inst/doc/replication.R

In alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

## ----eval = FALSE-------------------------------------------------------------
# # Import the data set
# library(haven)
# library(data.table)
# cudata <- read_dta("dataaxj1.dta")
# setDT(cudata)
# 
# # Subsetting relevant variables
# var.nms <- c("exp1to2", "custrict11", "ldist", "comlang", "border", "regional",
#              "comcol", "curcol", "colony", "comctry", "cuwoemu", "emu", "cuc",
#              "cty1", "cty2", "year", "pairid")
# cudata <- cudata[, ..var.nms]
# 
# # Generate identifiers required for structural gravity
# cudata[, pairid := factor(pairid)]
# cudata[, exp.time := interaction(cty1, year)]
# cudata[, imp.time := interaction(cty2, year)]
# 
# # Generate dummies for disaggregated currency unions
# cudata[, cuau := as.integer(cuc == "au")]
# cudata[, cube := as.integer(cuc == "be")]
# cudata[, cuca := as.integer(cuc == "ca")]
# cudata[, cucf := as.integer(cuc == "cf")]
# cudata[, cucp := as.integer(cuc == "cp")]
# cudata[, cudk := as.integer(cuc == "dk")]
# cudata[, cuea := as.integer(cuc == "ea")]
# cudata[, cuec := as.integer(cuc == "ec")]
# cudata[, cuem := as.integer(cuc == "em")]
# cudata[, cufr := as.integer(cuc == "fr")]
# cudata[, cugb := as.integer(cuc == "gb")]
# cudata[, cuin := as.integer(cuc == "in")]
# cudata[, cuma := as.integer(cuc == "ma")]
# cudata[, cuml := as.integer(cuc == "ml")]
# cudata[, cunc := as.integer(cuc == "nc")]
# cudata[, cunz := as.integer(cuc == "nz")]
# cudata[, cupk := as.integer(cuc == "pk")]
# cudata[, cupt := as.integer(cuc == "pt")]
# cudata[, cusa := as.integer(cuc == "sa")]
# cudata[, cusp := as.integer(cuc == "sp")]
# cudata[, cuua := as.integer(cuc == "ua")]
# cudata[, cuus := as.integer(cuc == "us")]
# cudata[, cuwa := as.integer(cuc == "wa")]
# cudata[, cuwoo := custrict11]
# cudata[cuc %in% c("em", "au", "cf", "ec", "fr", "gb", "in", "us"), cuwoo := 0L]
# 
# # Set missing trade flows to zero
# cudata[is.na(exp1to2), exp1to2 := 0.0]

## ----eval = FALSE-------------------------------------------------------------
# mod <- feglm(exp1to2 ~ emu + cuwoo + cuau + cucf + cuec + cufr + cugb + cuin + cuus +
#                regional + curcol | exp.time + imp.time + pairid | cty1 + cty2 + year, cudata,
#              family = poisson())
# summary(mod, "sandwich")

## ----eval = FALSE-------------------------------------------------------------
# ## poisson - log link
# ##
# ## exp1to2 ~ emu + cuwoo + cuau + cucf + cuec + cufr + cugb + cuin +
# ##     cuus + regional + curcol | exp.time + imp.time + pairid |
# ##     cty1 + cty2 + year
# ##
# ## Estimates:
# ##            Estimate Std. error z value Pr(> |z|)
# ## emu       0.0488950  0.0006057  80.722   < 2e-16 ***
# ## cuwoo     0.7659882  0.0047822 160.176   < 2e-16 ***
# ## cuau      0.3844686  0.0562134   6.839  7.95e-12 ***
# ## cucf     -0.1256085  0.0231247  -5.432  5.58e-08 ***
# ## cuec     -0.8773179  0.0234656 -37.387   < 2e-16 ***
# ## cufr      2.0957255  0.0048882 428.730   < 2e-16 ***
# ## cugb      1.0599574  0.0021330 496.925   < 2e-16 ***
# ## cuin      0.1697449  0.0068729  24.698   < 2e-16 ***
# ## cuus      0.0183233  0.0025739   7.119  1.09e-12 ***
# ## regional  0.1591810  0.0003433 463.662   < 2e-16 ***
# ## curcol    0.3868821  0.0022466 172.208   < 2e-16 ***
# ## ---
# ## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 35830779,
# ## null deviance= 2245707302,
# ## n= 1610165, l= [11227, 11277, 34104]
# ##
# ## ( 1363003 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 13

## ----eval = FALSE-------------------------------------------------------------
# summary(mod, "clustered", cluster = ~ cty1 + cty2 + year)

## ----eval = FALSE-------------------------------------------------------------
# ## poisson - log link
# ##
# ## exp1to2 ~ emu + cuwoo + cuau + cucf + cuec + cufr + cugb + cuin +
# ##     cuus + regional + curcol | exp.time + imp.time + pairid |
# ##     cty1 + cty2 + year
# ##
# ## Estimates:
# ##          Estimate Std. error z value Pr(> |z|)
# ## emu       0.04890    0.09455   0.517   0.60507
# ## cuwoo     0.76599    0.24933   3.072   0.00213 **
# ## cuau      0.38447    0.22355   1.720   0.08546 .
# ## cucf     -0.12561    0.35221  -0.357   0.72137
# ## cuec     -0.87732    0.29493  -2.975   0.00293 **
# ## cufr      2.09573    0.30625   6.843  7.75e-12 ***
# ## cugb      1.05996    0.23766   4.460  8.19e-06 ***
# ## cuin      0.16974    0.30090   0.564   0.57267
# ## cuus      0.01832    0.05092   0.360   0.71898
# ## regional  0.15918    0.07588   2.098   0.03593 *
# ## curcol    0.38688    0.15509   2.495   0.01261 *
# ## ---
# ## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 35830779,
# ## null deviance= 2245707302,
# ## n= 1610165, l= [11227, 11277, 34104]
# ##
# ## ( 1363003 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 13

## ----eval = FALSE-------------------------------------------------------------
# library(car)
# cus <- c("cuwoo", "cuau", "cucf", "cuec", "cufr", "cugb", "cuin", "cuus")
# linearHypothesis(mod, cus, vcov. = vcov(mod, "clustered", cluster = ~ cty1 + cty2 + year))

## ----eval = FALSE-------------------------------------------------------------
# ## Linear hypothesis test
# ##
# ## Hypothesis:
# ## cuwoo = 0
# ## cuau = 0
# ## cucf = 0
# ## cuec = 0
# ## cufr = 0
# ## cugb = 0
# ## cuin = 0
# ## cuus = 0
# ##
# ## Model 1: restricted model
# ## Model 2: exp1to2 ~ emu + cuwoo + cuau + cucf + cuec + cufr + cugb + cuin +
# ##     cuus + regional + curcol | exp.time + imp.time + pairid |
# ##     cty1 + cty2 + year
# ##
# ## Note: Coefficient covariance matrix supplied.
# ##
# ##   Df  Chisq Pr(>Chisq)
# ## 1
# ## 2  8 96.771  < 2.2e-16 ***
# ## ---
# ## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1