Fitting IVX-AR Models — ivx

ivx_ar implements the Yang et al (2020) new instrumental variable based Wald statistic (IVX-AR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model.

ivx_ar(
  formula,
  data,
  horizon,
  ar = "auto",
  ar_ic = c("bic", "aic", "aicc"),
  ar_max = 5,
  ar_grid = function(x) seq(x - 0.3, x + 0.3, by = 0.02),
  na.action,
  contrasts = NULL,
  offset,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  ...
)

# S3 method for ivx_ar
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	n optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
horizon	is the horizon (default horizon = 1 corresponds to a short-horizon regression).
ar	Method to include the autoregressive terms. "auto" find the optimal ar order by using the information criteria. `ar = 0` reduces to simple `ivx`. `ar > 1` uses a fixed order to estimate the model.
ar_ic	Information criterion to be used in model selection.
ar_max	Maximum ar order of model to fit.
ar_grid	The ar grid sequence of which to iterate.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of `options`, and is `na.fail` if that is unset. The ‘factory-fresh’ default is `na.omit`. Another possible value is `NULL`, no action. Value `na.exclude` can be useful.
contrasts	an optional list. See the `contrasts.arg` of `model.matrix.default`.
offset	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response. One or more offset terms can be included in the formula instead or as well, and if more than one are specified their sum is used. See model.offset
model	logical. If `TRUE` the model.frame of the fit is returned.
x	an object of class "ivx_ar", usually, a result of a call to ivx_ar.
y	logical. If `TRUE` the response of the fit is returned.
...	additional arguments to be passed to the low level regression fitting functions (see lm).
digits	the number of significant digits to use when printing.

References

Yang, B., Long, W., Peng, L., & Cai, Z. (2020). Testing the Predictability of US Housing Price Index Returns Based on an IVX-AR Model. Journal of the American Statistical Association, 1-22. DOI: doi: 10.1080/01621459.2019.1686392

Examples


ivx_ar(hpi ~ log(res) + cpi, ylpc)
#> 
#> Call:
#> ivx_ar(formula = hpi ~ log(res) + cpi, data = ylpc, horizon = 1)
#> 
#> Lag Selection:
#> Auto (bic) with AR terms q = 4
#> 
#> Coefficients:
#>   log(res)         cpi  
#>  0.0018317  -0.0002078  
#> 

ivx_ar(hpi ~ log(res) + cpi, ylpc, ar_ic = "aic")
#> 
#> Call:
#> ivx_ar(formula = hpi ~ log(res) + cpi, data = ylpc, ar_ic = "aic", 
#>     horizon = 1)
#> 
#> Lag Selection:
#> Auto (aic) with AR terms q = 4
#> 
#> Coefficients:
#>   log(res)         cpi  
#>  0.0018317  -0.0002078  
#> 

ivx_ar(hpi ~ log(res) + cpi, ylpc, ar = 1)
#> 
#> Call:
#> ivx_ar(formula = hpi ~ log(res) + cpi, data = ylpc, ar = 1, horizon = 1)
#> 
#> Lag Selection:
#> Fixed with AR terms q = 1
#> 
#> Coefficients:
#>   log(res)         cpi  
#>  0.0010780  -0.0001155  
#>