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Long Memory (long + memory)
Terms modified by Long Memory Selected AbstractsA NOTE ON CHAMBERS'S "LONG MEMORY AND AGGREGATION IN MACROECONOMIC TIME SERIES",INTERNATIONAL ECONOMIC REVIEW, Issue 3 2005Leonardo Rocha Souza This note reviews some results on aggregating discrete-time long memory processes, providing a formula for the spectrum of the aggregates that considers the aliasing effect. [source] Trade Balance and Exchange Rate: Unit Roots, Co-integration and Long Memory in the US and the UKECONOMIC NOTES, Issue 1 2008Luis A. Gil-Alana This paper deals with the relationship between the balance of trade and the exchange rate in the US/UK case. Many authors have studied this issue for many countries, but despite the intensive research, there is still no agreement about the effectiveness of currency devaluation to increase a country's balance of trade. We first analyse the relationship between the two variables using unit roots and co-integration methods, and the results are ambiguous. We try a new approach based on fractional integration. The unit root hypothesis is rejected in case of the trade balance in favour of smaller orders of integration, while this hypothesis is not rejected for the exchange rate. Thus, the two series do not possess the same order of integration. We sort this problem out by taking the exchange rate as an exogenous variable, and including it in a regression model where the residuals might follow a fractionally integrated model. [source] Model Selection for Broadband Semiparametric Estimation of Long Memory in Time SeriesJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2001Clifford M. Hurvich We study the properties of Mallows' CL criterion for selecting a fractional exponential (FEXP) model for a Gaussian long-memory time series. The aim is to minimize the mean squared error of a corresponding regression estimator dFEXP of the memory parameter, d. Under conditions which do not require that the data were actually generated by a FEXP model, it is known that the mean squared error MSE=E[dFEXP,d]2 can converge to zero as fast as (log n)/n, where n is the sample size, assuming that the number of parameters grows slowly with n in a deterministic fashion. Here, we suppose that the number of parameters in the FEXP model is chosen so as to minimize a local version of CL, restricted to frequencies in a neighborhood of zero. We show that, under appropriate conditions, the expected value of the local CL is asymptotically equivalent to MSE. A combination of theoretical and simulation results give guidance as to the choice of the degree of locality in CL. [source] Robust Automatic Bandwidth for Long MemoryJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2001Marc Henry The choice of bandwidth, or number of harmonic frequencies, is crucial to semiparametric estimation of long memory in a covariance stationary time series as it determines the rate of convergence of the estimate, and a suitable choice can insure robustness to some non-standard error specifications, such as (possibly long-memory) conditional heteroscedasticity. This paper considers mean squared error minimizing bandwidths proposed in the literature for the local Whittle, the averaged periodogram and the log periodogram estimates of long memory. Robustness of these optimal bandwidth formulae to conditional heteroscedasticity of general form in the errors is considered. Feasible approximations to the optimal bandwidths are assessed in an extensive Monte Carlo study that provides a good basis for comparison of the above-mentioned estimates with automatic bandwidth selection. [source] The Analysis of Seasonal Long Memory: The Case of Spanish Inflation,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 6 2007Josu Arteche Abstract This paper describes semiparametric techniques recently proposed for the analysis of seasonal or cyclical long memory and applies them to a monthly Spanish inflation series. One of the conclusions is that this series has long memory not only at the origin but also at some but not all seasonal frequencies, suggesting that the fractional difference operator (1,L12)d should be avoided. Moreover, different persistent cycles are observed before and after the first oil crisis. Whereas the cycles seem stationary in the former period, we find evidence of a unit root after 1973, which implies that a shock has a permanent effect. Finally, it is shown how to compute the exact impulse responses and the coefficients in the autoregressive expansion of parametric seasonal long memory models. These two quantities are important to assess the impact of aleatory shocks such as those produced by a change of economic policy and for forecasting purposes, respectively. [source] Estimating the Fractional Order of Integration of Yields in the Brazilian Fixed Income MarketECONOMIC NOTES, Issue 3 2007Benjamin M. Tabak This paper presents evidence that yields on the Brazilian fixed income market are fractionally integrated, and compares the period before and after the implementation of the Inflation Targeting (IT) regime. The paper employs the commonly used GPH estimator and recently developed wavelets-based estimator of long