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midasr (version 0.2)
Mixed Data Sampling Regression
Description
Econometric methods for mixed frequency time series data analysis
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Install
install.packages('midasr')
Monthly Downloads
1,237
Version
0.2
License
MIT + file LICENCE
Maintainer
Vaidotas Zemlys
Last Published
January 7th, 2014
Functions in midasr (0.2)
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USrealgdp
US annual gross domestic product in billions of chained 2005 dollars
imidas_r.imidas_r
Restricted MIDAS regression with I(1) regressors
midas_r.fit
Fit restricted MIDAS regression
amweights
Weights for aggregates based MIDAS regressions
mls_coef
Return the coefficients for fmls variables
nakagamip.gradient
Gradient function for normalized Nakagami probability density function MIDAS weights specification Calculate gradient function for normalized Nakagami probability density function specification of MIDAS weights.
hf_lags_table
Create a high frequency lag selection table for MIDAS regression model
get_estimation_sample
Get the data which was used to etimate MIDAS regression
weight_coef
Return the restricted coefficients generated by restriction function(s)
hAh.test
Test restrictions on coefficients of MIDAS regression
hAhr.test
Test restrictions on coefficients of MIDAS regression using robust version of the test
dmls
MIDAS lag structure for unit root processes
simplearma.sim
Simulate AR(1) or MA(1) model
midasr-package
Estimating and testing MIDAS regression
expand_amidas
Create table of weights, lags and starting values for Ghysels weight schema
forecast
Forecast MIDAS regression
gompertzp
Normalized Gompertz probability density function MIDAS weights specification Calculate MIDAS weights according to normalized Gompertz probability density function specification
fmls
Full MIDAS lag structure
midas_coef
Return the coefficients of MIDAS regression
split_data
Split mixed frequency data into in-sample and out-of-sample
midas_r_ic_table
Create a weight and lag selection table for MIDAS regression model
polystep
Step function specification for MIDAS weights
lcauchyp.gradient
Gradient function for normalized log-Cauchy probability density function MIDAS weights specification Calculate gradient function for normalized log-Cauchy probability density function specification of MIDAS weights.
midas.sim
Simulate MIDAS regression response variable
almonp
Almon polynomial MIDAS weights specification
weights_table
Create a weight function selection table for MIDAS regression model
midas.auto.sim
Simulate autoregressive MIDAS model
gompertzp.gradient
Gradient function for normalized Gompertz probability density function MIDAS weights specification Calculate gradient function for normalized Gompertz probability density function specification of MIDAS weights.
lcauchyp
Normalized log-Cauchy probability density function MIDAS weights specification Calculate MIDAS weights according to normalized log-Cauchy probability density function specification
imidas_r
Restricted MIDAS regression with I(1) regressors
+.lws_table
Combine
lws_table
objects
midas_r
Restricted MIDAS regression
mls
MIDAS lag structure
nbeta.gradient
Gradient function for normalized beta probability density function MIDAS weights specification Calculate gradient function for normalized beta probability density function specification of MIDAS weights.
midas_u
Estimate unrestricted MIDAS regression
weight_names
Return the names of restriction function(s)
midas_r_fast
Restricted MIDAS regression
amidas_table
Weight and lag selection table for aggregates based MIDAS regression model
nealmon
Normalized Exponential Almon lag MIDAS coefficients
deviance.midas_r
MIDAS regression model deviance
almonp.gradient
Gradient function for Almon polynomial MIDAS weights
nealmon.gradient
Gradient function for normalized exponential Almon lag weights
USunempr
US monthly unemployment rate
average_forecast
Average forecasts of MIDAS models
polystep.gradient
Gradient of step function specification for MIDAS weights
checkARstar
Check whether the MIDAS model is MIDAS-AR* model
modsel
Select the model based on given information criteria
prepmidas_r
Prepare necessary objects for fitting of the MIDAS regression
expand_weights_lags
Create table of weights, lags and starting values
deriv_tests
Check whether non-linear least squares restricted MIDAS regression problem has converged
lf_lags_table
Create a low frequency lag selection table for MIDAS regression model
check_mixfreq
Check data for MIDAS regression
select_and_forecast
Create table for different forecast horizons
rvsp500
Realized volatility of S&P500 index
prep_hAh
Calculate data for
hAh.test
and
hAhr.test
weight_param
Return the estimated hyper parameters of the weight function(s)
nakagamip
Normalized Nakagami probability density function MIDAS weights specification Calculate MIDAS weights according to normalized Nakagami probability density function specification
nbetaMT.gradient
Gradient function for normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible) Calculate gradient function for normalized beta probability density function specification of MIDAS weights.
agk.test
Andreou, Ghysels, Kourtellos LM test
predict.midas_r
Predict method for MIDAS regression fit
AICc
Compute AICc
midas_r.midas_r
Restricted MIDAS regression
nbeta
Normalized beta probability density function MIDAS weights specification Calculate MIDAS weights according to normalized beta probability density function specification
nbetaMT
Normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible) Calculate MIDAS weights according to normalized beta probability density function specification. Compatible with the specification in MATLAB toolbox.