In FedericoGarza/lineartestr: Linear Specification Testing

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "70%"
)

lineartestr

The goal of lineartestr is to contrast the linear hypothesis of a model:

formula

Using the Domínguez-Lobato test which relies on wild-bootstrap. Also the Ramsey RESET test is implemented.

Installation

You can install the released version of lineartestr from CRAN with:

install.packages("lineartestr")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("FedericoGarza/lineartestr")

References

Manuel A. Domínguez and Ignacio N. Lobato (2019). Specification testing with estimated variables. Econometric Reviews.

Cite as

Garza F (2020). lineartestr: Test the linear specification of a model. R package version 1.0.0, https://github.com/FedericoGarza/lineartestr.

Examples

Simplest linear models using `lm` function

library(lineartestr)

x <- 1:100
y <- 1:100

lm_model <- lm(y~x)

dl_test <- dominguez_lobato_test(lm_model)

dplyr::glimpse(dl_test$test)

Also lineartestr can plot the results

plot_dl_test(dl_test)

Run in parallel!

library(lineartestr)
x_p <- 1:1e5
y_p <- 1:1e5

lm_model_p <- lm(y_p~x_p)

dl_test_p <- dominguez_lobato_test(lm_model_p, n_cores=7)

dplyr::glimpse(dl_test_p$test)

RESET test can also be used to test the linear hypothesis

library(lineartestr)

x <- 1:100 + rnorm(100)
y <- 1:100

lm_model <- lm(y~x)

r_test <- reset_test(lm_model)

dplyr::glimpse(r_test)

An then we can plot the results

plot_reset_test(r_test)

Linear fixed effects with `lfe`

library(lineartestr)
library(dplyr)
library(lfe)

# This example was taken from https://www.rdocumentation.org/packages/lfe/versions/2.8-5/topics/felm
x <- rnorm(1000)
x2 <- rnorm(length(x))
# Individuals and firms
id <- factor(sample(20,length(x),replace=TRUE))
firm <- factor(sample(13,length(x),replace=TRUE))
# Effects for them
id.eff <- rnorm(nlevels(id))
firm.eff <- rnorm(nlevels(firm))
# Left hand side
u <- rnorm(length(x))
y <- x + 0.5*x2 + id.eff[id] + firm.eff[firm] + u
new_y <- y + rnorm(length(y))
## Estimate the model
est <- lfe::felm(y ~ x + x2 | id + firm)


## Testing the linear hypothesis and plotting results
dominguez_lobato_test(est, n_cores = 7) %>%
  plot_dl_test()

ARMA models

library(lineartestr)
library(dplyr)

x <- rnorm(100)**3

arma_model <- forecast::Arima(x, order = c(1, 0, 1))

dominguez_lobato_test(arma_model) %>%
  plot_dl_test()