Package <i>Wavemulcor</i>: Wavelet Multiple Regression and Correlation in R

Fernández-Macho, Javier

doi:10.1109/mcse.2018.2875320

Cited by 13 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section presents and discusses the methodological approach proposed and used in this study; the wavelet local multiple correlation (WLMC) 34 . We will follow 34 , 35 , 46 , 47 to summarize the method. Readers interested in obtaining more detailed information can consult these references.…”

Section: Methodsmentioning

confidence: 99%

“…Readers interested in obtaining more detailed information can consult these references. The WLMC method is implemented by a freely available R computer package called wavemulcor with its corresponding documentation to perform the WLMC analysis 35 , 47 .…”

Section: Methodsmentioning

confidence: 99%

“…The wavemulcor software 35 , 47 used to estimate the WLMC includes six of the most commonly used weight functions (or windows) for averaging and smoothing: the uniform window, Bartlett’s triangular window, Cleveland’s tricube window, Wendland’s truncated power window, Epanechnikov’s parabolic window, and the Gaussian window. All six windows satisfy and have compact support in , where M is the length of the weight function , except the Gaussian window that takes values for .…”

Section: Methodsmentioning

confidence: 99%

“…However 34 , suggested that the uniform, Cleveland’s tricube and Epanechnikov’s parabolic windows are not recommended due to the presence of negative values in their corresponding spectral windows and, he also suggested to use the Bartlett’s triangular, Wendland’s truncated power, and Gaussian windows because these windows are most adequate for signal extraction and smoothing and their spectral windows are non-negative. By default, wavemulcor 35 , 47 uses the Gaussian window for the following reasons: it is closest to the uniform weights (windows) in the time domain within a certain bandwidth, its Fourier transform is also Gaussian, it has near compact support in the frequency domain, and its spectral window is always positive 34 .…”

Section: Methodsmentioning

confidence: 99%

“…The WLMC measures a non-stationary time-evolving correlation structure at different scales within a multivariate set of data, and consists of one single set of multiscale correlations along time, each of them calculated as the square root of the regression coefficient of determination in that linear combination of locally weighted wavelet coefficients for which such coefficient of determination is a maximum 34 , 35 . The WLMC is a powerful statistical and computational tool that was originally developed to estimate correlation among multivariate, non-stationary, financial time series 34 , 35 . However, as we demonstrate in this study, the WLMC has a strong potential to be used with multivariate, stationary and non-stationary, climate and environmental time series as well.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamic wavelet correlation analysis for multivariate climate time series

Polanco-Martínez

Fernández-Macho

Medina‐Elizalde

2020

Sci Rep

View full text Add to dashboard Cite

The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic wavelet correlation analysis for multivariate climate time series

Polanco-Martínez

Fernández-Macho

Medina‐Elizalde

2020

Sci Rep

View full text Add to dashboard Cite

show abstract

Trade‐off between environmental sustainability and economic growth through coal consumption and natural resources exploitation in China: New policy insights from wavelet local multiple correlation

Adebayo

2022

Geological Journal

View full text Add to dashboard Cite

Challenges brought on by the rapid rate of economic growth include the depletion of natural resources (NAR) and environmental deterioration. Due to its abundant mineral resources and rapid economic growth, China has gained attention from both the developing and developed worlds. Using data from 1970/Q1 to 2020/Q4, this paper evaluates the time‐frequency nexus between CO2 emissions and natural resources, coal consumption, and economic growth. The study employs the Wavelet Local Multiple Correlation (WLMC) to explore this nexus. This approach enhances the capacity to comprehend the fundamental relationships between these indicators at different frequencies. To the best of the investigator' knowledge, this is the first empirical investigation to explore the time‐frequency nexus between natural resource, economic growth, coal use, and CO2 emissions in China, thus filling a gap in the literature. Furthermore, wavelet coherence is employed as a robustness check. The WLMC bivariate cases revealed that economic growth, coal consumption, and natural resources drive CO2 emissions positively at all frequencies, that is, in the short and long‐term, thus leading to environmental degradation. Furthermore, the wavelet coherence results support the outcomes of the WLMC bivariate cases. This study offers significant implications for policy and insights that are supported by the findings.

show abstract

How effective are trade policy and monetary policy in achieving a pathway to sustainable development? Evidence from a wavelet quantile‐on‐quantile Granger causality analysis

Adebayo,

Olanrewaju

2024

Sustainable Development

View full text Add to dashboard Cite

The paper explores the impact of trade policy (TRP) and monetary policy (MP) on CO2 emissions in the United States over the period February 1, 1988–December 12, 2022. The study employed wavelet quantile‐on‐quantile regression (WQQR) to explore the relationship across different quantiles and time‐scales. Furthermore, the present study introduced wavelet quantile‐on‐quantile Granger causality (WQQGC) to explore the predictive power of the independent variable over the dependent variable with a focus on different time‐scales and quantiles. The use of wavelet quantile‐based tools is supported by the nonlinear and non‐normal distribution of the variables. The results from WQQR show that in the short and medium term, an increase in renewable energy consumption (REC) in the United States is accompanied by an increase in CO2 emissions. However, in the long term, across all quantiles, REC effectively reduces CO2. In the short and medium term, the negative impact of TRP on CO2 emissions is apparent; however, we observed a supportive positive effect of TRP on reducing CO2 emissions in the long term. Across all periods, the influence of MP on CO2 emissions is generally weak yet positive; however, there are instances of negative associations. Across all quantiles in the short term, the impact of natural gas on CO2 emissions remains positive, although this positive effect diminishes in the long term. Finally, all the regressors can significantly predict CO2 emissions at dissimilar quantiles and time‐scales. The study formulates Sustainable Development Goals policies based on the above findings.

show abstract

Package Wavemulcor: Wavelet Multiple Regression and Correlation in R

Cited by 13 publications

References 10 publications

Dynamic wavelet correlation analysis for multivariate climate time series

Dynamic wavelet correlation analysis for multivariate climate time series

Trade‐off between environmental sustainability and economic growth through coal consumption and natural resources exploitation in China: New policy insights from wavelet local multiple correlation

How effective are trade policy and monetary policy in achieving a pathway to sustainable development? Evidence from a wavelet quantile‐on‐quantile Granger causality analysis

Contact Info

Product

Resources

About