Entregable1_Patentes

Ejercicio 1. Modelo Econométrico

https://rpubs.com/Samlilith/Modelos_Econometrico

Ejercicio 2. Good to Great

¿Debe una empresa que lo hace bien, buscar sr sobresaliente?

Sí, el mundo y las personas que vivimos en el estamos en constante cambio. Si la empresa que lo hace bien ahora no cambia o no se actualiza con nosotros se puede quedar irrelevante o de plano quebrar. Ej: KODAK empresa de cámaras que no quiso moverse a la era digital.

Shiny App

https://a01422749sam.shinyapps.io/Datos_panel_calc/

Actividad 1. Panel en equipos

https://rpubs.com/Samlilith/Modelos_Econometrico

Actividad 1. Patentes

Aplicar librerías

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

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ ggplot2   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
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library(plm)

## 
## Adjuntando el paquete: 'plm'
## 
## The following objects are masked from 'package:dplyr':
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##     between, lag, lead

library(gplots)

## 
## Adjuntando el paquete: 'gplots'
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## The following object is masked from 'package:stats':
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#install.packages("readxl")
library(readxl)
#install.packages("lmtest")
library(lmtest)

## Cargando paquete requerido: zoo
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## Adjuntando el paquete: 'zoo'
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## The following objects are masked from 'package:base':
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##     as.Date, as.Date.numeric

Contexto

El entorno de negocios en el que las organizaciones se desarrollan es cada vez más dinámico por lo que las empresas enfrentan constantemente el reto de mantenerse al día y superar los nuevos retos que el ambiente presenta. La innovación es una de las mejores formas que las empresas tienen para conseguirlo. De acuerdo con el artículo “Innovation in business: What it is and why is so important” ´publicado en el Harvard Business Review la innovación presenta tres grandes ventajas para las empresas: les permite adaptarse, promueve el crecimiento y además les ayuda a diferenciarse de su competencia generando ventajas competitivas.

Subir base de datos

patentes <- read_excel("C:\\Users\\aleja\\Documents\\00_Carrera_y_formación\\00_TEC_Por semestre_LIT\\SEMESTRE_8\\Bases_de_Datos\\PATENT 3.xls")

Entender la base de datos

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 totales

## [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$return)] <- mean(patentes$return, na.rm = TRUE)
patentes$stckpr[is.na(patentes$stckpr)] <- mean(patentes$stckpr, na.rm = TRUE)
patentes$rndstck[is.na(patentes$rndstck)] <- mean(patentes$rndstck, na.rm = TRUE)
patentes$sales[is.na(patentes$sales)] <- mean(patentes$sales, 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.:  5.139  
##  Median :501116   Median :0.0000   Median :  3.893   Median :  7.601  
##  Mean   :514536   Mean   :0.0177   Mean   : 18.826   Mean   :  8.003  
##  3rd Qu.:754688   3rd Qu.:0.0000   3rd Qu.: 16.034   3rd Qu.: 10.473  
##  Max.   :878555   Max.   :1.0000   Max.   :506.531   Max.   : 48.675  
##     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  
##     rndeflt             rndstck              sales                sic      
##  Min.   :   0.0000   Min.   :   0.1253   Min.   :    1.222   Min.   :2000  
##  1st Qu.:   0.4788   1st Qu.:   5.5882   1st Qu.:   53.204   1st Qu.:2890  
##  Median :   1.4764   Median :  16.2341   Median :  174.283   Median :3531  
##  Mean   :  19.7238   Mean   : 163.8234   Mean   : 1219.601   Mean   :3333  
##  3rd Qu.:   8.7527   3rd Qu.: 119.1048   3rd Qu.:  743.422   3rd Qu.:3661  
##  Max.   :1000.7876   Max.   :9755.3516   Max.   :44224.000   Max.   :9997  
##       year     
##  Min.   :2012  
##  1st Qu.:2014  
##  Median :2016  
##  Mean   :2016  
##  3rd Qu.:2019  
##  Max.   :2021

sum(is.na(patentes)) #NA's después del remove

## [1] 0

Analisis de Exploración de datos

boxplot(patentes$cusip, horizontal = TRUE)

boxplot(patentes$merger, horizontal = TRUE)

boxplot(patentes$employ, horizontal = TRUE)

boxplot(patentes$return, horizontal = TRUE)

boxplot(patentes$patents, 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

Paso 1 Generar conjunto de datos de panel

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

Paso 2: Prueba de Heterogeneidad

plotmeans(patents ~ cusip, main= "Prueba de heterogenidad entre paises para el PIB", data= panel_patentes)

#Como la línea sale quebrada, sube y baja, hay mucha Heterogeneidad, por lo que hay que ajustar.

