Package: midasr 0.9

Vaidotas Zemlys-Balevičius

midasr: Mixed Data Sampling Regression

Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

Authors:Vaidotas Zemlys-Balevičius [cre], Virmantas Kvedaras [aut], Vaidotas Zemlys-Balevičius [aut]

midasr_0.9.tar.gz
midasr_0.9.zip(r-4.7)midasr_0.9.zip(r-4.6)midasr_0.9.zip(r-4.5)
midasr_0.9.tgz(r-4.6-any)midasr_0.9.tgz(r-4.5-any)
midasr_0.9.tar.gz(r-4.7-any)midasr_0.9.tar.gz(r-4.6-any)
midasr_0.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
midasr/json (API)

# Install 'midasr' in R:
install.packages('midasr', repos = c('https://mpiktas.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mpiktas/midasr/issues

Datasets:
  • oos_prec - Out-of-sample prediction precision data on simulation example
  • rvsp500 - Realized volatility of S&P500 index
  • 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

On CRAN:

Conda:

5.92 score 82 stars 204 scripts 670 downloads 69 exports 51 dependencies

Last updated from:e170a40a6e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK264
source / vignettesOK289
linux-release-x86_64OK237
macos-release-arm64OK214
macos-oldrel-arm64OK237
windows-develOK177
windows-releaseOK152
windows-oldrelOK163
wasm-releaseOK177

Exports:agk.testalmonpalmonp_gradientamidas_tableamweightsaverage_forecastcheck_mixfreqderiv_testsdmlsexpand_amidasexpand_weights_lagsextract.midas_rfmlsforecastgenexpgenexp_gradientget_estimation_samplegompertzpgompertzp_gradienthAh_testhAhr_testharstepharstep_gradienthf_lags_tableimidas_rlcauchyplcauchyp_gradientlf_lags_tablelstrmidas_auto_simmidas_lstr_plainmidas_lstr_simmidas_mmm_plainmidas_mmm_simmidas_nlprmidas_pl_plainmidas_pl_simmidas_qrmidas_rmidas_r_ic_tablemidas_r_npmidas_r_plainmidas_si_plainmidas_si_simmidas_simmidas_spmidas_umlsmlsdmmmmodselnakagamipnakagamip_gradientnbetanbeta_gradientnbetaMTnbetaMT_gradientnealmonnealmon_gradientplot_lstrplot_midas_coefplot_sppolysteppolystep_gradientselect_and_forecastsimulatesplit_dataupdate_weightsweights_table