memory. Empirical results suggest that interest rates are fractionally integrated and that interest rate spreads are fractionally integrated, with a higher order of integration in the period after the implementation of the IT regime. These results have important implications for the development of macroeconomic models for the Brazilian economy and for long-term forecasting. Furthermore, they imply that shocks to interest rates are long-lived. [source] Multi-step forecasting for nonlinear models of high frequency ground ozone data: a Monte Carlo approachENVIRONMETRICS, Issue 4 2002Alessandro Fassò Abstract Multi-step prediction using high frequency environmental data is considered. The complex dynamics of ground ozone often requires models involving covariates, multiple frequency periodicities, long memory, nonlinearity and heteroscedasticity. For these reasons parametric models, which include seasonal fractionally integrated components, self-exciting threshold autoregressive components, covariates and autoregressive conditionally heteroscedastic errors with heavy tails, have been recently introduced. Here, to obtain an h step ahead forecast for these models we use a Monte Carlo approach. The performance of the forecast is evaluated on different nonlinear models comparing some statistical indices with respect to the prediction horizon. As an application of this method, the forecast precision of a 2 year hourly ozone data set coming from an air traffic pollution station located in Bergamo, Italy, is analyzed. Copyright © 2002 John Wiley & Sons, Ltd. [source] Multifractal detrended fluctuation analysis of streamflow series of the Yangtze River basin, ChinaHYDROLOGICAL PROCESSES, Issue 26 2008Qiang Zhang Abstract Scaling and multifractal properties of the hydrological processes of the Yangtze River basin were explored by using a multifractal detrended fluctuation analysis (MF-DFA) technique. Long daily mean streamflow series from Cuntan, Yichang, Hankou and Datong stations were analyzed. Using shuffled streamflow series, the types of multifractality of streamflow series was also studied. The results indicate that the discharge series of the Yangtze River basin are non-stationary. Different correlation properties were identified within streamflow series of the upper, the middle and the lower Yangtze River basin. The discharge series of the upper Yangtze River basin are characterized by short memory or anti-persistence; while the streamflow series of the lower Yangtze River basin is characterized by long memory or persistence. h(q) vs q curves indicate multifractality of the hydrological processes of the Yangtze River basin. h(q) curves of shuffled streamflow series suggest that the multifractality of the streamflow series is mainly due to the correlation properties within the hydrological series. This study may be of practical and scientific importance in regional flood frequency analysis and water resource management in different parts of the Yangtze River basin. Copyright © 2008 John Wiley & Sons, Ltd. [source] A simulated reduction in Antarctic sea-ice area since 1750: implications of the long memory of the oceanINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 5 2005Hugues Goosse Abstract Using the three-dimensional coarse-resolution climate model ECBILT-CLIO, 1000-year long ensemble simulations with natural and anthropogenic forcings have been performed to study the long-term variation of the ice cover in the Southern Ocean. Over the last 250 years, the ice area has decreased by about 1 × 106 km2 in its annual mean. A comparison with experiments driven by only natural forcings suggests that this reduction is due to both natural and anthropogenic forcing, the latter playing a larger role than natural forcing over the last 150 years. Despite this contribution from anthropogenic forcing, the simulated ice area at the end of the 20th century is similar to that simulated during the 14th century because of the slow response of the Southern Ocean to radiative forcing. Sensitivity experiments performed with the model show that the model's initial conditions have a large influence on the simulated ice cover and that it is necessary to start simulations at least two centuries before the period of interest in order to remove this influence. Copyright © 2005 Royal Meteorological Society. [source] Arbitrage Bounds and the Time Series Properties of the Discount on UK Closed-End Mutual FundsJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2007Laurence Copeland Abstract:, In a dataset of weekly observations over the period since 1990, the discount on UK closed-end mutual funds is shown to be nonstationary, but reverting to a nonzero long run mean. Although the long run discount could be explained by factors like management expenses etc., its short run fluctuations are harder to reconcile with an arbitrage-free equilibrium. In time series terms, there is evidence of long memory in discounts consistent with a bounded random walk. This conclusion is supported by explicit nonlinearity tests, and by results which suggest the behaviour of the discount is perhaps best represented by one of the class of Smooth-Transition Autoregressive (STAR) models. [source] Comparing density