inst/tests/arguments_for_StepByStepFunctionChecksMigdata.R
In markusfritsch/pdynmc: Moment Condition Based Estimation of Linear Dynamic Panel Data Models
rm(list = ls())
# install.packages("pdynmc")
library(pdynmc)
setwd("D:/Work/20_Projekte/450_Migration/migFlowsAndDrivers/data")
datInEU <- readRDS(file = "datInEUwCov.rds")
datOutEU <- readRDS(file = "datOutEUwCov.rds")
nrow(datInEU)
nrow(datOutEU)
table(datOutEU$orig)
table(datOutEU$year0)
summary(datOutEU$flow)
#number of observations if there is migration from country of origin i
# to destination j in any time period within Europe
length(unique(datInEU$dest)) * length(unique(datInEU$year0))
sum(table(datInEU$orig) == length(unique(datInEU$dest)) * length(unique(datInEU$year0)))
which(table(datInEU$orig) == length(unique(datInEU$dest)) * length(unique(datInEU$year0)))
#number of observations if there is migration from country of origin i
# to destination j in any time period from outside of Europe
length(unique(datOutEU$dest)) * length(unique(datOutEU$year0))
sum(table(datOutEU$orig) == length(unique(datOutEU$dest)) * length(unique(datOutEU$year0)))
which(table(datOutEU$orig) == length(unique(datOutEU$dest)) * length(unique(datOutEU$year0)))
#balance panel datasets (only sensible for migration from outside of Europe)
migPanel <- datOutEU[datOutEU$orig %in% names(which(table(datOutEU$orig) == length(unique(datOutEU$dest)) * length(unique(datOutEU$year0)))), ]
head(migPanel, 20)
#relevant columns:
#year0: time horizon; for example 1960 means 1960-1969 (t)
#orig: country of origin (i)
#dest: destination (j)
#flow: migration flow estimate
#UnempRate_orig: Unemployment (in %) in country of origin
#GDPdomCurr_orig: GDP (domestic currency) in country of origin
#ExchRateDomCurrPerUSD: Exchange rate (domestic currency per USD)
#CPI_orig: Consumer price index in country of origin
#IndProdIdx_orig: Industrial production index in country of origin
#Pop_orig: Population (in persons) in country of origin
#UnempRate_orig: Unemployment rate (in %) in country of origin
#GDPdomCurr_dest: GDP (domestic currency) in country of destination
#CPI_dest: Consumer price index in country of destination
#IndProdIdx_dest: Industrial production index in country of destination
#Pop_dest: Population (in persons) in country of destination
###############################################
### Extend data set
###############################################
dat <- migPanel
###
### Create contintent (of origin) variable
###
# mostly for plotting purposes
# we use a seven continent scheme (and omit continent antarctica)
dat$continent <- NA
dat$continent[dat$orig %in% c("CMR", "EGY", "MAR", "MUS", "RWA")] <- "AFR" # Africa
dat$continent[dat$orig %in% c("CAN", "USA")] <- "AMN" # North America
dat$continent[dat$orig %in% c(
"ARG", "BOL", "BRA", "CHL", "COL", "CUB", "DOM", "GTM",
"HND", "JAM", "MEX", "NIC", "PAN", "PER", "URY", "VEN"
)] <- "AMS" # South and Central America
dat$continent[dat$orig %in% c("IND", "IRQ", "LBN", "PHL", "QAT", "SYR", "TUR")] <- "ASI" # Asia
dat$continent[dat$orig %in% "FJI"] <- "AUS" # Australia
dat$continent[dat$orig %in% c("CHE", "GBR", "SLV")] <- "EUR" # Europe
table(dat$continent, dat$orig)
###
### Add log(flow) to data set
###
dat$lflow <- log(dat$flow)
###############################################
### Add Penn World Table information
###############################################