Dependencies:askpassclicolorspacecpp11curlfarverforecastFormulafracdiffgenericsggplot2gluegtablehttrisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixMatrixModelsmimenlmenloptrnnetnumDerivopenssloptimxpracmaquantregR6RColorBrewerRcppRcppArmadillorlangS7sandwichscalesSparseMsurvivalsystexregtimeDateurcavctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Combine 'lws_table' objects+.lws_table
Andreou, Ghysels, Kourtellos LM testagk.test
Almon polynomial MIDAS weights specificationalmonp
Gradient function for Almon polynomial MIDAS weightsalmonp_gradient
Weight and lag selection table for aggregates based MIDAS regression modelamidas_table
Weights for aggregates based MIDAS regressionsamweights
Average forecasts of MIDAS modelsaverage_forecast
Check data for MIDAS regressioncheck_mixfreq
Extract coefficients of MIDAS regressioncoef.midas_nlpr
Extract coefficients of MIDAS regressioncoef.midas_r
Extract coefficients of MIDAS regressioncoef.midas_sp
Check whether non-linear least squares restricted MIDAS regression problem has convergedderiv_tests deriv_tests.midas_r
Non-linear parametric MIDAS regression model deviancedeviance.midas_nlpr
MIDAS regression model deviancedeviance.midas_r
Semi-parametric MIDAS regression model deviancedeviance.midas_sp
MIDAS lag structure for unit root processesdmls
Create table of weights, lags and starting values for Ghysels weight schemaexpand_amidas
Create table of weights, lags and starting valuesexpand_weights_lags
Extract coefficients and GOF measures from MIDAS regression objectextract.midas_r
Fitted values for non-linear parametric MIDAS regression modelfitted.midas_nlpr
Fitted values for semi-parametric MIDAS regression modelfitted.midas_sp
Full MIDAS lag structurefmls
Forecast MIDAS regressionforecast forecast.midas_r
Generalized exponential MIDAS coefficientsgenexp
Gradient of generalized exponential MIDAS coefficient generating functiongenexp_gradient
Get the data which was used to etimate MIDAS regressionget_estimation_sample
Normalized Gompertz probability density function MIDAS weights specificationgompertzp
Gradient function for normalized Gompertz probability density function MIDAS weights specificationgompertzp_gradient
Test restrictions on coefficients of MIDAS regressionhAh_test
Test restrictions on coefficients of MIDAS regression using robust version of the testhAhr_test
HAR(3)-RV model MIDAS weights specificationharstep
Gradient function for HAR(3)-RV model MIDAS weights specificationharstep_gradient
Create a high frequency lag selection table for MIDAS regression modelhf_lags_table
Restricted MIDAS regression with I(1) regressorsimidas_r
Normalized log-Cauchy probability density function MIDAS weights specificationlcauchyp
Gradient function for normalized log-Cauchy probability density function MIDAS weights specificationlcauchyp_gradient
Create a low frequency lag selection table for MIDAS regression modellf_lags_table
Compute LSTR term for high frequency variablelstr
Simulate simple autoregressive MIDAS modelmidas_auto_sim
LSTR (Logistic Smooth TRansition) MIDAS regressionmidas_lstr_plain
Simulate LSTR MIDAS regression modelmidas_lstr_sim
MMM (Mean-Min-Max) MIDAS regressionmidas_mmm_plain
Simulate MMM MIDAS regression modelmidas_mmm_sim
Non-linear parametric MIDAS regressionmidas_nlpr
Fit restricted MIDAS regressionmidas_nlpr.fit
MIDAS Partialy linear non-parametric regressionmidas_pl_plain
Simulate PL MIDAS regression modelmidas_pl_sim
Restricted MIDAS quantile regressionmidas_qr
Restricted MIDAS regressionmidas_r
Create a weight and lag selection table for MIDAS regression modelmidas_r_ic_table
Estimate non-parametric MIDAS regressionmidas_r_np
Restricted MIDAS regressionmidas_r_plain
Fit restricted MIDAS regressionmidas_r.fit
MIDAS Single index regressionmidas_si_plain
Simulate SI MIDAS regression modelmidas_si_sim
Simulate simple MIDAS regression response variablemidas_sim
Semi-parametric MIDAS regressionmidas_sp
Estimate unrestricted MIDAS regressionmidas_u
MIDAS lag structuremls
MIDAS lag structure with datesmlsd
Compute MMM term for high frequency variablemmm
Select the model based on given information criteriamodsel
Normalized Nakagami probability density function MIDAS weights specificationnakagamip
Gradient function for normalized Nakagami probability density function MIDAS weights specificationnakagamip_gradient
Normalized beta probability density function MIDAS weights specificationnbeta
Gradient function for normalized beta probability density function MIDAS weights specificationnbeta_gradient
Normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible)nbetaMT
Gradient function for normalized beta probability density function MIDAS weights specification (MATLAB toolbox compatible)nbetaMT_gradient
Normalized Exponential Almon lag MIDAS coefficientsnealmon
Gradient function for normalized exponential Almon lag weightsnealmon_gradient
Out-of-sample prediction precision data on simulation exampleoos_prec
Plot MIDAS coefficientsplot_lstr
Plot MIDAS coefficientsplot_midas_coef plot_midas_coef.midas_r
Plot MIDAS coefficientsplot_midas_coef.midas_nlpr
Plot non-parametric part of the single index MIDAS regressionplot_sp
Step function specification for MIDAS weightspolystep
Gradient of step function specification for MIDAS weightspolystep_gradient
Predict method for non-linear parametric MIDAS regression fitpredict.midas_nlpr
Predict method for MIDAS regression fitpredict.midas_r
Predict method for semi-parametric MIDAS regression fitpredict.midas_sp
Calculate data for hAh_test and hAhr_testprep_hAh
Realized volatility of S&P500 indexrvsp500
Create table for different forecast horizonsselect_and_forecast
Simulate MIDAS regression responsesimulate simulate.midas_r
Split mixed frequency data into in-sample and out-of-samplesplit_data
Updates weights in MIDAS regression formulaupdate_weights
US quartely seasonaly adjusted consumer price indexUScpiqs
US weekly effective federal funds rate.USeffrw
United States total employment non-farms payroll, monthly, seasonally adjusted.USpayems
United States gross domestic product, quarterly, seasonaly adjusted annual rate.USqgdp
US annual gross domestic product in billions of chained 2005 dollarsUSrealgdp
US monthly unemployment rateUSunempr
Create a weight function selection table for MIDAS regression modelweights_table