# Librerías
#-------------------------
#install.packages("WDI")
#install.packages("wbstats")
#install.packages("tidyverse")
#install.packages("plm")
#install.packages("gplots")
library(WDI)
library(wbstats)
library(tidyverse)
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## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
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## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(plm)
## 
## Attaching package: 'plm'
## The following objects are masked from 'package:dplyr':
## 
##     between, lag, lead
library(gplots)
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess

OBTENCIÓN DE LOS DATOS

# DATA
# ----------------------------------------
gpd_data <- wb_data(country=c("MX", "EC", "CA"),
indicator = "NY.GDP.PCAP.CD", start_date=1950, end_date=2023)
gpd_data
## # A tibble: 189 × 9
##    iso2c iso3c country  date NY.GDP.PCAP.CD unit  obs_status footnote
##    <chr> <chr> <chr>   <dbl>          <dbl> <chr> <chr>      <chr>   
##  1 CA    CAN   Canada   1960          2259. <NA>  <NA>       <NA>    
##  2 CA    CAN   Canada   1961          2240. <NA>  <NA>       <NA>    
##  3 CA    CAN   Canada   1962          2269. <NA>  <NA>       <NA>    
##  4 CA    CAN   Canada   1963          2374. <NA>  <NA>       <NA>    
##  5 CA    CAN   Canada   1964          2555. <NA>  <NA>       <NA>    
##  6 CA    CAN   Canada   1965          2770. <NA>  <NA>       <NA>    
##  7 CA    CAN   Canada   1966          3047. <NA>  <NA>       <NA>    
##  8 CA    CAN   Canada   1967          3217. <NA>  <NA>       <NA>    
##  9 CA    CAN   Canada   1968          3463. <NA>  <NA>       <NA>    
## 10 CA    CAN   Canada   1969          3764. <NA>  <NA>       <NA>    
## # ℹ 179 more rows
## # ℹ 1 more variable: last_updated <date>
# PANEL
# ----------------------------
panel <- select(gpd_data, country, date, NY.GDP.PCAP.CD)
panel
## # A tibble: 189 × 3
##    country  date NY.GDP.PCAP.CD
##    <chr>   <dbl>          <dbl>
##  1 Canada   1960          2259.
##  2 Canada   1961          2240.
##  3 Canada   1962          2269.
##  4 Canada   1963          2374.
##  5 Canada   1964          2555.
##  6 Canada   1965          2770.
##  7 Canada   1966          3047.
##  8 Canada   1967          3217.
##  9 Canada   1968          3463.
## 10 Canada   1969          3764.
## # ℹ 179 more rows
#Industrialización, inflación, exportación