###
### Load data
###
# install.packages("pwt10")
library(pwt10)
# ?pwt10.01
data(pwt10.01)
pwt <- pwt10.01
# dim(pwt) # 12810 = 183 countries * 70 years (1950 to 2019)
# head(pwt)
###
### Add decade information and isocode-decade-combination
###
pwt$decade <- floor(pwt$year*0.1)*10
pwt$couDecade <- paste(as.character(pwt$isocode), pwt$decade, sep = "-")
###
### Missing values (general analysis)
###
count.NA <- function(vec){sum(is.na(vec))}
apply(X = pwt, MARGIN = 2, FUN = count.NA)
###
### Average variables over decades
###
### Determine variables to be averaged
pwt.columns <- data.frame(
col.id = 1:ncol(pwt),
col.name = colnames(pwt),
col.class = NA
)
for(co in 1:ncol(pwt)){
pwt.columns$col.class[co] <- class(pwt[, co])
}
vars.to.avg <- pwt.columns$col.id[
pwt.columns$col.class == "numeric" &
pwt.columns$col.name != "decade"
]
### Average per decade
pwt.avg <- pwt[pwt$year == pwt$decade, c("country", "isocode", "year")]
for(co in vars.to.avg){
pwt.avg[, colnames(pwt)[co]] <- tapply(X = pwt[, co], INDEX = pwt$couDecade, FUN = mean, na.rm = TRUE)
}
apply(X = pwt.avg, MARGIN = 2, FUN = count.NA)
apply(X = pwt.avg[pwt.avg$year > 1950 & pwt.avg$year < 2010, ], MARGIN = 2, FUN = count.NA)
### long to wide for analysis of single variables
library(tidyr)
var.temp <- "rgdpna"
# var.temp <- "emp"
pwt.wide <- spread(pwt.avg[, c("country", "isocode", "year", var.temp)], year, var.temp)
pwt.wide
###
### Merge data sets "dat" and "pwt.avg"
###
### create isocode-year-combination
dat$orig_year <- paste(dat$orig, dat$year0, sep = "-")
dat$dest_year <- paste(dat$dest, dat$year0, sep = "-")
pwt.avg$iso_year <- paste(pwt.avg$isocode, pwt.avg$year, sep = "-")
### Merge variables (in var.set) from pwt.avg to dat
var.set <- c("pop" #population
, "emp" #employment
, "rgdpna" #real GDP (USD; 2017 prices)
, "xr" #exchange rate (national currency per USD)
, "csh_c" #household share of consumption (in percent)
, "pl_c" #price level of household consumption (price level of USA in 2017 is 1)
) # !!! select variable names from pwt data
# var.temp <- "pop"
for(var.temp in var.set){
dat <- merge(
x = dat,
y = pwt.avg[, c("iso_year", var.temp)],
by.x = "orig_year",
by.y = "iso_year",
all.x = TRUE,
all.y = FALSE
)
dat <- merge(
x = dat,
y = pwt.avg[, c("iso_year", var.temp)],
by.x = "dest_year",
by.y = "iso_year",
all.x = TRUE,
all.y = FALSE,
suffixes = c("_orig", "_dest")
)
dat <- dat[order(dat$year0, dat$dest, dat$orig, decreasing = FALSE), ]
}
#real GDP per capita
dat$rgdpnaPC_orig <- dat$rgdpna_orig/dat$pop_orig
dat$rgdpnaPC_dest <- dat$rgdpna_dest/dat$pop_dest
#real GDP per capita that ends up with household
dat$rgdpnaHPC_orig <- (dat$rgdpna_orig/dat$pop_orig)*dat$csh_c_orig
dat$rgdpnaHPC_dest <- (dat$rgdpna_dest/dat$pop_dest)*dat$csh_c_dest
dat$rgdpnaHPC <- as.numeric((dat$rgdpnaHPC_dest-dat$rgdpnaHPC_orig)/dat$rgdpnaHPC_orig)
#price level of household consumption
dat$pl_c <- as.numeric((dat$pl_c_dest-dat$pl_c_orig)*dat$pl_c_orig)
#combine origin and destination information to variable s = (i,j),
# where i indicates the country of origin and j the destination
dat$orig_dest <- paste(dat$orig, "_", dat$dest, sep = "")
head(dat)
### Comparison of population variables
plot(dat$Pop_orig, dat$pop_orig)
abline(c(0, 1/10^6))
plot(dat$Pop_dest, dat$pop_dest)
abline(c(0, 1/10^6))
