MIDAS Matlab Toolbox

Repack of Mi(xed) Da(ta) S(ampling) regressions (MIDAS) written by Eric Ghysels and collaborators
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Aggiornato 5 mar 2021

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The mixed frequency regression studies the explanatory power of high frequency variables on the low frequency outcome. The weights associated with high frequency regressors are usually assumed some functional form. This toolbox is a repack of the Mi(xed) Da(ta) S(ampling) regressions (MIDAS) programs written by Eric Ghysels. It supports ADL-MIDAS type regressions. The package also includes two functions for GARCH-MIDAS and DCC-MIDAS estimation. See the enclosed user guide for details.
Syntax:
[...] = MIDAS_ADL(DataY,DataYdate,DataX,DataXdate)
[...] = MIDAS_ADL(DataY,DataYdate,DataX,DataXdate,name,value,...)
[...] = GarchMidas(y, name,value,...)
[...] = DccMidas(Data, name,value,...)

Compatibilità della release di MATLAB
Creato con R2015b
Compatibile con qualsiasi release
Compatibilità della piattaforma
Windows macOS Linux

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Versione Pubblicato Note della release
2.4.0.0

Add Legendre polynomial specification in the MIDAS_ADL function. Legendre polynomials are mutually orthogonal and avoid multicollinearity, compared to the non-orthogonal Almon power polynomials.

2.3.0.0

Add a name-value pair 'DiscountIncrease' to MIDAS_ADL.

2.2.0.0

Update user guide
Fix a bug in DccMIDAS FMINSEARCH when MATLAB Optimization Toolbox is not available.

2.1.0.0

version2.1 Add MIDAS quantile regression

2.0.0.0

Add GARCH-MIDAS and DCC-MIDAS functions
Package written by Eric Ghysels and collaborators
Update the toolbox title from "MIDAS Regression" to "MIDAS Matlab Toolbox"

1.5.0.0

Support Ylag as a cell array such as Ylag = {3,6,9} for flexible low frequency lagged regressors
Support Xlag = 0, so that the high frequency regressors are suppressed. OLS results will be produced.
User guide is updated to the version July 16, 2015

1.2.0.0

Support the special case DL_MIDAS by setting Ylag = 0
Update the user guide (version Dec 21, 2014)

1.1.0.0

Allow leads and lags specification 'horizon' be negative.

Add true out-of-sample forecast; results are restored in the last output argument 'Extended Forecast' struct.

1.0.0.0