 -0.0867  0.93094    
## merger      -1.1674e+01  7.2521e+00 -1.6097  0.10760    
## employ       1.3694e+00  4.2028e-02 32.5828  < 2e-16 ***
## return      -5.5049e-03  1.8177e-01 -0.0303  0.97584    
## stckpr       6.5251e-01  4.3209e-02 15.1012  < 2e-16 ***
## rnd         -1.3928e-01  1.6132e-02 -8.6336  < 2e-16 ***
## sales       -3.1901e-03  4.7031e-04 -6.7830  1.5e-11 ***
## sic         -2.6784e-03  1.4851e-03 -1.8035  0.07144 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    10994000
## Residual Sum of Squares: 4594700
## R-Squared:      0.58209
## Adj. R-Squared: 0.58079
## F-statistic: 446.512 on 7 and 2244 DF, p-value: < 2.22e-16
#Modelo 2. Efectos fijos (within)
#Cuando las diferencias no observadas son constante en el tiempo
within_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="within")
summary(within_patentes)
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = patents ~ merger + employ + return + stckpr + rnd + 
##     sales + sic, data = panel_patentes, model = "within")
## 
## Unbalanced Panel: n = 226, T = 6-10, N = 2252
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -497.16507   -1.62011   -0.19851    1.64094  184.59512 
## 
## Coefficients:
##           Estimate  Std. Error  t-value  Pr(>|t|)    
## merger  3.35351504  4.17165385   0.8039   0.42156    
## employ  0.11699838  0.07080752   1.6523   0.09862 .  
## return -0.07161127  0.11004982  -0.6507   0.51530    
## stckpr -0.01097329  0.03251309  -0.3375   0.73577    
## rnd    -0.19865376  0.01446336 -13.7350 < 2.2e-16 ***
## sales  -0.00310168  0.00041645  -7.4479 1.397e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1091400
## Residual Sum of Squares: 819090
## R-Squared:      0.24952
## Adj. R-Squared: 0.1637
## F-statistic: 111.938 on 6 and 2020 DF, p-value: < 2.22e-16
#Prueba F
pFtest(within_patentes, pooled_patentes)
## 
##  F test for individual effects
## 
## data:  patents ~ merger + employ + return + stckpr + rnd + sales + sic
## F = 41.568, df1 = 224, df2 = 2020, p-value < 2.2e-16
## alternative hypothesis: significant effects
# como p-value es menor a 0.05 se avanza a los siguientes modelos

#Modelo 3. Efectos Aleatorios 
#Cuando las diferencias no observadas son aleatorias

