midas_r_np: Estimate non-parametric MIDAS regression

In midasr: Mixed Data Sampling Regression

Description

Estimates non-parametric MIDAS regression

Usage

midas_r_np(formula, data, lambda = NULL)

Arguments

formula	formula specifying MIDAS regression
data	a named list containing data with mixed frequencies
lambda	smoothing parameter, defaults to NULL, which means that it is chosen by minimising AIC.

Details

Estimates non-parametric MIDAS regression accodring Breitung et al.

Value

a midas_r_np object

Author(s)

Vaidotas Zemlys

References

Breitung J, Roling C, Elengikal S (2013). Forecasting inflation rates using daily data: A nonparametric MIDAS approach Working paper, URL http://www.ect.uni-bonn.de/mitarbeiter/joerg-breitung/npmidas.

Examples

data("USunempr")
data("USrealgdp")
y <- diff(log(USrealgdp))
x <- window(diff(USunempr),start=1949)
trend <- 1:length(y)
midas_r_np(y~trend+fmls(x,12,12))

Example output

Loading required package: sandwich
Loading required package: optimx
Nonparametric MIDAS regression model (62 low frequency observations)
Formula:  y ~ trend + fmls(x, 12, 12)
The smoothing parameter:  0.1561959
The effective number of parameters: 5.93671
AIC of the model:  -4.918592
Root mean squared error:  0.009550417 

midasr documentation built on Feb. 23, 2021, 5:11 p.m.