comercio_data <- wb_data(country=c("US", "CN", "IR", "UA"), indicator=c("FP.CPI.TOTL.ZG", "NE.EXP.GNFS.ZS", "NV.IND.MANF.ZS"), start_date=1970, end_date=2023)
comercio_data
## # A tibble: 212 × 7
##    iso2c iso3c country  date FP.CPI.TOTL.ZG NE.EXP.GNFS.ZS NV.IND.MANF.ZS
##    <chr> <chr> <chr>   <dbl>          <dbl>          <dbl>          <dbl>
##  1 CN    CHN   China    1970             NA           2.49             NA
##  2 CN    CHN   China    1971             NA           2.79             NA
##  3 CN    CHN   China    1972             NA           3.25             NA
##  4 CN    CHN   China    1973             NA           4.24             NA
##  5 CN    CHN   China    1974             NA           4.93             NA
##  6 CN    CHN   China    1975             NA           4.70             NA
##  7 CN    CHN   China    1976             NA           4.51             NA
##  8 CN    CHN   China    1977             NA           4.30             NA
##  9 CN    CHN   China    1978             NA           4.56             NA
## 10 CN    CHN   China    1979             NA           5.16             NA
## # ℹ 202 more rows
panel_comercio <- select(comercio_data, country, date, FP.CPI.TOTL.ZG, NE.EXP.GNFS.ZS, NV.IND.MANF.ZS )
panel_comercio <- subset(panel_comercio, date == 1990 | date == 2000 | date == 2010 | date == 2020)
panel_comercio <- pdata.frame(panel_comercio, index = c("country", "date"))
panel_comercio
##                                    country date FP.CPI.TOTL.ZG NE.EXP.GNFS.ZS
## China-1990                           China 1990      3.0522901      12.451603
## China-2000                           China 2000      0.3478112      20.893697
## China-2010                           China 2010      3.1753248      27.185333
## China-2020                           China 2020      2.4194219      18.586139
## Iran, Islamic Rep.-1990 Iran, Islamic Rep. 1990      7.6276749      13.278742
## Iran, Islamic Rep.-2000 Iran, Islamic Rep. 2000     14.4767513      21.467058
## Iran, Islamic Rep.-2010 Iran, Islamic Rep. 2010     10.0893629      24.399653
## Iran, Islamic Rep.-2020 Iran, Islamic Rep. 2020     30.5941390      19.424609
## Ukraine-1990                       Ukraine 1990             NA      27.664671
## Ukraine-2000                       Ukraine 2000     28.2030972      60.297057
## Ukraine-2010                       Ukraine 2010      9.3729311      46.456538
## Ukraine-2020                       Ukraine 2020      2.7324921      38.821646
## United States-1990           United States 1990      5.3979564       9.254732
## United States-2000           United States 2000      3.3768573      10.692777
## United States-2010           United States 2010      1.6400434      12.341361
## United States-2020           United States 2020      1.2335844      10.209229
##                         NV.IND.MANF.ZS
## China-1990                          NA
## China-2000                          NA
## China-2010                    31.61282
## China-2020                    26.28517
## Iran, Islamic Rep.-1990       14.51243
## Iran, Islamic Rep.-2000       16.63923
## Iran, Islamic Rep.-2010       12.78391
## Iran, Islamic Rep.-2020       21.13949
## Ukraine-1990                        NA
## Ukraine-2000                  17.38735
## Ukraine-2010                  13.09575
## Ukraine-2020                  10.10138
## United States-1990                  NA
## United States-2000            15.11507
## United States-2010            11.90609
## United States-2020            10.62906
library(skimr)
skim(panel_comercio)
Data summary
Name panel_comercio
Number of rows 16
Number of columns 5
_______________________
Column type frequency:
factor 2
numeric 3
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
country 0 1 FALSE 4 Chi: 4, Ira: 4, Ukr: 4, Uni: 4
date 0 1 FALSE 4 199: 4, 200: 4, 201: 4, 202: 4

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
FP.CPI.TOTL.ZG 1 0.94 8.25 9.43 0.35 2.58 3.38 9.73 30.59 ▇▂▁▁▂
NE.EXP.GNFS.ZS 0 1.00 23.34 14.36 9.25 12.42 20.16 27.31 60.30 ▇▅▁▁▁
NV.IND.MANF.ZS 4 0.75 16.77 6.56 10.10 12.56 14.81 18.33 31.61 ▇▆▂▂▂

Tenemos datos ausentes. Los vamos a reemplazar por la media.

