Dynamic factor copula models with estimated cluster assignments

Oh, Dong Hwan; Patton, Andrew J.

doi:10.1016/j.jeconom.2022.07.012

Cited by 11 publications

(3 citation statements)

References 42 publications

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“…Generally speaking, it is difficult to solve a model that combines a min-max-min structure and constraint with uncertain variables [36]. In handling uncertain variables, this paper employs a method that involves generating 10 scenarios and their corresponding probability values based on 180 days of data [37]. This is achieved through copula joint WD-PV generation and the application of the k-means clustering algorithm.…”

Section: Model Solving Methodsmentioning

confidence: 99%

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

Zhang,

Zhu

2024

Processes

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With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness.

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Section: Model Solving Methodsmentioning

confidence: 99%

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

Zhang,

Zhu

2024

Processes

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“…Model-based procedures for the joint dependency of high dimensional time series often employ a factor model framework under which the clusters of the series are generated by specific dynamic factors. See, for instance, Ando and Bai (2017), Alonso et al (2020) and Oh and Patton (2023).…”

Section: Other Approaches For Clustering Scalar Time Seriesmentioning

confidence: 99%

A testing approach to clustering scalar time series

Peña

Tsay

2023

Journal Time Series Analysis

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This article considers clustering stationary scalar time series using their marginal properties and a hierarchical method. Two major issues involved are to detect the existence of clusters and to determine their number. We propose a new test statistic for detecting whether a data set consists of multiple clusters and a new procedure to determine the number of clusters. The proposed method is based on the jumps, that is, the increments, in the heights of the dendrogram when a hierarchical clustering is applied to the data. We use autoregressive sieve bootstrap to obtain a reference distribution of the test statistics and propose an iterative procedure to find the number of clusters. The clusters found are internally homogeneous according to the test statistics used in the analysis. The performance of the proposed procedure in finite samples is investigated by Monte Carlo simulations and illustrated by some empirical examples. Comparisons with some existing methods for selecting the number of clusters are also investigated.

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“…A key question in the application of k ‐means clustering in financial markets that needs to be addressed is whether group separations are statistically significant, thereby justifying the cluster outcomes. Oh and Patton (2023) and Patton and Weller (2022) tested for statistical significance in group separations in the S&P 100 stock market and U.S. mutual funds. Rejection of the hypothesis of homogeneous parameters gives evidence of group‐specific heterogeneity, a conclusion that has implications for financial investment.…”

Section: Empirical Applicationsmentioning

confidence: 99%

A Panel Clustering Approach to Analyzing Bubble Behavior

Liu

Phillips

2023

Int Economic Review

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This study provides new mechanisms for identifying and estimating explosive bubbles in mixed‐root panel autoregressions with a latent group structure. A postclustering approach is employed that combines k‐means clustering with right‐tailed panel‐data testing. Uniform consistency of the k‐means algorithm is established. Pivotal null limit distributions of the tests are introduced. A new method is proposed to consistently estimate the number of groups. Monte Carlo simulations show that the proposed methods perform well in finite samples; and empirical applications of the proposed methods identify bubbles in the U.S. and Chinese housing markets and the U.S. stock market.

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Dynamic factor copula models with estimated cluster assignments

Cited by 11 publications

References 42 publications

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

A testing approach to clustering scalar time series

A Panel Clustering Approach to Analyzing Bubble Behavior

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