Package: DMQ 0.1.2

Leopoldo Catania

DMQ: Dynamic Multiple Quantile (DMQ) Model

Perform estimation, prediction, and simulations using the Dynamic Multiple Quantile model of Catania and Luati (2023) <doi:10.1016/j.jeconom.2022.11.002>. Can be used to estimate a set of conditional time-varying quantiles of a time series that do not cross.

Authors:Leopoldo Catania [cre, aut], Alessandra Luati [ctb]

DMQ_0.1.2.tar.gz
DMQ_0.1.2.tar.gz(r-4.5-noble)DMQ_0.1.2.tar.gz(r-4.4-noble)
DMQ_0.1.2.tgz(r-4.4-emscripten)DMQ_0.1.2.tgz(r-4.3-emscripten)
DMQ.pdf |DMQ.html✨
DMQ/json (API)

# Install 'DMQ' in R:
install.packages('DMQ', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • vY - Data: Microsoft Corporation logarithmic percentage returns from December 8, 2010 to November 15, 2018 for a total of T = 2000 observation downloaded from Yahoo finance.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.00 score 167 downloads 8 exports 6 dependencies

Last updated 1 years agofrom:0469c3ecd6. Checks:2 OK, 1 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-linux-x86_64NOTEMar 21 2025
R-4.4-linux-x86_64OKMar 21 2025

Exports:EstimateDMQfn.DEoptimfn.optimfn.solnpForecastDMQMomentsDMQSimulateDMQUpdateDMQ

Dependencies:DEoptimMASSRcppRcppArmadilloRsolnptruncnorm

Citation

To cite GAS in publications use:

Catania, L. and Luati, A. (2023). Semiparametric modeling of multiple quantiles. Journal of Econometrics.<doi:10.1016/j.jeconom.2022.11.002>.

Corresponding BibTeX entry: 

  @Article{,
    title = {Semiparametric modeling of multiple quantiles},
    author = {Leopoldo Catania and Alessandra Luati},
    journal = {Journal of Econometrics},
    year = {2023},
    doi = {10.1016/j.jeconom.2022.11.002},
  }