#Na's = reemplazar por el promedio
panel_comercio$NV.IND.MANF.ZS[is.na(panel_comercio$NV.IND.MANF.ZS)]<-mean(panel_comercio$NV.IND.MANF.ZS,na.rm=TRUE)
panel_comercio$FP.CPI.TOTL.ZG [is.na(panel_comercio$FP.CPI.TOTL.ZG)]<-mean(panel_comercio$FP.CPI.TOTL.ZG
,na.rm=TRUE)
panel_comercio
##                                    country date FP.CPI.TOTL.ZG NE.EXP.GNFS.ZS
## China-1990                           China 1990      3.0522901      12.451603
## China-2000                           China 2000      0.3478112      20.893697
## China-2010                           China 2010      3.1753248      27.185333
## China-2020                           China 2020      2.4194219      18.586139
## Iran, Islamic Rep.-1990 Iran, Islamic Rep. 1990      7.6276749      13.278742
## Iran, Islamic Rep.-2000 Iran, Islamic Rep. 2000     14.4767513      21.467058
## Iran, Islamic Rep.-2010 Iran, Islamic Rep. 2010     10.0893629      24.399653
## Iran, Islamic Rep.-2020 Iran, Islamic Rep. 2020     30.5941390      19.424609
## Ukraine-1990                       Ukraine 1990      8.2493159      27.664671
## Ukraine-2000                       Ukraine 2000     28.2030972      60.297057
## Ukraine-2010                       Ukraine 2010      9.3729311      46.456538
## Ukraine-2020                       Ukraine 2020      2.7324921      38.821646
## United States-1990           United States 1990      5.3979564       9.254732
## United States-2000           United States 2000      3.3768573      10.692777
## United States-2010           United States 2010      1.6400434      12.341361
## United States-2020           United States 2020      1.2335844      10.209229
##                         NV.IND.MANF.ZS
## China-1990                    16.76731
## China-2000                    16.76731
## China-2010                    31.61282
## China-2020                    26.28517
## Iran, Islamic Rep.-1990       14.51243
## Iran, Islamic Rep.-2000       16.63923
## Iran, Islamic Rep.-2010       12.78391
## Iran, Islamic Rep.-2020       21.13949
## Ukraine-1990                  16.76731
## Ukraine-2000                  17.38735
## Ukraine-2010                  13.09575
## Ukraine-2020                  10.10138
## United States-1990            16.76731
## United States-2000            15.11507
## United States-2010            11.90609
## United States-2020            10.62906
skim(panel_comercio)
Data summary
Name panel_comercio
Number of rows 16
Number of columns 5
_______________________
Column type frequency:
factor 2
numeric 3
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
country 0 1 FALSE 4 Chi: 4, Ira: 4, Ukr: 4, Uni: 4
date 0 1 FALSE 4 199: 4, 200: 4, 201: 4, 202: 4

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
FP.CPI.TOTL.ZG 0 1 8.25 9.11 0.35 2.65 4.39 9.55 30.59 ▇▃▁▁▂
NE.EXP.GNFS.ZS 0 1 23.34 14.36 9.25 12.42 20.16 27.31 60.30 ▇▅▁▁▁
NV.IND.MANF.ZS 0 1 16.77 5.62 10.10 13.02 16.70 16.92 31.61 ▅▇▁▁▁

GRÁFICOS DE HETEROGENEIDAD

# PLOTMEANS
#-------------------------------------
plotmeans(NE.EXP.GNFS.ZS ~ country, data = panel_comercio,
          main = "Heterogeneidad entre países",
          xlab = "País", ylab = "Exportación",
          barcol = "blue")
## 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

Existen diferencias en media entre los países de la muestra.

plotmeans(NE.EXP.GNFS.ZS ~ date, data = panel_comercio,
          main = "Heterogeneidad entre años",
          xlab = "años", ylab = "Exportación",
          barcol = "red")

En el año 2000 se produjo el máximo en índice de exportaciones y a partir de este año ha ido descendiendo hasta 20.

# PLOTMEANS
#-------------------------------------
plotmeans(FP.CPI.TOTL.ZG ~ country, data = panel_comercio,
          main = "Heterogeneidad entre países",
          xlab = "País", ylab = "Inflación",
          barcol = "blue")

Irán y Ucrania tiene un índice de inflación muy superior al resto.

plotmeans(FP.CPI.TOTL.ZG ~ date, data = panel_comercio,
          main = "Heterogeneidad entre años",
          xlab = "Años", ylab = "Inflación",
          barcol = "red")

La media de la inflación es similar en cada uno de los años.

plotmeans(NV.IND.MANF.ZS ~ country, data = panel_comercio,
          main = "Heterogeneidad entre países",
          xlab = "País", ylab = "Industrialización",
          barcol = "blue")

El índice de industrialización es superior en China e Irán respecto al resto.

plotmeans(NV.IND.MANF.ZS ~ date, data = panel_comercio,
          main = "Heterogeneidad entre países",
          xlab = "años", ylab = "Industrialización",
          barcol = "red")

El índice de industrialización es similar en los cuatro años.