#countries with missing values in the variables
unique(dat[is.na(dat$rgdpnaHPC + dat$pl_c + dat$lflow), "orig"])
#remove countries with missing values in the variables
dat <- dat[!(dat$orig %in% unique(dat[is.na(dat$rgdpnaHPC + dat$pl_c + dat$lflow), "orig"])), ]
dat = dat
varname.i = "orig_dest"
varname.t = "year0"
use.mc.diff = TRUE
use.mc.lev = FALSE
use.mc.nonlin = FALSE
use.mc.nonlinAS = NULL
inst.stata = FALSE
include.y = TRUE
varname.y = "lflow"
lagTerms.y = 1
maxLags.y = NULL
include.x = TRUE
varname.reg.end = c("rgdpnaHPC","pl_c")
lagTerms.reg.end = c(0,0)
maxLags.reg.end = NULL
varname.reg.pre = NULL
lagTerms.reg.pre = NULL
maxLags.reg.pre = NULL
varname.reg.ex = NULL
lagTerms.reg.ex = NULL
maxLags.reg.ex = NULL
include.x.instr = FALSE
varname.reg.instr = NULL
inst.reg.ex.expand = TRUE
include.x.toInstr = FALSE
varname.reg.toInstr = NULL
fur.con = FALSE
fur.con.diff = NULL
fur.con.lev = NULL
varname.reg.fur = NULL
lagTerms.reg.fur = NULL
include.dum = TRUE
#custom.dum = TRUE
dum.diff = FALSE
dum.lev = TRUE
varname.dum = "year0"
col_tol = 0.65
w.mat = "iid.err"
w.mat.stata = FALSE
std.err = "corrected"
estimation = "onestep"
max.iter = 4
iter.tol = 0.01
inst.thresh = NULL
opt.meth = "none"
hessian = FALSE
optCtrl = list(kkt = FALSE, kkttol = .Machine$double.eps^(1/3), kkt2tol =
.Machine$double.eps^(1/3), starttests = TRUE, dowarn = TRUE, badval = (0.25) *
.Machine$double.xmax, usenumDeriv = FALSE, reltol = 1e-12, maxit = 200, trace = TRUE,
follow.on = FALSE, save.failures = TRUE, maximize = FALSE, factr = 1e+07, pgtol = 0,
all.methods = FALSE)
custom.start.val = FALSE
start.val = NULL
start.val.lo = -1
start.val.up = 1
seed.input = 2
markusfritsch/pdynmc documentation built on March 5, 2025, 10:14 a.m.
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rm(list = ls())
# install.packages("pdynmc")
library(pdynmc)
setwd("D:/Work/20_Projekte/450_Migration/migFlowsAndDrivers/data")
datInEU <- readRDS(file = "datInEUwCov.rds")
datOutEU <- readRDS(file = "datOutEUwCov.rds")
nrow(datInEU)
nrow(datOutEU)
table(datOutEU$orig)
table(datOutEU$year0)
summary(datOutEU$flow)
#number of observations if there is migration from country of origin i
# to destination j in any time period within Europe
length(unique(datInEU$dest)) * length(unique(datInEU$year0))
sum(table(datInEU$orig) == length(unique(datInEU$dest)) * length(unique(datInEU$year0)))
which(table(datInEU$orig) == length(unique(datInEU$dest)) * length(unique(datInEU$year0)))
#number of observations if there is migration from country of origin i
# to destination j in any time period from outside of Europe
length(unique(datOutEU$dest)) * length(unique(datOutEU$year0))
sum(table(datOutEU$orig) == length(unique(datOutEU$dest)) * length(unique(datOutEU$year0)))
which(table(datOutEU$orig) == length(unique(datOutEU$dest)) * length(unique(datOutEU$year0)))
#balance panel datasets (only sensible for migration from outside of Europe)
migPanel <- datOutEU[datOutEU$orig %in% names(which(table(datOutEU$orig) == length(unique(datOutEU$dest)) * length(unique(datOutEU$year0)))), ]
head(migPanel, 20)
#relevant columns:
#year0: time horizon; for example 1960 means 1960-1969 (t)
#orig: country of origin (i)
#dest: destination (j)
#flow: migration flow estimate
#UnempRate_orig: Unemployment (in %) in country of origin
#GDPdomCurr_orig: GDP (domestic currency) in country of origin
