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Gotowe bibliografie tematyczne / Geweke Porter-Hudak method

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Gotowa bibliografia na temat „Geweke Porter-Hudak method”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Geweke Porter-Hudak method"

1

Rege, Sameer, and Samuel Martín. "Portuguese Stock Market: A Long-Memory Process?" Business: Theory and Practice 12, no. (1) (2011): 75–84. https://doi.org/10.3846/btp.2011.08.

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This paper gives a basic overview of the various attempts at modelling stochastic processes for stock markets with a specific application to the Portuguese stock market data. Long-memory dependence in the stock prices would completely alter the data generation process and econometric models not considering the long-range dependence would exhibit poor forecasting abilities. The Hurst exponent is used to identify the presence of long-memory or fractal behaviour of the data generation process for the daily returns to ascertain if the process follows a fractional brownian motion. Detrended fluctua

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Rege, Sameer, and Samuel Gil Martín. "PORTUGUESE STOCK MARKET: A LONG-MEMORY PROCESS?" Business: Theory and Practice 12, no. 1 (2011): 75–84. http://dx.doi.org/10.3846/btp.2011.08.

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This paper gives a basic overview of the various attempts at modelling stochastic processes for stock markets with a specific application to the Portuguese stock market data. Long-memory dependence in the stock prices would completely alter the data generation process and econometric models not considering the long-range dependence would exhibit poor forecasting abilities. The Hurst exponent is used to identify the presence of long-memory or fractal behaviour of the data generation process for the daily returns to ascertain if the process follows a fractional brownian motion. Detrended fluctua

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3

Saber, Ammar Muayad, and Rabab Abdulrida Saleh. "A Comparative Study for Estimate Fractional Parameter of ARFIMA Model." Journal of Economics and Administrative Sciences 28, no. 133 (2022): 131–48. http://dx.doi.org/10.33095/jeas.v28i133.2359.

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Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this res

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Wang, W., P. H. A. J. M. van Gelder, J. K. Vrijling, and X. Chen. "Detecting long-memory: Monte Carlo simulations and application to daily streamflow processes." Hydrology and Earth System Sciences Discussions 3, no. 4 (2006): 1603–27. http://dx.doi.org/10.5194/hessd-3-1603-2006.

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Abstract. The Lo's R/S tests (Lo, 1991), GPH test (Geweke and Porter-Hudak, 1983) and the maximum likelihood estimation method implemented in S-Plus (S-MLE) are evaluated through intensive Mote Carlo simulations for detecting the existence of long-memory. It is shown that, it is difficult to find an appropriate lag q for Lo's test for different AR and ARFIMA processes, which makes the use of Lo's test very tricky. In general, the GPH test outperforms the Lo's test, but for cases where there is strong autocorrelations (e.g., AR(1) processes with φ=0.97 or even 0.99), the GPH test is totally use

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Masad, Awdh Alrasheedi. "Long memory in the Hybrid Time Series." Journal of Progressive Research in Mathematics 12, no. 5 (2017): 2066–72. https://doi.org/10.5281/zenodo.3974841.

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In this paper, consideration is given to the assessment of the availability of long-term memory in a time series with variable coefficients that depend on the Markov chain or the continuous Markov process. In the work,the author has succeeded in establishing sufficient conditions for the presence of a long-term memory based on a multifractal detrended fluctuation analysis and the Geweke-Porter-Hudak method. This is demonstrated with a real example which involved an analysis of the Erste Group for the period07.01.2000 – 23.10.2017, as a result of which it was possible to prove that this c

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Swarnalatha, P., V. Srinivasa Rao, G. Raghunadha Reddy, Santosha Rathod, D. Ramesh, and K. Uma Devi. "Modelling Long Memory Time Series for Groundnut Prices in Andhra Pradesh with Autoregressive Fractionally Integrated Moving Average for Forecasting." Journal of Scientific Research and Reports 30, no. 7 (2024): 289–302. http://dx.doi.org/10.9734/jsrr/2024/v30i72145.

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The presence of long memory in time series is characterized by an autocorrelation function that decreases slowly or hyperbolically. The most suitable model for capturing this phenomenon is the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is particularly useful for modeling historical prices in financial data analysis. This research aims to assess ARFIMA modeling of long memory processes using the Geweke and Porter-Hudak (GPH) parameter estimation method. The model was applied to the monthly prices of Groundnut in Andhra Pradesh, from the period January 2002 to De

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Kaya Soylu, Pınar, Mustafa Okur, Özgür Çatıkkaş, and Z. Ayca Altintig. "Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple." Journal of Risk and Financial Management 13, no. 6 (2020): 107. http://dx.doi.org/10.3390/jrfm13060107.

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This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable mo

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Saganowski, Łukasz, and Tomasz Andrysiak. "Time series forecasting with model selection applied to anomaly detection in network traffic." Logic Journal of the IGPL 28, no. 4 (2020): 531–45. http://dx.doi.org/10.1093/jigpal/jzz059.

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Abstract In herein article an attempt of problem solution connected with anomaly detection in network traffic with the use of statistic models with long or short memory dependence was presented. In order to select the proper type of a model, the parameter describing memory on the basis of the Geweke and Porter-Hudak test was estimated. Bearing in mind that the value of statistic model depends directly on quality of data used for its creation, at the initial stage of the suggested method, outliers were identified and then removed. For the implementation of this task, the criterion using the val

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Wang, W., P. H. A. J. M. Van Gelder, J. K. Vrijling, and X. Chen. "Detecting long-memory: Monte Carlo simulations and application to daily streamflow processes." Hydrology and Earth System Sciences 11, no. 2 (2007): 851–62. http://dx.doi.org/10.5194/hess-11-851-2007.

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Abstract. The Lo's modified rescaled adjusted range test (R/S test) (Lo, 1991), GPH test (Geweke and Porter-Hudak, 1983) and two approximate maximum likelihood estimation methods, i.e., Whittle's estimator (W-MLE) and another one implemented in S-Plus (S-MLE) based on the algorithm of Haslett and Raftery (1989) are evaluated through intensive Monte Carlo simulations for detecting the existence of long-memory. It is shown that it is difficult to find an appropriate lag q for Lo's test for different short-memory autoregressive (AR) and fractionally integrated autoregressive and moving average (A

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10

Babayemi, A. W., Hussaini. Jamilu, and A. Abdullahi. "Long Memory Behaviour of Nigerian Telecommunication Network Flow (A Case Study MTN Network)." International Journal of Novel Research in Healthcare and Nursing 10, no. 3 (2023): 278–86. https://doi.org/10.5281/zenodo.10118187.

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<strong>Abstract:</strong><i> </i>As the demand for telecommunication services continues to grow, there is a need to understand the behavior of the telecommunication network flow to ensure optimal performance, reliability and increase the number of subscribers. This research used an Autoregressive Fractionally Integrated Moving Average (ARFIMA) model along with its different estimation procedures to investigate long memory behaviour of Nigerian telecommunication network flow from August 11, 2017, to December 31, 2022. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests were emplo

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