REGRESIÓN AGRUPADA O POOLED

pooled <- plm(NE.EXP.GNFS.ZS ~NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, data = panel_comercio, model= "pooling")
summary(pooled)
## Pooling Model
## 
## Call:
## plm(formula = NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, 
##     data = panel_comercio, model = "pooling")
## 
## Balanced Panel: n = 4, T = 4, N = 16
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -20.3587  -8.7484  -3.7500   5.6762  21.8281 
## 
## Coefficients:
##                Estimate Std. Error t-value Pr(>|t|)  
## (Intercept)    19.32268   11.13593  1.7352  0.10634  
## NV.IND.MANF.ZS -0.13559    0.62589 -0.2166  0.83186  
## FP.CPI.TOTL.ZG  0.76246    0.38585  1.9761  0.06976 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    3093.4
## Residual Sum of Squares: 2378.9
## R-Squared:      0.231
## Adj. R-Squared: 0.11269
## F-statistic: 1.95251 on 2 and 13 DF, p-value: 0.18135

Constante = 19,32. El índice de exportaciones independiente del valor de la inflación y del índice de industrialización es de 19,32 unidades. El coeficiente tiene un p valor de 0,1063, siendo superior a los niveles del 1%, 5% y 10%. El coeficiente de la constante es similar al valor cero (acepta H0).

Industria = -0,13. Se estima que un aumento en el índice de industris de una unidad, con el resto de factores constantes, provocaría un descenso del índice de exportaciones de 0,13 unidades. El coeficiente tiene un p valor de 0,83816, siendo superior a los niveles del 1%, 5% y 10%. El coeficiente de la constante es similar al valor cero (acepta H0). La variable industrialización es irrelevante para determinar el nivel de exportaciones.

Inflación = 0,76. Se estima que un incremento de la inflación de una unidad, con el resto de variables constantes, provocaría un incremento de las exportacioned de 0,76 unidades. El p-valor en este caso es de 0,06, indicando que al nivel del 10% sería una variable relevnte, pero no al 5% ni al 1%.

EFECTOS FIJOS

within <- plm(NE.EXP.GNFS.ZS ~NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, data = panel_comercio, model = "within")
summary(within)
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, 
##     data = panel_comercio, model = "within")
## 
## Balanced Panel: n = 4, T = 4, N = 16
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -13.49706  -2.17112   0.51032   3.02790   8.29901 
## 
## Coefficients:
##                 Estimate Std. Error t-value Pr(>|t|)
## NV.IND.MANF.ZS -0.013969   0.530951 -0.0263   0.9795
## FP.CPI.TOTL.ZG  0.543505   0.322619  1.6847   0.1230
## 
## Total Sum of Squares:    746.1
## Residual Sum of Squares: 541.05
## R-Squared:      0.27483
## Adj. R-Squared: -0.087758
## F-statistic: 1.89492 on 2 and 10 DF, p-value: 0.20054
within$coefficients
## NV.IND.MANF.ZS FP.CPI.TOTL.ZG 
##    -0.01396916     0.54350463
pFtest(within, pooled) #¿Efectos fijos relevantes?
## 
##  F test for individual effects
## 
## data:  NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG
## F = 11.323, df1 = 3, df2 = 10, p-value = 0.001483
## alternative hypothesis: significant effects

H0: la constante de cada país es similar alfa1 = alfa2 = alfa3 = alfa4 (la constante de todos los países es similar)[POOLED]

H1: La constante de cada país es diferente.

El p-valor = 0,001 < 0,05. Tenemos evidencia empírica para rechazar H0 al 1%, al 5% y al 10%. Por tanto, las constantes son diferentes, indicando que el modelo pooled es inconsistente, pues no capta la heterogeneidad inobservable entre países.