forecast models,JOURNAL OF FORECASTING, Issue 3 2007Yong Bao Abstract In this paper we discuss how to compare various (possibly misspecified) density forecast models using the Kullback,Leibler information criterion (KLIC) of a candidate density forecast model with respect to the true density. The KLIC differential between a pair of competing models is the (predictive) log-likelihood ratio (LR) between the two models. Even though the true density is unknown, using the LR statistic amounts to comparing models with the KLIC as a loss function and thus enables us to assess which density forecast model can approximate the true density more closely. We also discuss how this KLIC is related to the KLIC based on the probability integral transform (PIT) in the framework of Diebold et al. (1998). While they are asymptotically equivalent, the PIT-based KLIC is best suited for evaluating the adequacy of each density forecast model and the original KLIC is best suited for comparing competing models. In an empirical study with the S&P500 and NASDAQ daily return series, we find strong evidence for rejecting the normal-GARCH benchmark model, in favor of the models that can capture skewness in the conditional distribution and asymmetry and long memory in the conditional variance.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependenceJOURNAL OF FORECASTING, Issue 6 2006Trino-Manuel Ñíguez Abstract Long-range persistence in volatility is widely modelled and forecast in terms of the so-called fractional integrated models. These models are mostly applied in the univariate framework, since the extension to the multivariate context of assets portfolios, while relevant, is not straightforward. We discuss and apply a procedure which is able to forecast the multivariate volatility of a portfolio including assets with long memory. The main advantage of this model is that it is feasible enough to be applied on large-scale portfolios, solving the problem of dealing with extremely complex likelihood functions which typically arises in this context. An application of this procedure to a portfolio of five daily exchange rate series shows that the out-of-sample forecasts for the multivariate volatility are improved under several loss functions when the long-range dependence property of the portfolio assets is explicitly accounted for.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memoryJOURNAL OF FORECASTING, Issue 5 2006David Karemera Abstract This paper presents an autoregressive fractionally integrated moving-average (ARFIMA) model of nominal exchange rates and compares its forecasting capability with the monetary structural models and the random walk model. Monthly observations are used for Canada, France, Germany, Italy, Japan and the United Kingdom for the period of April 1973 through December 1998. The estimation method is Sowell's (1992) exact maximum likelihood estimation. The forecasting accuracy of the long-memory model is formally compared to the random walk and the monetary models, using the recently developed Harvey, Leybourne and Newbold (1997) test statistics. The results show that the long-memory model is more efficient than the random walk model in steps-ahead forecasts beyond 1 month for most currencies and more efficient than the monetary models in multi-step-ahead forecasts. This new finding strongly suggests that the long-memory model of nominal exchange rates be studied as a viable alternative to the conventional models.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Forecasting high-frequency financial data with the ARFIMA,ARCH modelJOURNAL OF FORECASTING, Issue 7 2001Michael A. Hauser Abstract Financial data series are often described as exhibiting two non-standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA,ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid (1987). For daily data on the Swiss 1-month Euromarket interest rate during the period 1986,1989, the ARFIMA,ARCH (5,d,2/4) model with non-integer d is selected by AIC. Model-based out-of-sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (, > 40). Regarding volatility forecasts, however, the selected ARFIMA,ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd. [source] Time series modelling of two millennia of northern hemisphere temperatures: long memory or shifting trends?JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2007Terence C. Mills Summary., The time series properties of the temperature reconstruction of Moberg and co-workers are analysed. It is found that the record appears to exhibit long memory characteristics that can be modelled by an autoregressive fractionally integrated moving average process that is both stationary and mean reverting, so that forecasts will eventually return to a constant underlying level. Recent research has suggested that long memory and shifts in level and trend may be confused with each other, and fitting models with slowly changing trends is found to remove the evidence of long memory. Discriminating between the two models is difficult, however, and the strikingly different forecasts that are implied by the two models point towards some intriguing research questions concerning the stochastic process driving this temperature