#Método walhus
walhus_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="random", random.method = "walhus")
summary(walhus_patentes)
## Oneway (individual) effect Random Effect Model 
##    (Wallace-Hussain's transformation)
## 
## Call:
## plm(formula = patents ~ merger + employ + return + stckpr + rnd + 
##     sales + sic, data = panel_patentes, model = "random", random.method = "walhus")
## 
## Unbalanced Panel: n = 226, T = 6-10, N = 2252
## 
## Effects:
##                   var std.dev share
## idiosyncratic  549.82   23.45 0.264
## individual    1531.45   39.13 0.736
## theta:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.7624  0.8138  0.8138  0.8135  0.8138  0.8138 
## 
## Residuals:
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -434.3797   -3.8922   -1.7663    0.0049    0.7622  211.3411 
## 
## Coefficients:
##                Estimate  Std. Error z-value  Pr(>|z|)    
## (Intercept) 11.81991653 13.02952376  0.9072    0.3643    
## merger       4.44477469  4.51229785  0.9850    0.3246    
## employ       1.09431950  0.04897343 22.3452 < 2.2e-16 ***
## return      -0.13029160  0.11861251 -1.0985    0.2720    
## stckpr       0.16740658  0.03358392  4.9847 6.205e-07 ***
## rnd         -0.14658043  0.01471279 -9.9628 < 2.2e-16 ***
## sales       -0.00392718  0.00042924 -9.1492 < 2.2e-16 ***
## sic         -0.00097605  0.00383427 -0.2546    0.7991    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1434600
## Residual Sum of Squares: 1090600
## R-Squared:      0.23978
## Adj. R-Squared: 0.2374
## Chisq: 708.033 on 7 DF, p-value: < 2.22e-16
#Método amemiya
amemiya_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales,
data = panel_patentes, model="random", random.method = "amemiya")
summary(amemiya_patentes)
## Oneway (individual) effect Random Effect Model 
##    (Amemiya's transformation)
## 
## Call:
## plm(formula = patents ~ merger + employ + return + stckpr + rnd + 
##     sales, data = panel_patentes, model = "random", random.method = "amemiya")
## 
## Unbalanced Panel: n = 226, T = 6-10, N = 2252
## 
## Effects:
##                   var std.dev share
## idiosyncratic  405.49   20.14 0.051
## individual    7547.88   86.88 0.949
## theta:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9058  0.9269  0.9269  0.9268  0.9269  0.9269 
## 
## Residuals:
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -454.5441   -2.9615   -1.6605    0.0033    0.6178  193.2184 
## 
## Coefficients:
##                Estimate  Std. Error  z-value  Pr(>|z|)    
## (Intercept) 22.75387365  5.92762406   3.8386 0.0001237 ***
## merger       3.92493580  4.12056765   0.9525 0.3408318    
## employ       0.49109781  0.06164783   7.9662 1.637e-15 ***
## return      -0.09571453  0.10863461  -0.8811 0.3782808    
## stckpr       0.04664956  0.03171241   1.4710 0.1412860    
## rnd         -0.17973247  0.01409500 -12.7515 < 2.2e-16 ***
## sales       -0.00343629  0.00040743  -8.4341 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1144300
## Residual Sum of Squares: 891640
## R-Squared:      0.22083
## Adj. R-Squared: 0.21875
## Chisq: 636.323 on 6 DF, p-value: < 2.22e-16
#Método nerlove
nerlove_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="random", random.method = "nerlove")
summary(nerlove_patentes)
## Oneway (individual) effect Random Effect Model 
##    (Nerlove's transformation)
## 
## Call:
## plm(formula = patents ~ merger + employ + return + stckpr + rnd + 
##     sales + sic, data = panel_patentes, model = "random", random.method = "nerlove")
## 
## Unbalanced Panel: n = 226, T = 6-10, N = 2252
## 
## Effects:
##                   var std.dev share
## idiosyncratic  363.72   19.07 0.046
## individual    7602.26   87.19 0.954
## theta:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9111  0.9310  0.9310  0.9309  0.9310  0.9310 
## 
## Residuals:
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -455.9302   -2.9399   -1.6084    0.0032    0.6178  192.3469 
## 
## Coefficients:
##                Estimate  Std. Error  z-value  Pr(>|z|)    
## (Intercept)  8.37943183 31.51076930   0.2659    0.7903    
## merger       3.88283943  4.10699605   0.9454    0.3444    
## employ       0.45875996  0.06225393   7.3692 1.717e-13 ***
## return      -0.09373310  0.10828377  -0.8656    0.3867    
## stckpr       0.04168538  0.03164395   1.3173    0.1877    
## rnd         -0.18140485  0.01406829 -12.8946 < 2.2e-16 ***
## sales       -0.00340492  0.00040657  -8.3747 < 2.2e-16 ***
## sic          0.00452769  0.00928394   0.4877    0.6258    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1138600
## Residual Sum of Squares: 885010
## R-Squared:      0.22271
## Adj. R-Squared: 0.22028
## Chisq: 642.996 on 7 DF, p-value: < 2.22e-16
#Comparar la r2 ajustada de los 3 métodos y elegir el que tenga el mayor r2 ese lo ponemos en la prueba de hausman

phtest(walhus_patentes, within_patentes)
## 
##  Hausman Test
## 
## data:  patents ~ merger + employ + return + stckpr + rnd + sales + sic
## chisq = 347.29, df = 6, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent
#Si el p-value en <0.05, usamos Efectos Fijos
#Por lo tat nos quedamos con el modelo de efectos fijos (within)

Paso 4: Prueba de heterocedasticidad y autocorrelación serial

#Prieba de heterocedasticidad
bptest(within_patentes)
## 
##  studentized Breusch-Pagan test
## 
## data:  within_patentes
## BP = 1444.3, df = 7, p-value < 2.2e-16
#Si el p-value < 0.05, hay heterocedasticidad en los residuos (problema detectado)

#prueba de autocorrelación serial
pwartest(within_patentes)
## 
##  Wooldridge's test for serial correlation in FE panels
## 
## data:  within_patentes
## F = 104.18, df1 = 1, df2 = 2024, p-value < 2.2e-16
## alternative hypothesis: serial correlation
#Si el p-value < 0.05, hay autocorrelación serial en los errores (problema detectado)

#Modelo de Corrección de Errores Estandar Robustos
coeficientes_corregidos <- coeftest(within_patentes, vcov= vcovHC(within_patentes, type="HC0"))
solo_coeficientes <- coeficientes_corregidos[,1]

