actividad 1
Mariano Bautista A01656904
2025-02-13
Actividad 1 Modelo econometrico
#Mariano Bautista Arreguin #Jose Fernando Cardelas Perez #Daniel Emiliano Najera Otero
#Pregunta. #¿Qué factores afectan el rendimiento de un jugador de futbol? #Modelo económico: #Rendimiento del jugador = f(Habilidad, Condición física, Motivación) #Modelo econométrico: #Rendimiento del jugador = β₀ + β₁(Habilidad) + β₂(Condición física) + β₃(Motivación) + ε #En este modelo: #• β₀ es la constante o intercepto. #• β₁, β₂, β₃ son los coeficientes que indican la relación entre cada variable independiente (habilidad, condición física y motivación) y el rendimiento del jugador. #• ε es el término de error, que captura las variaciones no explicadas por las variables independientes.
Actividad 1,2,3
library(WDI)
library(wbstats)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(gplots)##
## Adjuntando el paquete: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowesslibrary(plm)##
## Adjuntando el paquete: 'plm'
##
## The following objects are masked from 'package:dplyr':
##
## between, lag, lead# Obtener info de 1 pais
# 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. :2025-01-28ggplot(PIB_MEX, aes(x= date, y= NY.GDP.PCAP.CD)) + geom_point()+ geom_line()+ labs(title = "PIB per capita en Mexico(current USD)",x= "Año",y= "valor")
#varios Pais
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. :2025-01-28ggplot(PIB_PANEL, aes(x= date, y= NY.GDP.PCAP.CD, color = iso3c)) + geom_point()+ geom_line()+ labs(title = "PIB per capita en Norte america(current USD)",x= "Año",y= "valor")
MEGAPIB <- wb_data(country = c("MX","US","CA"), indicator = c("NY.GDP.PCAP.CD","SP.DYN.LE00.IN"),start_date = 1900, end_date = 2025)
summary(MEGAPIB)## 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
#variacion entre los individuos
plotmeans(NY.GDP.PCAP.CD ~ country, main = "heterogeneidad entre pais", xlab = "pais", ylab = "PIB peer capita", data = MEGAPIB)## 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
#interpretacion
#Alta heterogeneidad: si los puntos (medias) estan muy separados entre paises.
#Baja heteregoneneidad: si los puntos (medias) estan cerca uno de otros.
#En este caso, EUA y Canada tienen un PIB per capita mayor que Mexico, mostrando
#alta heterogeneidad
#modelos de efectos fijos y aleatorios
#paso 1
datos_panel1 <- pdata.frame(PIB_PANEL, index = c("country","date"))
#modelo de efectos fijos
modelo_efecto_fijos <- plm(NY.GDP.PCAP.CD ~ date,data=datos_panel1,
model="within")
summary(modelo_efecto_fijos)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ date, data = datos_panel1, 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#modelos efecto aleatorio
modelos_efectos_aleatorios <- plm(NY.GDP.PCAP.CD ~ date, data = datos_panel1,
model = "random")
summary(modelos_efectos_aleatorios)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = NY.GDP.PCAP.CD ~ date, data = datos_panel1, 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_efecto_fijos, modelos_efectos_aleatorios)##
## Hausman Test
##
## data: NY.GDP.PCAP.CD ~ date
## chisq = 3.8736e-13, df = 63, p-value = 1
## alternative hypothesis: one model is inconsistentAplicaciones shiny
Dar click aqui para ir a la actividad en equipo en aplicacion de shiny
Actividad patentes
importar la base de datos
# Instalar y cargar el paquete necesario (si no lo has hecho previamente)
library(readxl)
# Seleccionar el archivo usando un cuadro de diálogo
ruta_archivo <- file.choose()
# Leer el archivo Excel seleccionado
patentes <- read_excel(ruta_archivo)
# Si es necesario, convertir a data frame
patentes <- as.data.frame(patentes)
# Ver las primeras filas del archivo
head(patentes)## cusip merger employ return patents patentsg stckpr rnd rndeflt
## 1 800 0 9.848999 5.817664 22 24 47.62500 2.562973 2.562973
## 2 800 0 12.316994 5.689478 34 32 57.87497 3.096045 2.909817
## 3 800 0 12.199997 4.419929 31 30 33.00000 3.272318 2.796852
## 4 800 0 11.842995 5.278894 32 34 38.49997 3.236010 2.518295
## 5 800 0 12.990997 4.907936 40 28 35.12497 3.784838 2.780924
## 6 800 0 13.232994 4.801697 60 33 34.49997 3.860655 2.646095
## rndstck sales sic year
## 1 16.15511 343.6790 3740 2012
## 2 17.35593 436.1460 3740 2013
## 3 19.60889 535.0967 3740 2014
## 4 21.90880 566.9644 3740 2015
## 5 23.14388 631.1006 3740 2016
## 6 25.15906 705.7036 3740 2017entender 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. : 0.125 Min. : 1.22 Min. :2000
## 1st Qu.: 0.4788 1st Qu.: 5.152 1st Qu.: 52.99 1st Qu.:2890
## Median : 1.4764 Median : 13.353 Median : 174.06 Median :3531
## Mean : 19.7238 Mean : 163.823 Mean : 1219.60 Mean :3333
## 3rd Qu.: 8.7527 3rd Qu.: 74.563 3rd Qu.: 728.96 3rd Qu.:3661
## Max. :1000.7876 Max. :9755.352 Max. :44224.00 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] 191sapply(patentes, function(x) sum(is.na(x)))## 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 0patentes1 = na.omit(patentes)library(plm)
panel_patentes <- pdata.frame(patentes1, index = c("cusip","year"))modelo de efectos fijos
