| hpiModel.hed {hpiR} | R Documentation |
Specific method for hpi modeling (hed) approach)
Description
Estimate hpi models with hed approach
Usage
## S3 method for class 'hed'
hpiModel(model_type, hpi_df, estimator = "base",
log_dep = TRUE, trim_model = TRUE, mod_spec = NULL,
dep_var = NULL, ind_var = NULL, ...)
Arguments
model_type |
Type of model to estimate ('rt', 'hed', 'rf') |
hpi_df |
Dataset created by one of the *CreateSales() function in this package. |
estimator |
Type of estimator to be used ('base', 'weighted', 'robust') |
log_dep |
default=TRUE; should the dependent variable (change in price) be logged? |
trim_model |
default TRUE, should excess be trimmed from model results ('lm' or 'rlm' object)? |
mod_spec |
default=NULL; hedonic model specification |
dep_var |
default=NULL; dependent variable of the model |
ind_var |
default=NULL; independent variable(s) of the model |
... |
Additional Arguments |
Value
hpimodel object consisting of:
- estimator
Type of estimator
- coefficients
Data.frame of coefficient
- model_obj
class 'rtmodel' or 'hedmodel'
- mod_spec
Full model specification
- log_dep
Binary: is the dependent variable in logged format
- base_price
Mean price in the base period
- periods
'data.frame' of periods
- approach
Type of model used