Paso 3: Prueba de efectos fijos y aleatorios

#Modelo 1. Regresión agrupada (pooled)
#Asume que no hay heterogeneidad observada
pooled <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, panel_patentes, model="pooling")
summary(pooled)

## Pooling Model
## 
## Call:
## plm(formula = patents ~ merger + employ + return + stckpr + rnd + 
##     sales + sic, data = panel_patentes, model = "pooling")
## 
## Balanced Panel: n = 226, T = 10, N = 2260
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -320.36212  -10.01555    0.94472    7.40861  433.86316 
## 
## Coefficients:
##                Estimate  Std. Error t-value  Pr(>|t|)    
## (Intercept) -4.1831e-01  5.2757e+00 -0.0793   0.93681    
## merger      -1.1612e+01  7.2433e+00 -1.6031   0.10905    
## employ       1.3683e+00  4.1969e-02 32.6040 < 2.2e-16 ***
## return      -4.3505e-03  1.8155e-01 -0.0240   0.98088    
## stckpr       6.5137e-01  4.3139e-02 15.0994 < 2.2e-16 ***
## rnd         -1.3853e-01  1.6106e-02 -8.6007 < 2.2e-16 ***
## sales       -3.2049e-03  4.6962e-04 -6.8246  1.13e-11 ***
## sic         -2.6894e-03  1.4820e-03 -1.8146   0.06972 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    10998000
## Residual Sum of Squares: 4600300
## R-Squared:      0.58173
## Adj. R-Squared: 0.58043
## F-statistic: 447.437 on 7 and 2252 DF, p-value: < 2.22e-16

#Modelo 2. Efectos fijos (within)
#Cuando las diferencias no observadas son constantes en el tiempo
within_patentes <- plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, 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")
## 
## Balanced Panel: n = 226, T = 10, N = 2260
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -497.22898   -1.64569   -0.19669    1.64341  184.49423 
## 
## Coefficients:
##           Estimate  Std. Error  t-value  Pr(>|t|)    
## merger  3.30904770  4.16313684   0.7948   0.42680    
## employ  0.11963128  0.07052503   1.6963   0.08998 .  
## return -0.07056694  0.10867769  -0.6493   0.51620    
## stckpr -0.01107952  0.03242512  -0.3417   0.73262    
## rnd    -0.19889614  0.01443066 -13.7829 < 2.2e-16 ***
## sales  -0.00309052  0.00041525  -7.4426 1.451e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1091400
## Residual Sum of Squares: 819280
## R-Squared:      0.24935
## Adj. R-Squared: 0.16385
## F-statistic: 112.278 on 6 and 2028 DF, p-value: < 2.22e-16

#Prueba F
pFtest(within_patentes, pooled)

## 
##  F test for individual effects
## 
## data:  patents ~ merger + employ + return + stckpr + rnd + sales + sic
## F = 41.782, df1 = 224, df2 = 2028, p-value < 2.2e-16
## alternative hypothesis: significant effects

# Si p-value < 0.05 se avanza a los siguientes modelos.
#p-value: < 2.22e-16


#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")
## 
## Balanced Panel: n = 226, T = 10, N = 2260
## 
## Effects:
##                   var std.dev share
## idiosyncratic  555.26   23.56 0.273
## individual    1480.26   38.47 0.727
## theta: 0.8099
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -433.72438   -3.89667   -1.76198    0.78484  211.91016 
## 
## Coefficients:
##                Estimate  Std. Error z-value  Pr(>|z|)    
## (Intercept) 11.84397257 12.78087032  0.9267    0.3541    
## merger       4.47647107  4.51685216  0.9911    0.3217    
## employ       1.10525428  0.04853786 22.7710 < 2.2e-16 ***
## return      -0.12920955  0.11762230 -1.0985    0.2720    
## stckpr       0.17097726  0.03355374  5.0956 3.476e-07 ***
## rnd         -0.14575073  0.01469317 -9.9196 < 2.2e-16 ***
## sales       -0.00393738  0.00042854 -9.1880 < 2.2e-16 ***
## sic         -0.00107515  0.00376075 -0.2859    0.7750    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1449600
## Residual Sum of Squares: 1098300
## R-Squared:      0.24236
## Adj. R-Squared: 0.24
## Chisq: 720.388 on 7 DF, p-value: < 2.22e-16