#ExchRateDomCurrPerUSD: Exchange rate (domestic currency per USD)
#CPI_orig: Consumer price index in country of origin
#IndProdIdx_orig: Industrial production index in country of origin
#Pop_orig: Population (in persons) in country of origin
#UnempRate_orig: Unemployment rate (in %) in country of origin
#GDPdomCurr_dest: GDP (domestic currency) in country of destination
#CPI_dest: Consumer price index in country of destination
#IndProdIdx_dest: Industrial production index in country of destination
#Pop_dest: Population (in persons) in country of destination
###############################################
### Extend data set
###############################################
dat <- migPanel
###
### Create contintent (of origin) variable
###
# mostly for plotting purposes
# we use a seven continent scheme (and omit continent antarctica)
dat$continent <- NA
dat$continent[dat$orig %in% c("CMR", "EGY", "MAR", "MUS", "RWA")] <- "AFR" # Africa
dat$continent[dat$orig %in% c("CAN", "USA")] <- "AMN" # North America
dat$continent[dat$orig %in% c(
"ARG", "BOL", "BRA", "CHL", "COL", "CUB", "DOM", "GTM",
"HND", "JAM", "MEX", "NIC", "PAN", "PER", "URY", "VEN"
)] <- "AMS" # South and Central America
dat$continent[dat$orig %in% c("IND", "IRQ", "LBN", "PHL", "QAT", "SYR", "TUR")] <- "ASI" # Asia
dat$continent[dat$orig %in% "FJI"] <- "AUS" # Australia
dat$continent[dat$orig %in% c("CHE", "GBR", "SLV")] <- "EUR" # Europe
table(dat$continent, dat$orig)
###
### Add log(flow) to data set
###
dat$lflow <- log(dat$flow)
###############################################
### Add Penn World Table information
###############################################
###
### Load data
###
# install.packages("pwt10")
library(pwt10)
# ?pwt10.01
data(pwt10.01)
pwt <- pwt10.01
# dim(pwt) # 12810 = 183 countries * 70 years (1950 to 2019)
# head(pwt)
###
### Add decade information and isocode-decade-combination
###
pwt$decade <- floor(pwt$year*0.1)*10
pwt$couDecade <- paste(as.character(pwt$isocode), pwt$decade, sep = "-")
###
### Missing values (general analysis)
###
count.NA <- function(vec){sum(is.na(vec))}
apply(X = pwt, MARGIN = 2, FUN = count.NA)
###
### Average variables over decades
###
### Determine variables to be averaged
pwt.columns <- data.frame(
col.id = 1:ncol(pwt),
col.name = colnames(pwt),
col.class = NA
)
for(co in 1:ncol(pwt)){
pwt.columns$col.class[co] <- class(pwt[, co])
}
vars.to.avg <- pwt.columns$col.id[
pwt.columns$col.class == "numeric" &
pwt.columns$col.name != "decade"
]
### Average per decade
pwt.avg <- pwt[pwt$year == pwt$decade, c("country", "isocode", "year")]
for(co in vars.to.avg){
pwt.avg[, colnames(pwt)[co]] <- tapply(X = pwt[, co], INDEX = pwt$couDecade, FUN = mean, na.rm = TRUE)
}
apply(X = pwt.avg, MARGIN = 2, FUN = count.NA)
apply(X = pwt.avg[pwt.avg$year > 1950 & pwt.avg$year < 2010, ], MARGIN = 2, FUN = count.NA)
### long to wide for analysis of single variables
library(tidyr)
var.temp <- "rgdpna"
# var.temp <- "emp"
pwt.wide <- spread(pwt.avg[, c("country", "isocode", "year", var.temp)], year, var.temp)
pwt.wide
###
### Merge data sets "dat" and "pwt.avg"
###
### create isocode-year-combination
dat$orig_year <- paste(dat$orig, dat$year0, sep = "-")
dat$dest_year <- paste(dat$dest, dat$year0, sep = "-")
pwt.avg$iso_year <- paste(pwt.avg$isocode, pwt.avg$year, sep = "-")
