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| midasr-package | Estimating and testing MIDAS regression |
| +.lws_table | Combine 'lws_table' objects |
| agk.test | Andreou, Ghysels, Kourtellos LM test |
| AICc | Compute AICc |
| 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 |
| checkARstar | Check whether the MIDAS model is MIDAS-AR* model |
| check_mixfreq | Check data for 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 |
| 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. |
| hAh.test | Test restrictions on coefficients of MIDAS regression |
| hAhr.test | Test restrictions on coefficients of MIDAS regression using robust version of the test |
| hf_lags_table | Create a high frequency lag selection table for MIDAS regression model |
| imidas_r | Restricted MIDAS regression with I(1) regressors |
| imidas_r.default | Restricted MIDAS regression with I(1) regressors |
| imidas_r.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 |
| midas.auto.sim | Simulate autoregressive MIDAS model |
| midas.sim | Simulate MIDAS regression response variable |
| midasr | Estimating and testing MIDAS regression |
| midas_coef | Return the coefficients of MIDAS regression |
| midas_r | Restricted MIDAS regression |
| midas_r.default | Restricted MIDAS regression |
| midas_r.fit | Fit restricted MIDAS regression |
| midas_r.midas_r | Restricted MIDAS regression |
| midas_r_fast | Restricted MIDAS regression |
| midas_r_ic_table | Create a weight and lag selection table for MIDAS regression model |
| midas_r_ic_table.default | Create a weight and lag selection table for MIDAS regression model |
| midas_u | Estimate unrestricted MIDAS regression |
| mls | MIDAS lag structure |
| mls_coef | Return the coefficients for fmls variables |
| 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 |
| 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. |
| 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. |
| nealmon | Normalized Exponential Almon lag MIDAS coefficients |
| nealmon.gradient | Gradient function for normalized exponential Almon lag weights |
| 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 |
| prepmidas_r | Prepare necessary objects for fitting of the MIDAS regression |
| 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 |
| simplearma.sim | Simulate AR(1) or MA(1) model |
| split_data | Split mixed frequency data into in-sample and out-of-sample |
| 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 |
| weight_coef | Return the restricted coefficients generated by restriction function(s) |
| weight_names | Return the names of restriction function(s) |
| weight_param | Return the estimated hyper parameters of the weight function(s) |