Long memory time series
A STOCHASTIC PARAMETER REGRESSION MODEL FOR LONG MEMORY TIME SERIES
... with time but are deterministic and can thus be defined by a ...stationary time series processes, saying they are of theoretical value, but as they cannot, in general, be written in Markovian ...
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Modeling Long Memory in Volatility for Spot Price of Lentil with Multi-step Ahead Out-of-sample Forecast Using AR-FIGARCH Model
... and long memory behaviour in the conditional ...having long memory ...with long memory in agricultural commodities prices in ...of long memory time ...
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On a Class of Estimation and Test for Long Memory
... analyze time series properties based on the both time and frequency domain perspectives, and corresponding long memory models are confined to time and frequency domain analysis ...
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Long memory and structural breaks in time series models
... the long run variance of x tut has an additional component which is a function of the fourth cumulants and which is not estimated by the ex pression displayed ...short memory processes, the results of ...
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The Exponential Model for the Spectrum of a Time Series: Extensions and Applications
... important time series models, we illustrate the potential and the lim- itations of cepstral analysis for long memory time ...the long memory component from the short one; ...
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Seasonal and cyclical long memory in time series
... e memory or persistence param eter, d, is different from zero, u> is a frequency in th e interval [0,7r] and a ~ b means th a t a /6 —*• ...considered long memory as long as d ^ 0, more ...
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Time series properties of ARCH processes with persistent covariates
... as Long Memory ARCH model by Ding and Granger (1996) and FIGARCH model by Baillie et al (1996) are known to capture this long memory property in ...short memory volatility models can ...
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Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks
... model time series characteristics of postwar US inflation using a long memory autore- gressive fractionally integrated moving average model with stochastic volatility where we allow for ...
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Long memory and non linearity in Stock Markets
... the time series behaviour of stock market indexes can be described by a fractional ...to long memory or nonlinearities and has serious conse- quences for the efficient market hypothesis in ...
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Multivariate analysis of long memory series in the frequency domain
... a series is weak dependent it means that any disequilibrium situation will be transitory and in a short time the series will reach its trending value, so the role of economic policy is ...the ...
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Searching for long memory effects in time series of central Europe stock market indices
... These values of Hurst exponent for PX are also not far from Tran (2005), who found value of Hurst ex- ponent for period of more than 11 years from Sep- tember 1993 to October 2004 (i.e. 2607 daily closing prices) H = ...
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ANALYSING Inflation in Nigeria: A Fractionally Integrated ARFIMA GARCH Modelling Approach
... Clear conclusions can be drawn out for the CPI-inflation series in Nigeria as regards it long- memory properties. This properties also are of interest in policy making as the government will be ...
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Hurst’s Memory for Chaotic, Tree Ring, and SOI Series
... the time series in a Section ...term memory and others show the long term ...short memory models possessing short-term dependence structure, and h > 1/2 with long ...
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Forecasting Stock Market Volatility: A Forecast Combination Approach
... in time series starting in 1980, econometrics experts examined other types and subtypes of non-stationary and approximate persistence which explain the processes existing in many of the financial and ...
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Long memory and changepoint models:a spectral classification procedure
... for long memory over ...short memory dependence and we have shown here that if that short memory structure is cor- rectly taken into account within the sub-series then the series ...
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Forecasting long memory series subject to structural change: A two-stage approach
... real time series which might exhibit long memory is the possibility of structural ...long memory. In such cases the change(s) in the structure of the series might be ...
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Long Range Correlation in Time Series of News Sentiments
... constructed time series of news sentiments as follows: a nine-month period of 2015 was divided into non- overlapping consecutive intervals of equal length, and then we calculated the sum of sentiment scores ...
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Forecasting High Frequency Long Memory Series with Long Periods Using the SARFIMA Model
... is time-consuming when calculating the covariance matrix, especially for the high-frequency series with a relatively large sample size and when the AR coefficients, MA coefficients, seasonal and ...
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Extreme event return times in long-term memory processes near 1/f
... The simulated data is used to evaluate the potential pre- dictability of the extreme event return times. The LTM in the sequence of return times is analyzed by the detrended fluctuation analysis and reveals a power law ...
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Time Series Analysis For Long Memory Process Of Air Traffic Using Arfima
... In this study ARIMA and ARFIMA models have been fitted to the air traffic data regarding number of passengers travelled monthly by Air India scheduled domestic flights during January 2012 to December 2018. Since the air ...
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