broom (version 0.4.1)

gmm_tidiers: Tidying methods for generalized method of moments "gmm" objects

Description

These methods tidy the coefficients of "gmm" objects from the gmm package, or glance at the model-wide statistics (especially the J-test).

Usage

"tidy"(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...)
"glance"(x, ...)

Arguments

x

gmm object

conf.int

whether to include a confidence interval

conf.level

confidence level of the interval, used only if conf.int=TRUE

exponentiate

whether to exponentiate the coefficient estimates and confidence intervals (typical for logistic regression)

quick

whether to compute a smaller and faster version, containing only the term and estimate columns (and confidence interval if requested, which may be slower)

...

extra arguments (not used)

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.tidy.gmm returns one row for each coefficient, with six columns:

term

The term in the model being estimated

estimate

The estimated coefficient

std.error

The standard error from the linear model

statistic

t-statistic

p.value

two-sided p-value

If all the the terms have _ in them (e.g. WMK_(Intercept)), they are split into variable and term.If conf.int=TRUE, it also includes columns for conf.low and conf.high, computed with confint.glance.gmm returns a one-row data.frame with the columns

df

Degrees of freedom

statistic

Statistic from J-test for E(g)=0

p.value

P-value from J-test

df.residual

Residual degrees of freedom, if included in "gmm" object

Details

If conf.int=TRUE, the confidence interval is computed with the confint function.

Note that though the "gmm" object contains residuals and fitted values, there is not yet an augment method implemented. This is because the input to gmm is not tidy (it's a "wide" matrix), so it is not immediately clear what the augmented results should look like.

Examples

if (require("gmm", quietly = TRUE)) {
  # examples come from the "gmm" package
  ## CAPM test with GMM
  data(Finance)
  r <- Finance[1:300, 1:10]
  rm <- Finance[1:300, "rm"]
  rf <- Finance[1:300, "rf"]
  
  z <- as.matrix(r-rf)
  t <- nrow(z)
  zm <- rm-rf
  h <- matrix(zm, t, 1)
  res <- gmm(z ~ zm, x = h)
  
  # tidy result
  tidy(res)
  tidy(res, conf.int = TRUE)
  tidy(res, conf.int = TRUE, conf.level = .99)
  
  # coefficient plot
  library(ggplot2)
  library(dplyr)
  tidy(res, conf.int = TRUE) %>%
    mutate(variable = reorder(variable, estimate)) %>%
    ggplot(aes(estimate, variable)) +
    geom_point() +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
    facet_wrap(~ term) +
    geom_vline(xintercept = 0, color = "red", lty = 2)
  
  # from a function instead of a matrix
  g <- function(theta, x) {
  	e <- x[,2:11] - theta[1] - (x[,1] - theta[1]) %*% matrix(theta[2:11], 1, 10)
  	gmat <- cbind(e, e*c(x[,1]))
  	return(gmat) }
  
  x <- as.matrix(cbind(rm, r))
  res_black <- gmm(g, x = x, t0 = rep(0, 11))
  
  tidy(res_black)
  tidy(res_black, conf.int = TRUE)
  
  ## APT test with Fama-French factors and GMM
  
  f1 <- zm
  f2 <- Finance[1:300, "hml"] - rf
  f3 <- Finance[1:300, "smb"] - rf
  h <- cbind(f1, f2, f3)
  res2 <- gmm(z ~ f1 + f2 + f3, x = h)
  
  td2 <- tidy(res2, conf.int = TRUE)
  td2
  
  # coefficient plot
  td2 %>%
    mutate(variable = reorder(variable, estimate)) %>%
    ggplot(aes(estimate, variable)) +
    geom_point() +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
    facet_wrap(~ term) +
    geom_vline(xintercept = 0, color = "red", lty = 2)
}

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