Testing for a Markov-Switching Mean in Serially Correlated Data

Morley, James; Rabah, Zohra

doi:10.1007/978-1-4614-8060-0_5

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The component of forecasted price that is calculated with neural network in the HIRA model represents the price oscillation due to new input parameters. There are a large number of scientific papers where neural networks are used, however, in cases of high price volatility the results are not satisfying [25], therefore, in the HIRA model neural networks are used only to calculate the deviation of the predicted price caused by changes in input data. Maryniak and Weron conclude that electricity prices are mostly affected by traders' behaviors rather than by the fundamental characteristics of an electric power system [26], which is another reason for applying a model that combines a similar day and neural network method.…”

Section: Neural Network Componentmentioning

confidence: 99%

HIRA Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Petricic²,

Delimar

2019

Energies

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In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, market coupling, and global financial instability are contributing to the importance of accurate electricity price forecasting. Since power markets are not all equally developed, different price forecasting methods have been introduced for individual markets. The aim of this research is to introduce a short-term electricity price forecasting method that addresses the problems of price volatility, a varying number of input parameters, varying data availability, and a large number of parameters and input data. Furthermore, the proposed model can be used on any market as it targets the characteristics and specifics of each market. The proposed Hybrid Iterative Reactive Adaptive (HIRA) method consists of two phases. In analysis phase, fundamental parameters which affect the electricity price are identified depending on market development. Obtained parameters are used as data inputs for price forecasting using a hybrid method. The HIRA model combines a statistical approach for large data set analysis and a similar day method with neural network tools. Similar days are examined using a statistical method which combines correlation significance, price volatility, and forecasting accuracy of the historical data. Data are collected based on their availability and electricity prices are forecasted in several iterations. All relevant data for price forecasting are collected, categorized, and arranged using simple indicators which makes the HIRA model adaptive and reactive to new market circumstances. The proposed model is validated using the Hungarian Power Exchange (HUPX) electricity price data records. The results show that with HIRA model forecasting, the error is stable and does not depend on price volatility. The HIRA method has proven to be applicable for forecasting electricity prices in real-time market conditions and enables effective hedging of price risk in the production or market portfolio.

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Section: Neural Network Componentmentioning

confidence: 99%

HIRA Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Petricic²,

Delimar

2019

Energies

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Aimar¹,

Bismans²,

Diebolt³

2015

SpringerBriefs in Economics

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Filtering Persistent and Asymmetric Cycles

Donayre

2022

The B.E. Journal of Macroeconomics

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This paper evaluates the ability of the trend-cycle decomposition approach of Hamilton (2018. “Why You Should Never Use the Hodrick-Prescott Filter.” The Review of Economics and Statistics 100 (5): 831–43) to adequately characterize linear and (a)symmetric nonlinear business cycles fluctuations that are known to be persistent. This ability is contrasted to that of the Hodrick–Prescott filter. By means of Monte Carlo simulations, the results indicate that neither filter is able to preserve the cyclical properties of the data-generating process nor reproduce U.S. business cycles features, although this inability is exacerbated for the decomposition of Hamilton (2018. “Why You Should Never Use the Hodrick–Prescott Filter.” The Review of Economics and Statistics 100 (5): 831–43). Based on these findings, caution is called into question when this approach is applied to linear or nonlinear processes that are thought to exhibit persistence.

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Testing for a Markov-Switching Mean in Serially Correlated Data

Cited by 4 publications

References 13 publications

HIRA Model for Short-Term Electricity Price Forecasting

HIRA Model for Short-Term Electricity Price Forecasting

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Filtering Persistent and Asymmetric Cycles

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