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,...)
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- Computational Finance > Econometrics Toolbox >
- Computational Finance > Econometrics Toolbox > Conditional Mean Models >
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Start Hunting!MIDASv2.4
MIDASv2.4/private
版本 | 已发布 | 发行说明 | |
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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. |
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2.3.0.0 | Add a name-value pair 'DiscountIncrease' to MIDAS_ADL. |
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2.2.0.0 | Update user guide
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2.1.0.0 | version2.1 Add MIDAS quantile regression |
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2.0.0.0 | Add GARCH-MIDAS and DCC-MIDAS functions
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1.5.0.0 | Support Ylag as a cell array such as Ylag = {3,6,9} for flexible low frequency lagged regressors
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1.2.0.0 | Support the special case DL_MIDAS by setting Ylag = 0
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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. |
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1.0.0.0 |