## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)
library(csdm)

## ----load-data----------------------------------------------------------------
data(PWT_60_07, package = "csdm")
head(PWT_60_07, 10)
str(PWT_60_07)

# For computational speed in this vignette, use a subset: 
# first 15 countries, 1970-2007
first_15_ids <- unique(PWT_60_07$id)[1:15]
df <- subset(PWT_60_07, id %in% first_15_ids & year >= 1970 & year <= 2007)

## ----install-package, eval = FALSE--------------------------------------------
# install.packages("csdm")

## ----install-github, eval = FALSE---------------------------------------------
# install.packages("remotes")
# remotes::install_github("Macosso/csdm")

## ----mg-fit-------------------------------------------------------------------
# MG: Separate regression per country, then average coefficients
fit_mg <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "mg"
)

print(fit_mg)
summary(fit_mg)

## ----cce-fit------------------------------------------------------------------
# CCE: Add cross-sectional means to control for common shocks
fit_cce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)

print(fit_cce)
summary(fit_cce)

## ----dcce-fit-----------------------------------------------------------------
# DCCE: Include dynamics and cross-sectional means
# Use lagged dependent variable to capture dynamic adjustment
fit_dcce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "dcce",
  csa = csdm_csa(
    vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), 
    lags = 3
  ),
  lr = csdm_lr(type = "ardl", ylags = 1, xdlags = 0)
)

print(fit_dcce)
summary(fit_dcce)

## ----csardl-fit---------------------------------------------------------------
# CS-ARDL: Separate short-run and long-run dynamics
# Includes lagged dependent and lagged regressors
fit_csardl <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "cs_ardl",
  csa = csdm_csa(
    vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), 
    lags = 3
  ),
  lr = csdm_lr(type = "ardl", ylags = 1, xdlags = 1)
)

print(fit_csardl)
summary(fit_csardl)

## ----cd-tests-----------------------------------------------------------------
# Test MG residuals for CSD
cd_mg <- cd_test(fit_mg, type = "CD")
print(cd_mg)

# Test CCE residuals for CSD
set.seed(1234)
cd_cce <- cd_test(fit_cce, type = "all")
print(cd_cce)

# Test DCCE residuals for CSD
set.seed(1234)
cd_dcce <- cd_test(fit_dcce, type = "CDw")
print(cd_dcce)

# Test CS-ARDL residuals for CSD
set.seed(1234)
cd_csardl <- cd_test(fit_csardl, type = "all")
print(cd_csardl)