Paso 5: Generar Pronosticos y Evaluar modelo

datos_de_prueba <- data.frame( merger=0,  employ=10, return=6, stckpr=48, rnd=3, sales=344)
prediccion <- sum(solo_coeficientes*datos_de_prueba)
prediccion
## [1] -1.449341

Conclusiones

En conclusion este ejercicio nos permitio generar pronosticos en bases de datos con panel, tomando en cuenta los tratamientos para distintos efectos en los datosy sus errores

---
title: "Actividad 1"
author: "Rodrigo Angulo A00834667"
date: "2025-08-12"
output:
   html_document:
     toc: TRUE
     toc_float: TRUE
     code_download: TRUE
     Theme: cosmo
  
---
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)

# <span style ="color: orange; "> Ejercicio 1. Modelo Econométrico
![](C:/Users/admin/Downloads/Captura de pantalla 2025-08-13 165735.png)

# <span style ="color: orange; "> Ejercicio 2. good to great
![](C:\\Users\\admin\\Downloads\\Imagen de WhatsApp 2025-08-13 a las 16.59.49_a154e751.jpg)
# <span style ="color: orange; "> Aplicación de shiny

[Aplicación de shiny](https://rodrigoangulo.shinyapps.io/EM25/)

# <span style ="color: orange; "> Actividad 3 en parejas
[Ejercicio Panel En Equipos](https://rpubs.com/rorroangulo15/1336068)

# <span style ="color: orange; "> instalar paquetes y llamar librerias

```{r message=FALSE, warning=FALSE}
#install.packages("WDI")
#install.packages("wbstats")
library(WDI)
library(wbstats)
#install.packages("tidyverse")
library(tidyverse)
#install.packages("plm") #paquete para realizar modelos lineales para datos de panel
library(plm)
#install.packages("gplots")
library(gplots)
library(readxl)
library(lmtest)
```

## <span style ="color: orange; "> Paso 1: Generar el conjunto de datos de panel

```{r}
gdp <- wb_data(country=c("MX","US","CA"), indicator=c("NY.GDP.PCAP.CD",
                        "SM.POP.NETM"), start_date=1950, end_date=2025)

#generar conjunto de datos de panel
panel_1 <- select(gdp, country, date, NY.GDP.PCAP.CD, SM.POP.NETM)
panel_1 <- subset(panel_1, date == 1960 | date == 1970 | date== 1980 | date== 1990 | date==2000 | date==2010 | date==2020)

panel_1 <- pdata.frame(panel_1, index = c("country","date"))

```
## <span style ="color: orange; "> Paso 2: Prueba de Heterogenidad
```{r}
plotmeans(NY.GDP.PCAP.CD ~ country, main= "Prueba de heterogenidad entre paises para el PIB", data=panel_1)

plotmeans(SM.POP.NETM ~ country, main= "Prueba de heterogenidad entre paises para la migracion neta", data=panel_1)

#si la linea sale casi horizontal, hay poca o nula heterogenidad, por lo que no hay diferencia sitematica que ajustar
#si la linea sale quebrada, sube y baja, hay mucha heterogenidad, por lo que hay que ajustar
```

