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mqqcause - Multivariate Quantile-on-Quantile Granger Causality

Implements bivariate and Multivariate Quantile-on-Quantile Granger causality tests building on the Quantile-on-Quantile regression framework of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013> and the quantile Granger causality test of Troster (2018) <doi:10.1080/07474938.2016.1172400>. The bivariate test estimates the local-linear slope in the quantile regression of y_t on lagged x_t with lagged y_t as control, using Gaussian kernel weights, and tests it against zero by paired bootstrap. The multivariate (conditional) test additionally conditions on a set of moderators Z and optional x times Z interaction terms, in the spirit of Sinha, Ghosh, Hussain, Nguyen and Das (2023) <doi:10.1016/j.eneco.2023.107021>. A Sup-Wald summary across the quantile grid is also provided. Heatmaps and 3D surfaces default to the 'MATLAB' 'Parula' colour map.

Last updated

3.18 score

qqkrls - Quantile-on-Quantile Kernel Regularized Least Squares

Implements Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024) <doi:10.1016/j.jclepro.2024.140832>. Combines Kernel-Based Regularized Least Squares (KRLS) of Hainmueller and Hazlett (2014) <doi:10.1093/pan/mpt019> with the Quantile-on-Quantile regression of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>: for each quantile theta of the independent variable the response is fit by KRLS on the corresponding sub-sample and the tau-quantile of the resulting pointwise marginal effects yields beta(theta, tau). Standard errors come from a paired bootstrap. Visualisations use the 'MATLAB' 'Parula' colour map by default.

Last updated

3.18 score

mqqr - Multivariate Quantile-on-Quantile Regression

Implements Multivariate Quantile-on-Quantile Regression (m-QQR) of Sinha, Ghosh, Hussain, Nguyen and Das (2023) <doi:10.1016/j.eneco.2023.107021>, extending the bivariate Quantile-on-Quantile regression of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013> to include exogenous moderators and controls with optional interaction terms. For each pair of quantile levels (theta of the response and tau of the regressor) the package fits a locally-weighted quantile regression of y on the principal regressor x, a lagged dependent variable, moderators Z and the x*Z interaction terms, using Gaussian kernel weights on the empirical cumulative distribution function (CDF) distance. Bootstrap standard errors and Koenker-Machado pseudo R-squared are reported. Visualisations include 'MATLAB'-style 'Parula' and 'Jet' 3D surfaces, heatmaps and contour plots through 'plotly'.

Last updated

3.18 score

QuantileOnQuantile - Quantile-on-Quantile Regression Analysis

Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>. QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions.

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2.00 score 113 downloads

fjohansen - Johansen Cointegration Test with Fourier-Type Smooth Nonlinear Trends

Implements the Johansen cointegration test with Fourier-type smooth nonlinear deterministic trends restricted to cointegrating relations, as developed by Kurita and Shintani (2025) <doi:10.1080/07474938.2025.2530640>. Six model variants are supported: CNR (constant plus nonlinear, restricted in the cointegrating space), LNR (linear plus nonlinear, restricted), CNU (constant restricted, nonlinear unrestricted), LNU (linear restricted, nonlinear unrestricted), plus the standard constant- and linear-trend restricted Johansen models. The package also bundles the feasible generalised least squares (FGLS) Wald test of Perron, Shintani and Yabu (2017) <doi:10.1111/obes.12169> used as a frequency-selection pre-step, together with bundled critical-value tables, a vectorised simulator for the limiting distribution, publication-quality table exports (LaTeX and HTML) and 'ggplot2' figures matching those of the paper.

Last updated

1.70 score