#Método amemiya
amemiya_patentes <-plm(patents ~ merger + employ + return + stckpr + rnd + sales + sic, 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 + sic, data = panel_patentes, model = "random", random.method = "amemiya")
## 
## Balanced Panel: n = 226, T = 10, N = 2260
## 
## Effects:
##                   var std.dev share
## idiosyncratic  402.79   20.07 0.051
## individual    7483.44   86.51 0.949
## theta: 0.9268
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -454.59697   -2.99704   -1.65272    0.59741  193.17353 
## 
## Coefficients:
##                Estimate  Std. Error  z-value  Pr(>|z|)    
## (Intercept)  8.58107091 29.77947247   0.2882    0.7732    
## merger       3.91351453  4.11354681   0.9514    0.3414    
## employ       0.49060426  0.06153621   7.9726 1.554e-15 ***
## return      -0.09427795  0.10733800  -0.8783    0.3798    
## stckpr       0.04660332  0.03163610   1.4731    0.1407    
## rnd         -0.17995961  0.01406835 -12.7918 < 2.2e-16 ***
## sales       -0.00342554  0.00040647  -8.4275 < 2.2e-16 ***
## sic          0.00425278  0.00877425   0.4847    0.6279    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1144500
## Residual Sum of Squares: 891720
## R-Squared:      0.22085
## Adj. R-Squared: 0.21842
## Chisq: 638.312 on 7 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")
## 
## Balanced Panel: n = 226, T = 10, N = 2260
## 
## Effects:
##                   var std.dev share
## idiosyncratic  362.51   19.04 0.046
## individual    7557.16   86.93 0.954
## theta: 0.9309
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -455.94828   -2.93752   -1.60035    0.62863  192.36375 
## 
## Coefficients:
##                Estimate  Std. Error  z-value  Pr(>|z|)    
## (Intercept)  8.38498937 31.41700295   0.2669    0.7896    
## merger       3.86675065  4.09938561   0.9433    0.3456    
## employ       0.46018862  0.06203371   7.4184 1.186e-13 ***
## return      -0.09236163  0.10697310  -0.8634    0.3879    
## stckpr       0.04167663  0.03156299   1.3204    0.1867    
## rnd         -0.18153379  0.01403810 -12.9315 < 2.2e-16 ***
## sales       -0.00339833  0.00040545  -8.3816 < 2.2e-16 ***
## sic          0.00451640  0.00925634   0.4879    0.6256    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1138700
## Residual Sum of Squares: 885220
## R-Squared:      0.22262
## Adj. R-Squared: 0.22021
## Chisq: 644.925 on 7 DF, p-value: < 2.22e-16

#Comparar la r2 ajustada de los 3 métodos y elegir el que tenga el mayor. 


phtest(walhus_patentes, within_patentes)

## 
##  Hausman Test
## 
## data:  patents ~ merger + employ + return + stckpr + rnd + sales + sic
## chisq = 352.48, df = 6, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent

# si el valor de p-value es < 0.05 usamos Efectos fijos

Paso 4: Pruebas de heterocedasticidad y Autocorrelación serial

#Prueba de heterocedasticidad
bptest(within_patentes)

## 
##  studentized Breusch-Pagan test
## 
## data:  within_patentes
## BP = 1447.6, 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 = 104.29, df1 = 1, df2 = 2032, 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 con Errores Estándar  Robustos
coeficientes_corregidos <- coeftest(within_patentes, vcov = vcovHC(within_patentes, type = "HC0"))
solo_coeficientes <- coeficientes_corregidos[,1]

Paso 5. Generar Pronóstico 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] -1.418735

Conclusiones

En conclusión este ejercicio nos permite generar pronósticos en bases de datos con panel, tomando en cuenta los tratamientos para distintos efectos en los datos y sus errores.