MODELO RANDOM EFFECTS 1

walhus <- plm(NE.EXP.GNFS.ZS ~NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, data = panel_comercio, model= "random", random.method = "walhus")
summary(walhus)
## Oneway (individual) effect Random Effect Model 
##    (Wallace-Hussain's transformation)
## 
## Call:
## plm(formula = NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, 
##     data = panel_comercio, model = "random", random.method = "walhus")
## 
## Balanced Panel: n = 4, T = 4, N = 16
## 
## Effects:
##                   var std.dev share
## idiosyncratic  47.251   6.874 0.318
## individual    101.428  10.071 0.682
## theta: 0.677
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -11.3695  -4.6894  -0.8683   5.5393  13.3124 
## 
## Coefficients:
##                 Estimate Std. Error z-value Pr(>|z|)  
## (Intercept)    19.522835   9.587209  2.0363  0.04172 *
## NV.IND.MANF.ZS -0.064129   0.500120 -0.1282  0.89797  
## FP.CPI.TOTL.ZG  0.592957   0.304645  1.9464  0.05161 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    990.96
## Residual Sum of Squares: 737.68
## R-Squared:      0.25559
## Adj. R-Squared: 0.14107
## Chisq: 4.46357 on 2 DF, p-value: 0.10734
amemiya <- plm(NE.EXP.GNFS.ZS ~NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, data = panel_comercio, model= "random", random.method = "amemiya")
summary(amemiya)
## Oneway (individual) effect Random Effect Model 
##    (Amemiya's transformation)
## 
## Call:
## plm(formula = NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, 
##     data = panel_comercio, model = "random", random.method = "amemiya")
## 
## Balanced Panel: n = 4, T = 4, N = 16
## 
## Effects:
##                   var std.dev share
## idiosyncratic  45.087   6.715 0.296
## individual    107.467  10.367 0.704
## theta: 0.6919
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -11.19960  -4.57480  -0.81202   5.56180  13.10955 
## 
## Coefficients:
##                Estimate Std. Error z-value Pr(>|z|)  
## (Intercept)    19.49533    9.67140  2.0158  0.04382 *
## NV.IND.MANF.ZS -0.06062    0.49747 -0.1219  0.90301  
## FP.CPI.TOTL.ZG  0.58916    0.30297  1.9446  0.05182 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    968.93
## Residual Sum of Squares: 720.09
## R-Squared:      0.25682
## Adj. R-Squared: 0.14248
## Chisq: 4.49229 on 2 DF, p-value: 0.10581
nerlove <- plm(NE.EXP.GNFS.ZS ~NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, data = panel_comercio, model= "random", random.method = "nerlove")
summary(nerlove)
## Oneway (individual) effect Random Effect Model 
##    (Nerlove's transformation)
## 
## Call:
## plm(formula = NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, 
##     data = panel_comercio, model = "random", random.method = "nerlove")
## 
## Balanced Panel: n = 4, T = 4, N = 16
## 
## Effects:
##                   var std.dev share
## idiosyncratic  33.816   5.815 0.176
## individual    158.318  12.582 0.824
## theta: 0.7749
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -10.29388  -3.93960  -0.47956   5.00513  11.93748 
## 
## Coefficients:
##                Estimate Std. Error z-value Pr(>|z|)  
## (Intercept)    19.33851   10.64463  1.8167  0.06926 .
## NV.IND.MANF.ZS -0.04169    0.48394 -0.0861  0.93135  
## FP.CPI.TOTL.ZG  0.56969    0.29442  1.9350  0.05300 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    865.09
## Residual Sum of Squares: 636.95
## R-Squared:      0.26371
## Adj. R-Squared: 0.15044
## Chisq: 4.65618 on 2 DF, p-value: 0.097482

HAUSMAN TEST

PANEL, PASOS:

1.- Comparar entre pooled y FE/RE —- FE 2.- Comparar FE/RE ¿cuál es mejor? —- Test de Hausman

H0: El modelo de RE es consistente y no hace falta estimar por FE. H1: El modleo de RE es inconsistente y sí hace falta estimar por FE.

#HT1
#------------------------
phtest(walhus, within)
## 
##  Hausman Test
## 
## data:  NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG
## chisq = 0.12456, df = 2, p-value = 0.9396
## alternative hypothesis: one model is inconsistent

P-valor = 0,93 > 0,05 — acepta H0 —- RE Walhus es consistente (mejor)

#HT2
#----------------------------
phtest(amemiya, within)
## 
##  Hausman Test
## 
## data:  NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG
## chisq = 0.60266, df = 2, p-value = 0.7398
## alternative hypothesis: one model is inconsistent

P-valor = 0,73 > 0,05 — acepta H0 —- RE Amemiya es consistente (mejor)

