Aggregation and long memory: recent developments

It is well-known that the aggregated time series might have very different properties from those of the individual series, in particular, long memory. At the present time, aggregation has become one of the main tools for modelling of long memory processes. We review recent work on contemporaneous aggregation of random-coefficient AR(1) and related models, with particular focus on various long memory properties of the aggregated process

LARCH, Leverage and Long Memory

We consider the long memory and leverage properties of a model for the conditional variance of an observable stationary sequence, where the conditional variance is the square of an inhomogeneous linear combination of past values of the observable sequence, with square summable weights. This model, which we call linear ARCH (LARCH), specializes to the asymmetric ARCH model of Engle (1990), and to a version of the quadratic ARCH model of Sentana (1995), these authors having discussed leverage potential in such models. The model which we consider was suggested by Robinson (1991), for use as a possibly long memory conditionally heteroscedastic alternative to i.i.d. behaviour, and further studied by Giraitis, Robinson and Surgailis (2000), who showed that integer powers, of degree at least 2, can have long memory autocorrelation. We establish conditions under which the cross-autovariance function between volatility and levels decays in the manner of moving average weights of long memory processes. We also establish a leverage property and conditions for finiteness of third and higher moments.Leverage, long memory, linear ARCH, LARCH, finiteness of moments.

El análisis funcional de materias primas heterogéneas y su aplicación a diferentes variedades de cuarcitas de la región pampeana (Agentina) : resultados experimentales y arqueológicos

Fil: Leipus, Marcela Sandra. Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; Argentin

Estimating long memory in panel random-coefficient AR(1) data

It is well-known that random-coefficient AR(1) process can have long memory depending on the index $\beta$ of the tail distribution function of the random coefficient, if it is a regularly varying function at unity. We discuss estimation of $\beta$ from panel data comprising N random-coefficient AR(1) series, each of length T. The estimator of $\beta$ is constructed as a version of the tail index estimator of Goldie and Smith (1987) applied to sample lag 1 autocorrelations of individual time series. Its asymptotic normality is derived under certain conditions on N, T and some parameters of our statistical model. Based on this result, we construct a statistical procedure to test if the panel random-coefficient AR(1) data exhibit long memory. A simulation study illustrates finite-sample performance of the introduced estimator and testing procedure

Nonparametric estimation of the distribution of the autoregressive coefficient from panel random-coefficient AR(1) data

We discuss nonparametric estimation of the distribution function $G(x)$ of the autoregressive coefficient $a \in (-1,1)$ from a panel of $N$ random-coefficient AR(1) data, each of length $n$, by the empirical distribution function of lag 1 sample autocorrelations of individual AR(1) processes. Consistency and asymptotic normality of the empirical distribution function and a class of kernel density estimators is established under some regularity conditions on $G(x)$ as $N$ and $n$ increase to infinity. The Kolmogorov-Smirnov goodness-of-fit test for simple and composite hypotheses of Beta distributed $a$ is discussed. A simulation study for goodness-of-fit testing compares the finite-sample performance of our nonparametric estimator to the performance of its parametric analogue discussed in Beran et al. (2010)

A Generalized Fractionally Differencing Approach inLong-Memory Modelling

We extend the class of known fractional ARIMA models to the class ofgeneralized ARIMA models which allows the generation of long-memory time serieswith long-range periodical behaviour at a finite number of spectrum frequences.The exact asymptotics of the covariance function and the spectrum at the pointsof peaks and zeroes are given. For obtaining asymptotic expansions, Gegenbauerpolynomials are used. Consistent parameter estimating is discussed using Whittle's estimate

Time series aggregation, disaggregation and long memory

We study the aggregation/disaggregation problem of random parameter AR(1) processes and its relation to the long memory phenomenon. We give a characterization of a subclass of aggregated processes which can be obtained from simpler, "elementary", cases. In particular cases of the mixture densities, the structure (moving average representation) of the aggregated process is investigated

The Change-point Problem for Dependent Observations

We consider the change-point problem for the marginal distributionfunction of a strictly stationary time series. Asymptotic behavior ofKolmogorov-Smirnov type tests and estimators of the change point is studiedunder the null-hypothesis and converging alternatives. The discussion is basedon a general empirical process' approach which enables a unified treatment ofboth short memory (weakly dependent) and long memory time series. In particular,the case of a long memory moving average process is studied, using recentresults of Giraitis and Surgailis (1994)

Laiko eilučių agregavimo, deagregavimo uždaviniai ir tolima priklausomybė

Large-scale aggregation and its inverse, disaggregation, problems are important in many fields of studies like macroeconomics, astronomy, hydrology and sociology. It was shown in Granger (1980) that a certain aggregation of random coefficient AR(1) models can lead to long memory output. Dacunha-Castelle and Oppenheim (2001) explored the topic further, answering when and if a predefined long memory process could be obtained as the result of aggregation of a specific class of individual processes.  In this paper,  the disaggregation scheme of Leipus et al.  (2006) is briefly discussed. Then disaggregation into AR(1)  is analyzed further, resulting in a theorem that helps, under corresponding assumptions, to construct a mixture density for a given aggregated by AR(1) scheme process. Finally the theorem is illustrated by FARUMA mixture densityÆs example.Santraukos nėra