modelos_efectos_fijos_patentes <- plm(patents ~ merger + employ + return +patentsg + stckpr + rnd +rndeflt + rndstck + sales + sic,
data = panel_patentes, model = "within")
summary(modelos_efectos_fijos_patentes)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = patents ~ merger + employ + return + patentsg +
## stckpr + rnd + rndeflt + rndstck + sales + sic, data = panel_patentes,
## model = "within")
##
## Unbalanced Panel: n = 215, T = 2-10, N = 2083
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -468.39577 -1.75634 -0.25666 1.85265 172.64513
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## merger 6.02467998 4.30535335 1.3993 0.1619
## employ -0.09095534 0.08057733 -1.1288 0.2591
## return -0.01221444 0.12005904 -0.1017 0.9190
## patentsg 0.03913907 0.02580379 1.5168 0.1295
## stckpr -0.03959771 0.03347713 -1.1828 0.2370
## rnd -2.04101003 0.15053766 -13.5581 < 2.2e-16 ***
## rndeflt 3.25369409 0.22523191 14.4460 < 2.2e-16 ***
## rndstck 0.19724166 0.01808942 10.9037 < 2.2e-16 ***
## sales -0.00188938 0.00041715 -4.5293 6.294e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1090400
## Residual Sum of Squares: 714450
## R-Squared: 0.34479
## Adj. R-Squared: 0.2662
## F-statistic: 108.696 on 9 and 1859 DF, p-value: < 2.22e-16Modelo de efectos aleatorios
modelos_efectos_aleatorios_patentes<- plm(patents ~merger + employ + return +patentsg + stckpr + rnd +rndeflt + rndstck + sales + sic,
data = panel_patentes, model = "random")
summary(modelos_efectos_aleatorios_patentes)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = patents ~ merger + employ + return + patentsg +
## stckpr + rnd + rndeflt + rndstck + sales + sic, data = panel_patentes,
## model = "random")
##
## Unbalanced Panel: n = 215, T = 2-10, N = 2083
##
## Effects:
## var std.dev share
## idiosyncratic 384.3 19.6 1
## individual 0.0 0.0 0
## theta:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -525.42194 -2.59738 -0.31264 1.88763 277.92369
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 1.19864916 2.94181986 0.4075 0.68368
## merger 1.92231907 4.04770404 0.4749 0.63485
## employ 0.12548448 0.03060149 4.1006 4.121e-05 ***
## return 0.06432167 0.10374558 0.6200 0.53526
## patentsg 0.78696226 0.01016726 77.4016 < 2.2e-16 ***
## stckpr 0.00355791 0.02557045 0.1391 0.88934
## rnd -0.18291882 0.04480367 -4.0827 4.452e-05 ***
## rndeflt 0.26805014 0.03877619 6.9128 4.753e-12 ***
## rndstck -0.00122890 0.00628664 -0.1955 0.84502
## sales -0.00054529 0.00025769 -2.1161 0.03434 *
## sic -0.00049485 0.00081918 -0.6041 0.54579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 10910000
## Residual Sum of Squares: 1154800
## R-Squared: 0.89416
## Adj. R-Squared: 0.89365
## Chisq: 17504.4 on 10 DF, p-value: < 2.22e-16prueba de hausman
library(lmtest)## Cargando paquete requerido: zoo##
## Adjuntando el paquete: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(pglm)## Cargando paquete requerido: maxLik## Cargando paquete requerido: miscTools##
## Please cite the 'maxLik' package as:
## Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.
##
## If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
## https://r-forge.r-project.org/projects/maxlik/phtest(modelos_efectos_aleatorios_patentes,modelos_efectos_fijos_patentes )##
## Hausman Test
##
## data: patents ~ merger + employ + return + patentsg + stckpr + rnd + ...
## chisq = 1104.9, df = 9, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent3 prueba de heterocedasticidad y autocorrelacion serial
bptest(modelos_efectos_fijos_patentes)##
## studentized Breusch-Pagan test
##
## data: modelos_efectos_fijos_patentes
## BP = 617.25, df = 10, p-value < 2.2e-16bptest(modelos_efectos_aleatorios_patentes)##
## studentized Breusch-Pagan test
##
## data: modelos_efectos_aleatorios_patentes
## BP = 617.25, df = 10, p-value < 2.2e-16pbnftest(modelos_efectos_aleatorios_patentes)##
## modified Bhargava/Franzini/Narendranathan Panel Durbin-Watson Test
##
## data: patents ~ merger + employ + return + patentsg + stckpr + rnd + ...
## DW = 1.0069
## alternative hypothesis: serial correlation in idiosyncratic errorspwartest(modelos_efectos_fijos_patentes)##
## Wooldridge's test for serial correlation in FE panels
##
## data: modelos_efectos_fijos_patentes
## F = 42.281, df1 = 1, df2 = 1866, p-value = 1.012e-10
## alternative hypothesis: serial correlationcoeficiente_corregido <- coeftest(modelos_efectos_fijos_patentes, vcoc=vcocHC(modelos_efectos_fijos_patentes,
type="HC0"))
solo_coeficientes <- coeficiente_corregido[,1]4 generar pronosticos y evaluar modelo
datos_de_prueba <- data.frame(merger=0, employ=10, returns=6, patentsg=24, stckpr=48, rnd=3, rndeflt=3,rndstck=16, sales=344)
prediccion <- sum(solo_coeficientes*c(datos_de_prueba$merger,datos_de_prueba$employ,datos_de_prueba$return,
datos_de_prueba$patentsg, datos_de_prueba$stckpr, datos_de_prueba$rnd, datos_de_prueba$rndeflt,
datos_de_prueba$rndstck, datos_de_prueba$sales))
prediccion## [1] 4.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