## <span style ="color: orange; "> Paso 3: Prueba de Efectos fijos y Aleatoris 

```{r}
#Modelo 1. Regresión agrupada (pooled) ##para poca o nula heterogenidad
#asume que no hay heterogenidad no observada
pooled <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="pooling")
summary(pooled)

#Modelo 2. Efectos fijos (within)
#Cuando las diferencias no observadas son constante en el tiempo
within <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="within")
summary(within)

#Prueba F
pFtest(within, pooled)
# Si p-value es menor a 0.05 se prefiere el modelo de efectos fijos

#Modelo 3. Efectos Aleatorios 
#Cuando las diferencias no observadas son aleatorias

#Método walhus
walhus <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="random", random.method = "walhus")
summary(walhus)

#Método amemiya
amemiya <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="random", random.method = "amemiya")
summary(amemiya)

#Método nerlove
nerlove <- plm(NY.GDP.PCAP.CD ~ SM.POP.NETM, data = panel_1, model="random", random.method = "nerlove")
summary(nerlove)

#Comparar la r2 ajustada de los 3 métodos y elegir el que tenga el mayor r2 ese lo ponemos en la prueba de hausman

phtest(walhus, within)
#Si el p-value en <0.05, usamos Efectos Fijos
```
```{r}
patentes <- read_excel("C:\\Users\\admin\\Downloads\\PATENT 3.xls")
```

```{r}
summary(patentes)
str(patentes)
sum(is.na(patentes))#NA´s en la base de datos
sapply(patentes, function(x) sum(is.na(x))) #NA´s por variable
patentes$employ[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$return[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$stckpr[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$rndstck[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
patentes$sales[is.na(patentes$employ)] <- mean(patentes$employ, na.rm=TRUE)
summary(patentes)
sum(is.na(patentes))
boxplot(patentes$cusip, horizontal=TRUE)
boxplot(patentes$merger, horizontal=TRUE)
boxplot(patentes$employ, horizontal=TRUE)
boxplot(patentes$patents, horizontal=TRUE)
boxplot(patentes$return, horizontal=TRUE)
boxplot(patentes$patentsg, horizontal=TRUE)
boxplot(patentes$stckpr, horizontal=TRUE)
boxplot(patentes$rnd, horizontal=TRUE)
boxplot(patentes$rndeflt, horizontal=TRUE)
boxplot(patentes$rndstck, horizontal=TRUE)
boxplot(patentes$sales, horizontal=TRUE)
boxplot(patentes$sic, horizontal=TRUE)
boxplot(patentes$year, horizontal=TRUE)
patentes$year <- patentes$year -40
summary(patentes)


```

## <span style ="color: orange; "> Paso 1: Generar el conjunto de datos de panel

```{r}
#generar conjunto de datos de panel

panel_patentes <- pdata.frame(patentes, index = c("cusip","year"))

```

## <span style ="color: orange; "> Paso 2: Prueba de Heterogenidad
```{r message=FALSE, warning=FALSE}
plotmeans(patents ~ cusip, main= "Prueba de heterogenidad entre empresas para sus patentes", data=panel_patentes)


#Como la linea sale quebrada, sube y baja, hay mucha heterogenidad, por lo que hay que ajustar
```

## <span style ="color: orange; "> Paso 3: Prueba de Efectos fijos y Aleatoris 

```{r}
#Modelo 1. Regresión agrupada (pooled) ##para poca o nula heterogenidad
#asume que no hay heterogenidad no observada
pooled_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="pooling")
summary(pooled_patentes)

#Modelo 2. Efectos fijos (within)
#Cuando las diferencias no observadas son constante en el tiempo
within_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="within")
summary(within_patentes)

#Prueba F
pFtest(within_patentes, pooled_patentes)
# como p-value es menor a 0.05 se avanza a los siguientes modelos

#Modelo 3. Efectos Aleatorios 
#Cuando las diferencias no observadas son aleatorias

#Método walhus
walhus_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="random", random.method = "walhus")
summary(walhus_patentes)

#Método amemiya
amemiya_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales,
data = panel_patentes, model="random", random.method = "amemiya")
summary(amemiya_patentes)

#Método nerlove
nerlove_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, data = panel_patentes, model="random", random.method = "nerlove")
summary(nerlove_patentes)

#Comparar la r2 ajustada de los 3 métodos y elegir el que tenga el mayor r2 ese lo ponemos en la prueba de hausman

phtest(walhus_patentes, within_patentes)
#Si el p-value en <0.05, usamos Efectos Fijos
#Por lo tat nos quedamos con el modelo de efectos fijos (within)
```
## <span style ="color: orange; "> Paso 4: Prueba de heterocedasticidad y autocorrelación serial

```{r}
#Prieba de heterocedasticidad
bptest(within_patentes)
#Si el p-value < 0.05, hay heterocedasticidad en los residuos (problema detectado)

#prueba de autocorrelación serial
pwartest(within_patentes)
#Si el p-value < 0.05, hay autocorrelación serial en los errores (problema detectado)

#Modelo de Corrección de Errores Estandar Robustos
coeficientes_corregidos <- coeftest(within_patentes, vcov= vcovHC(within_patentes, type="HC0"))
solo_coeficientes <- coeficientes_corregidos[,1]
```

## <span style ="color: orange; "> Paso 5: Generar Pronosticos y Evaluar modelo

```{r}
datos_de_prueba <- data.frame( merger=0,  employ=10, return=6, stckpr=48, rnd=3, sales=344)
prediccion <- sum(solo_coeficientes*datos_de_prueba)
prediccion
```
## <span style ="color: orange; "> Conclusiones
En conclusion este ejercicio nos permitio generar pronosticos en bases de datos con panel, tomando en cuenta los tratamientos para distintos efectos en los datosy sus errores

