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| 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_r | 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_r | 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 |
| fmls | Full MIDAS lag structure |
| forecast | Forecast MIDAS regression |
| forecast.midas_r | Forecast MIDAS regression |
| genexp | Generalized exponential MIDAS coefficients |
| genexp_gradient | Gradient of feneralized 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 Calculate MIDAS weights according to normalized Gompertz probability density function specification |
| 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. |
| 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 Calculate MIDAS weights according to normalized log-Cauchy probability density function specification |
| 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. |
| lf_lags_table | Create a low frequency lag selection table for MIDAS regression model |
| midasr | Mixed Data Sampling Regression |
| midas_auto_sim | Simulate simple autoregressive MIDAS model |
| 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_simple | Restricted MIDAS regression |
| midas_sim | Simulate simple MIDAS regression response variable |
| midas_u | Estimate unrestricted MIDAS regression |
| mls | MIDAS lag structure |
| modsel | Select the model based on given information criteria |
| nakagamip | Normalized Nakagami probability density function MIDAS weights specification Calculate MIDAS weights according to normalized Nakagami probability density function specification |
| 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. |
| 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. |
| 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. |
| 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. |
| 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_midas_coef | Plot MIDAS coefficients |
| polystep | Step function specification for MIDAS weights |
| polystep_gradient | Gradient of step function specification for MIDAS weights |
| predict.midas_r | Predict method for 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 |
| 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 |