Description Usage Arguments References Examples
ivx_ar implements the Yang et al (2020) new instrumental variable based Wald statistic (IVXAR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  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 class 'ivx_ar'
print(x, digits = max(3L, getOption("digits")  3L), ...)

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

horizon 
is the horizon (default horizon = 1 corresponds to a shorthorizon regression). 
ar 
Method to include the autoregressive terms. "auto" find the optimal
ar order by using the information criteria. 
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 
contrasts 
an optional list. See the 
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 
x 
an object of class "ivx_ar", usually, a result of a call to ivx_ar. 
y 
logical. If 
... 
additional arguments to be passed to the low level regression fitting functions (see lm). 
digits 
the number of significant digits to use when printing. 
Yang, B., Long, W., Peng, L., & Cai, Z. (2020). Testing the Predictability of US Housing Price Index Returns Based on an IVXAR Model. Journal of the American Statistical Association, 122. DOI: doi: 10.1080/01621459.2019.1686392
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