# Mixed Data Sampling Regression

## Documentation for package ‘midasr’ version 0.7

- DESCRIPTION file.
- Code demos. Use demo() to run them.
- Package NEWS.

## Help Pages

midasr-package | Mixed Data Sampling Regression
+.lws_table | Combine 'lws_table' objects
agk.test | Andreou, Ghysels, Kourtellos LM test
almonp | Almon polynomial MIDAS weights specification
almonp_gradient | Gradient function for Almon polynomial MIDAS weights
amidas_table | Weight and lag selection table for aggregates based MIDAS regression model
amweights | Weights for aggregates based MIDAS regressions
average_forecast | Average forecasts of MIDAS models
check_mixfreq | Check data for MIDAS regression
coef.midas_nlpr | Extract coefficients of MIDAS regression
coef.midas_r | Extract coefficients of MIDAS regression
coef.midas_sp | Extract coefficients of MIDAS regression
deriv_tests | Check whether non-linear least squares restricted MIDAS regression problem has converged
deriv_tests.midas_r | Check whether non-linear least squares restricted MIDAS regression problem has converged
deviance.midas_nlpr | Non-linear parametric MIDAS regression model deviance
deviance.midas_r | MIDAS regression model deviance
deviance.midas_sp | Semi-parametric MIDAS regression model deviance
dmls | MIDAS lag structure for unit root processes
expand_amidas | Create table of weights, lags and starting values for Ghysels weight schema
expand_weights_lags | Create table of weights, lags and starting values
extract.midas_r | Extract coefficients and GOF measures from MIDAS regression object
fitted.midas_nlpr | Fitted values for non-linear parametric MIDAS regression model
fitted.midas_sp | Fitted values for semi-parametric MIDAS regression model
fmls | Full MIDAS lag structure
forecast | Forecast MIDAS regression
forecast.midas_r | Forecast MIDAS regression
genexp | Generalized exponential MIDAS coefficients
genexp_gradient | Gradient of generalized exponential MIDAS coefficient generating function
get_estimation_sample | Get the data which was used to etimate MIDAS regression
gompertzp | Normalized Gompertz probability density function MIDAS weights specification
gompertzp_gradient | Gradient function for normalized Gompertz probability density function MIDAS weights specification
hAhr_test | Test restrictions on coefficients of MIDAS regression using robust version of the test
hAh_test | Test restrictions on coefficients of MIDAS regression
harstep | HAR(3)-RV model MIDAS weights specification
harstep_gradient | Gradient function for HAR(3)-RV model MIDAS weights specification
hf_lags_table | Create a high frequency lag selection table for MIDAS regression model
imidas_r | Restricted MIDAS regression with I(1) regressors
lcauchyp | Normalized log-Cauchy probability density function MIDAS weights specification
lcauchyp_gradient | Gradient function for normalized log-Cauchy probability density function MIDAS weights specification
lf_lags_table | Create a low frequency lag selection table for MIDAS regression model
lstr | Compute LSTR term for high frequency variable
midasr | Mixed Data Sampling Regression
midas_auto_sim | Simulate simple autoregressive MIDAS model
midas_lstr_plain | LSTR (Logistic Smooth TRansition) MIDAS regression
midas_lstr_sim | Simulate LSTR MIDAS regression model
midas_mmm_plain | MMM (Mean-Min-Max) MIDAS regression
midas_mmm_sim | Simulate MMM MIDAS regression model
midas_nlpr | Non-linear parametric MIDAS regression
midas_nlpr.fit | Fit restricted MIDAS regression
midas_pl_plain | MIDAS Partialy linear non-parametric regression
midas_pl_sim | Simulate PL MIDAS regression model
midas_qr | Restricted MIDAS quantile regression
midas_r | Restricted MIDAS regression
midas_r.fit | Fit restricted MIDAS regression
midas_r_ic_table | Create a weight and lag selection table for MIDAS regression model
midas_r_np | Estimate non-parametric MIDAS regression
midas_r_plain | Restricted MIDAS regression
midas_sim | Simulate simple MIDAS regression response variable
midas_si_plain | MIDAS Single index regression
midas_si_sim | Simulate SI MIDAS regression model
midas_sp | Semi-parametric MIDAS regression
midas_u | Estimate unrestricted MIDAS regression
mls | MIDAS lag structure
mlsd | MIDAS lag structure with dates
mmm | Compute MMM term for high frequency variable
modsel | Select the model based on given information criteria
nakagamip | Normalized Nakagami probability density function MIDAS weights specification
nakagamip_gradient | Gradient function for normalized Nakagami probability density function MIDAS weights specification
nbeta | Normalized beta probability density function MIDAS weights specification
nbetaMT | Normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible)
nbetaMT_gradient | Gradient function for normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible)
nbeta_gradient | Gradient function for normalized beta probability density function MIDAS weights specification
nealmon | Normalized Exponential Almon lag MIDAS coefficients
nealmon_gradient | Gradient function for normalized exponential Almon lag weights
oos_prec | Out-of-sample prediction precision data on simulation example
plot_lstr | Plot MIDAS coefficients
plot_midas_coef | Plot MIDAS coefficients
plot_midas_coef.midas_nlpr | Plot MIDAS coefficients
plot_midas_coef.midas_r | Plot MIDAS coefficients
plot_sp | Plot non-parametric part of the single index MIDAS regression
polystep | Step function specification for MIDAS weights
polystep_gradient | Gradient of step function specification for MIDAS weights
predict.midas_nlpr | Predict method for non-linear parametric MIDAS regression fit
predict.midas_r | Predict method for MIDAS regression fit
predict.midas_sp | Predict method for semi-parametric MIDAS regression fit
prep_hAh | Calculate data for hAh_test and hAhr_test
rvsp500 | Realized volatility of S&P500 index
select_and_forecast | Create table for different forecast horizons
simulate | Simulate MIDAS regression response
simulate.midas_r | Simulate MIDAS regression response
split_data | Split mixed frequency data into in-sample and out-of-sample
update_weights | Updates weights in MIDAS regression formula
UScpiqs | US quartely seasonaly adjusted consumer price index
USeffrw | US weekly effective federal funds rate.
USpayems | United States total employment non-farms payroll, monthly, seasonally adjusted.
USqgdp | United States gross domestic product, quarterly, seasonaly adjusted annual rate.
USrealgdp | US annual gross domestic product in billions of chained 2005 dollars
USunempr | US monthly unemployment rate
weights_table | Create a weight function selection table for MIDAS regression model