reconstruction. [source] Contemporaneous aggregation of GARCH processesJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2007Paolo Zaffaroni Abstract., In this article, the effect of contemporaneous aggregation of heterogeneous generalized autoregressive conditionally heteroskedastic (GARCH) processes, as the cross-sectional size diverges to infinity is studied. We analyse both cases of cross-sectionally dependent and independent individual processes. The limit aggregate does not belong to the class of GARCH processes. Dynamic conditional heteroskedasticity is only preserved when the individual processes are sufficiently cross-correlated, although long memory for the limit aggregate volatility is not attainable. We also explore more general forms of cross-sectional dependence and various types of aggregation schemes. [source] Averaged Periodogram Spectral Estimation with Long-memory Conditional HeteroscedasticityJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2001Marc Henry The empirical relevance of long-memory conditional heteroscedasticity has emerged in a variety of studies of long time series of high frequency financial measurements. A reassessment of the applicability of existing semiparametric frequency domain tools for the analysis of time dependence and long-run behaviour of time series is therefore warranted. To that end, in this paper the averaged periodogram statistic is analysed in the framework of a generalized linear process with long-memory conditional heteroscedastic innovations according to a model specification first proposed by Robinson (Testing for strong serial correlation and dynamic conditional heteroscedasticity in multiple regression. J. Economet. 47 (1991), 67,84). It is shown that the averaged periodogram estimate of the spectral density of a short-memory process remains asymptotically normal with unchanged asymptotic variance under mild moment conditions, and that for strongly dependent processes Robinson's averaged periodogram estimate of long memory (Semiparametric analysis of long memory time series. Ann. Stat. 22 (1994), 515,39) remains consistent. [source] Robust Automatic Bandwidth for Long MemoryJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2001Marc Henry The choice of bandwidth, or number of harmonic frequencies, is crucial to semiparametric estimation of long memory in a covariance stationary time series as it determines the rate of convergence of the estimate, and a suitable choice can insure robustness to some non-standard error specifications, such as (possibly long-memory) conditional heteroscedasticity. This paper considers mean squared error minimizing bandwidths proposed in the literature for the local Whittle, the averaged periodogram and the log periodogram estimates of long memory. Robustness of these optimal bandwidth formulae to conditional heteroscedasticity of general form in the errors is considered. Feasible approximations to the optimal bandwidths are assessed in an extensive Monte Carlo study that provides a good basis for comparison of the above-mentioned estimates with automatic bandwidth selection. [source] Estimation of the Dominating Frequency for Stationary and Nonstationary Fractional Autoregressive ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2000Jan Beran This paper was motivated by the investigation of certain physiological series for premature infants. The question was whether the series exhibit periodic fluctuations with a certain dominating period. The observed series are nonstationary and/or have long-range dependence. The assumed model is a Gaussian process Xt whose mth difference Yt = (1 ,B)mXt is stationary with a spectral density f that may have a pole (or a zero) at the origin. the problem addressed in this paper is the estimation of the frequency ,max where f achieves the largest local maximum in the open interval (0, ,). The process Xt is assumed to belong to a class of parametric models, characterized by a parameter vector ,, defined in Beran (1995). An estimator of ,max is proposed and its asymptotic distribution is derived, with , being estimated by maximum likelihood. In particular, m and a fractional differencing parameter that models long memory are estimated from the data. Model choice is also incorporated. Thus, within the proposed framework, a data driven procedure is obtained that can be applied in situations where the primary interest is in estimating a dominating frequency. A simulation study illustrates the finite sample properties of the method. In particular, for short series, estimation of ,max is difficult, if the local maximum occurs close to the origin. The results are illustrated by two of the data examples that motivated this research. [source] A Semiparametric Analysis of the Term Structure of the US Interest Rates,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 4 2009Fabrizio Iacone Abstract The short end of the US$ term structure of interest rates is analysed allowing for the possibility of fractional integration and cointegration. This approach permits mean-reverting dynamics for the data and the existence of a common long run stochastic trend to be maintained simultaneously. We estimate the model for the period 1963,2006 and find it