### Merge variables (in var.set) from pwt.avg to dat
var.set <- c("pop" #population
, "emp" #employment
, "rgdpna" #real GDP (USD; 2017 prices)
, "xr" #exchange rate (national currency per USD)
, "csh_c" #household share of consumption (in percent)
, "pl_c" #price level of household consumption (price level of USA in 2017 is 1)
) # !!! select variable names from pwt data
# var.temp <- "pop"
for(var.temp in var.set){
dat <- merge(
x = dat,
y = pwt.avg[, c("iso_year", var.temp)],
by.x = "orig_year",
by.y = "iso_year",
all.x = TRUE,
all.y = FALSE
)
dat <- merge(
x = dat,
y = pwt.avg[, c("iso_year", var.temp)],
by.x = "dest_year",
by.y = "iso_year",
all.x = TRUE,
all.y = FALSE,
suffixes = c("_orig", "_dest")
)
dat <- dat[order(dat$year0, dat$dest, dat$orig, decreasing = FALSE), ]
}
#real GDP per capita
dat$rgdpnaPC_orig <- dat$rgdpna_orig/dat$pop_orig
dat$rgdpnaPC_dest <- dat$rgdpna_dest/dat$pop_dest
#real GDP per capita that ends up with household
dat$rgdpnaHPC_orig <- (dat$rgdpna_orig/dat$pop_orig)*dat$csh_c_orig
dat$rgdpnaHPC_dest <- (dat$rgdpna_dest/dat$pop_dest)*dat$csh_c_dest
dat$rgdpnaHPC <- as.numeric((dat$rgdpnaHPC_dest-dat$rgdpnaHPC_orig)/dat$rgdpnaHPC_orig)
#price level of household consumption
dat$pl_c <- as.numeric((dat$pl_c_dest-dat$pl_c_orig)*dat$pl_c_orig)
#combine origin and destination information to variable s = (i,j),
# where i indicates the country of origin and j the destination
dat$orig_dest <- paste(dat$orig, "_", dat$dest, sep = "")
head(dat)
### Comparison of population variables
plot(dat$Pop_orig, dat$pop_orig)
abline(c(0, 1/10^6))
plot(dat$Pop_dest, dat$pop_dest)
abline(c(0, 1/10^6))
#countries with missing values in the variables
unique(dat[is.na(dat$rgdpnaHPC + dat$pl_c + dat$lflow), "orig"])
#remove countries with missing values in the variables
dat <- dat[!(dat$orig %in% unique(dat[is.na(dat$rgdpnaHPC + dat$pl_c + dat$lflow), "orig"])), ]
dat = dat
varname.i = "orig_dest"
varname.t = "year0"
use.mc.diff = TRUE
use.mc.lev = FALSE
use.mc.nonlin = FALSE
use.mc.nonlinAS = NULL
inst.stata = FALSE
include.y = TRUE
varname.y = "lflow"
lagTerms.y = 1
maxLags.y = NULL
include.x = TRUE
varname.reg.end = c("rgdpnaHPC","pl_c")
lagTerms.reg.end = c(0,0)
maxLags.reg.end = NULL
varname.reg.pre = NULL
lagTerms.reg.pre = NULL
maxLags.reg.pre = NULL
varname.reg.ex = NULL
lagTerms.reg.ex = NULL
maxLags.reg.ex = NULL
include.x.instr = FALSE
varname.reg.instr = NULL
inst.reg.ex.expand = TRUE
include.x.toInstr = FALSE
varname.reg.toInstr = NULL
fur.con = FALSE
fur.con.diff = NULL
fur.con.lev = NULL
varname.reg.fur = NULL
lagTerms.reg.fur = NULL
include.dum = TRUE
#custom.dum = TRUE
dum.diff = FALSE
dum.lev = TRUE
varname.dum = "year0"
col_tol = 0.65
w.mat = "iid.err"
w.mat.stata = FALSE
std.err = "corrected"
estimation = "onestep"
max.iter = 4
iter.tol = 0.01
inst.thresh = NULL
opt.meth = "none"
hessian = FALSE
optCtrl = list(kkt = FALSE, kkttol = .Machine$double.eps^(1/3), kkt2tol =
.Machine$double.eps^(1/3), starttests = TRUE, dowarn = TRUE, badval = (0.25) *
.Machine$double.xmax, usenumDeriv = FALSE, reltol = 1e-12, maxit = 200, trace = TRUE,
follow.on = FALSE, save.failures = TRUE, maximize = FALSE, factr = 1e+07, pgtol = 0,
all.methods = FALSE)
custom.start.val = FALSE
start.val = NULL
start.val.lo = -1
start.val.up = 1
seed.input = 2
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