ecmaveback: Backwards selection using an averaged error correction model

In ecm: Build Error Correction Models

Backwards selection using an averaged error correction model

Description

Much like the ecmback function, ecmaveback uses backwards selection to build an error correction model. However, it uses the averaging method of ecmave to build models and then choose variables based on lowest AIC or BIC, or highest adjusted R-squared.

Usage

ecmaveback(
  y,
  xeq,
  xtr,
  includeIntercept = T,
  criterion = "AIC",
  k,
  method = "boot",
  seed = 5,
  weights = NULL,
  keep = NULL,
  ...
)

Arguments

y	The target variable
xeq	The variables to be used in the equilibrium term of the error correction model
xtr	The variables to be used in the transient term of the error correction model
includeIntercept	Boolean whether the y-intercept should be included
criterion	Whether AIC (default), BIC, or adjustedR2 should be used to select variables
k	The number of models or data partitions desired
method	Whether to split data by folds ("fold"), nested folds ("nestedfold"), or bootstrapping ("boot")
seed	Seed for reproducibility (only needed if method is "boot")
weights	Optional vector of weights to be passed to the fitting process
keep	Optional character vector of variables to forcibly retain
...	Additional arguments to be passed to the 'lm' function (careful in that these may need to be modified for ecm or may not be appropriate!)

Details

When inputting a single variable for xeq or xtr, it is important to input it in the format "xeq=df['col1']" in order to retain the data frame class. Inputting such as "xeq=df[,'col1']" or "xeq=df$col1" will result in errors in the ecm function.

If using weights, the length of weights should be one less than the number of rows in xeq or xtr.

This function only works with the 'lm' linear fitter.

Value

an lm object representing an error correction model using backwards selection

See Also

lm

Examples

##Not run

#Use ecm to predict Wilshire 5000 index based on corporate profits, 
#Federal Reserve funds rate, and unemployment rate
data(Wilshire)

#Use 2015-12-01 and earlier data to build models
trn <- Wilshire[Wilshire$date<='2015-12-01',]

#Use backwards selection to choose which predictors are needed 
xeq <- xtr <- trn[c('CorpProfits', 'FedFundsRate', 'UnempRate')]
modelaveback <- ecmaveback(trn$Wilshire5000, xeq, xtr, k = 5)
print(modelaveback)
#Backwards selection chose CorpProfits and FedFundsRate in the equilibrium term, 
#CorpProfits and UnempRate in the transient term.

modelavebackFFR <- ecmaveback(trn$Wilshire5000, xeq, xtr, k = 5, keep = 'UnempRate')
print(modelavebackFFR)
#Backwards selection was forced to retain UnempRate in both terms.

ecm documentation built on May 29, 2024, 8:12 a.m.