compatible with this structure. The restriction that the data are I(1) and the errors are I(0) is rejected, mainly because the latter still display long memory. This result is consistent with a model of monetary policy in which the Central Bank operates affecting contracts with short term maturity, and the impulses are transmitted to contracts with longer maturities and then to the final goals. However, the transmission of the impulses along the term structure cannot be modelled using the Expectations Hypothesis. [source] The Analysis of Seasonal Long Memory: The Case of Spanish Inflation,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 6 2007Josu Arteche Abstract This paper describes semiparametric techniques recently proposed for the analysis of seasonal or cyclical long memory and applies them to a monthly Spanish inflation series. One of the conclusions is that this series has long memory not only at the origin but also at some but not all seasonal frequencies, suggesting that the fractional difference operator (1,L12)d should be avoided. Moreover, different persistent cycles are observed before and after the first oil crisis. Whereas the cycles seem stationary in the former period, we find evidence of a unit root after 1973, which implies that a shock has a permanent effect. Finally, it is shown how to compute the exact impulse responses and the coefficients in the autoregressive expansion of parametric seasonal long memory models. These two quantities are important to assess the impact of aleatory shocks such as those produced by a change of economic policy and for forecasting purposes, respectively. [source] On Business Cycle Asymmetries in G7 CountriesOXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2004Khurshid M. Kiani Abstract We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean. We provide evidence on this issue using a variety of time series models. Our approach is fully parametric. Our testing strategy is robust to any conditional heteroskedasticity, outliers, and/or long memory that may be present. Our results indicate fairly strong evidence of nonlinearities in the conditional mean dynamics of the GDP growth rates for Canada, Germany, Italy, Japan, and the US. For France and the UK, the conditional mean dynamics appear to be largely linear. Our study shows that while the existence of conditional heteroskedasticity and long memory does not have much effect on testing for linearity in the conditional mean, accounting for outliers does reduce the evidence against linearity. [source] Persistence in some energy futures marketsTHE JOURNAL OF FUTURES MARKETS, Issue 5 2010Juncal Cunado In this study, we examine the possibility of long-range dependence in some energy futures markets for different maturities. In order to test for persistence, we use a variety of techniques based on non-parametric, semi-parametric and parametric methods. The results indicate that there is little or no evidence of long memory in gasoline, propane, oil and heating oil at different maturities. However, when we focus on the volatility process, proxied by the absolute returns, we find strong evidence of long memory in all the variables at different contracts. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:490,507, 2010 [source] The Stochastic Structure of the Time,Varying Beta: Evidence from UK CompaniesTHE MANCHESTER SCHOOL, Issue 6 2002Taufiq Choudhry The stochastic structure of time,varying betas from 15 companies in the UK is investigated. Time,varying betas are estimated by means of the bivariate MA,GARCH model. The stochastic structure is investigated by means of two fractional integration tests, the Geweke and Porter,Hudak and the Robinson tests, and a structural,break,oriented unit root test. Results show that time,varying betas are mean,reverting but only few have a long memory and thus are mean,reverting at a slow rate. This result is further backed by the structural break unit root test. These results contradict earlier studies, which fail to find a stationary beta. Stationary betas may imply that stock returns may be forecast in the long run. [source] Out-of-sample Hedge Performances for Risk Management in China Commodity Futures Markets,ASIAN ECONOMIC JOURNAL, Issue 3 2009Sang-Kuck Chung C13; C32; G13 We consider a new time-series model that describes long memory and asymmetries simultaneously under the dynamic conditional correlation specification, and that can be used to assess an extensive evaluation of out-of-sample hedging performances using aluminum and fuel oil futures markets traded on the Shanghai Futures Exchange. Upon fitting it to the spot and futures returns of aluminum and fuel oil markets, it is found that a parsimonious version of the model captures the salient features of the data rather well. The empirical results suggest that separating the effects of positive and negative basis on the market volatility, and the correlation between two markets as well as jointly incorporating the long memory effect of the basis on market returns not only provides better descriptions of the dynamic behaviors of commodity prices, but also plays a statistically significant role in determining dynamic hedging strategies. [source] | ||||||||||||||||