#HT3
#----------------------------
phtest(nerlove, within)
## 
##  Hausman Test
## 
## data:  NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG
## chisq = 0.039574, df = 2, p-value = 0.9804
## alternative hypothesis: one model is inconsistent

P valor = 0,98 > 0,05 – acepto H0 — RE Nerlove es consistente (mejor)

R2 ajustado WALHUS = 0.14107

R2 ajustado AMEMIYA = 0.14248

R2 ajustado NERLOVE = 0.15044 – La mejor estimación y más consistente es la del método NERLOVE.

final <- plm(NE.EXP.GNFS.ZS ~NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, data = panel_comercio, model= "random", random.method = "nerlove")
summary(final)
## Oneway (individual) effect Random Effect Model 
##    (Nerlove's transformation)
## 
## Call:
## plm(formula = NE.EXP.GNFS.ZS ~ NV.IND.MANF.ZS + FP.CPI.TOTL.ZG, 
##     data = panel_comercio, model = "random", random.method = "nerlove")
## 
## Balanced Panel: n = 4, T = 4, N = 16
## 
## Effects:
##                   var std.dev share
## idiosyncratic  33.816   5.815 0.176
## individual    158.318  12.582 0.824
## theta: 0.7749
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -10.29388  -3.93960  -0.47956   5.00513  11.93748 
## 
## Coefficients:
##                Estimate Std. Error z-value Pr(>|z|)  
## (Intercept)    19.33851   10.64463  1.8167  0.06926 .
## NV.IND.MANF.ZS -0.04169    0.48394 -0.0861  0.93135  
## FP.CPI.TOTL.ZG  0.56969    0.29442  1.9350  0.05300 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    865.09
## Residual Sum of Squares: 636.95
## R-Squared:      0.26371
## Adj. R-Squared: 0.15044
## Chisq: 4.65618 on 2 DF, p-value: 0.097482

Constante = 19,33. Se estima que la constante del modelo, independientemente de la industrialización y de la inflación, sea de 19,33 unidades. El coeficiente es significativo al 10%, ya que su p-valor es 0,069 < 0,10.

Industrialización = -0,04. Al aumentar el índice de industrialización en una unidad, con lo demás constante, provocaría que el índice de exportaciones disminuya en 0,04 unidades. El coeficiente es irrelevante, pues el p-valor (0,93) es mayor al 10%.

Inflación = 0,56. Al aumentar el índice de industrialización en una unidad, con lo demás constante, provocaría que el índice de exportaciones disminuya en 0,56 unidades. El coeficiente es relevante, pues el p-valor (0,053) es inferior al 10%.

library(stargazer)
## 
## Please cite as:
##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
stargazer(pooled, within, walhus, amemiya, final, type = "text")
## 
## ===============================================================================
##                                      Dependent variable:                       
##                ----------------------------------------------------------------
##                                         NE.EXP.GNFS.ZS                         
##                       (1)                (2)           (3)      (4)      (5)   
## -------------------------------------------------------------------------------
## NV.IND.MANF.ZS       -0.136             -0.014        -0.064   -0.061   -0.042 
##                     (0.626)            (0.531)       (0.500)  (0.497)  (0.484) 
##                                                                                
## FP.CPI.TOTL.ZG       0.762*             0.544         0.593*   0.589*   0.570* 
##                     (0.386)            (0.323)       (0.305)  (0.303)  (0.294) 
##                                                                                
## Constant             19.323                          19.523** 19.495** 19.339* 
##                     (11.136)                         (9.587)  (9.671)  (10.645)
##                                                                                
## -------------------------------------------------------------------------------
## Observations           16                 16            16       16       16   
## R2                   0.231              0.275         0.256    0.257    0.264  
## Adjusted R2          0.113              -0.088        0.141    0.142    0.150  
## F Statistic    1.953 (df = 2; 13) 1.895 (df = 2; 10)  4.464    4.492    4.656* 
## ===============================================================================
## Note:                                               *p<0.1; **p<0.05; ***p<0.01

CONCLUSIONES

El mejor modelo como hemos comentado es el modelo (5), que obtiene las estimaciones más precisas y correctas.