Improvement of extrapolation in multivariate stationary processes
Improvement of Fisher's test of periodicity
Fisher's test of periodicity in time series and Siegel's version of this test for compound periodicities are investigated in the paper. An improvement increasing the power of the test is suggested and demonstrated by means of numerical simulations.
Improving EWMA plans for detecting unusual increases in Poisson counts.
Incomplete time series prediction using max-margin classification of data with absent features.
Indices económicos. Modelo dinámico de inversión.
Se estudia el problema de inversión en un mercado en donde las rentabilidades aleatorias de los títulos satisfacen una relación temporal con rentabilidades anteriores y las interrelaciones vendrán dadas a través de unos índices, uno común a todos los títulos y otro específico del sector en que pueda incluirse cada título.
Inference Robustness of ARIMA Models under Non-Normality - Special Applications to Stock Price Data.
Inferences about the Parameters of a Time Series Model with Changing Variance.
Inferring the residual waiting time for binary stationary time series
For a binary stationary time series define to be the number of consecutive ones up to the first zero encountered after time , and consider the problem of estimating the conditional distribution and conditional expectation of after one has observed the first outputs. We present a sequence of stopping times and universal estimators for these quantities which are pointwise consistent for all ergodic binary stationary processes. In case the process is a renewal process with zero the renewal state...
Invariance principle in a bilinear model with weak nonlinearity.
Investigation of periodicity for dependent observations
It is proved that Hannan's procedure for statistical test of periodicity in the case of time series with dependent observations can be combined with Siegel's improvement of the classical Fischer's test of periodicity. Simulations performed in the paper show that this combination can increase the power of Hannan's test when at least two periodicities are present in the time series with dependent observations.
Least Squares Estimator for Regression Models with some Deterministic Time Varying Parameters.
Linear approximations to some non-linear AR(1) processes
Some methods for approximating non-linear AR(1) processes by classical linear AR(1) models are proposed. The quality of approximation is studied in special non-linear AR(1) models by means of comparisons of quality of extrapolation and interpolation in the original models and in their approximations. It is assumed that the white noise has either rectangular or exponential distribution.
Linear prediction of long-range dependent time series
We present two approaches for linear prediction of long-memory time series. The first approach consists in truncating the Wiener-Kolmogorov predictor by restricting the observations to the last k terms, which are the only available data in practice. We derive the asymptotic behaviour of the mean-squared error as k tends to +∞. The second predictor is the finite linear least-squares predictor i.e. the projection of the forecast value on the last k observations. It is shown that these two predictors...
Local Identification of ARMAX Structures Subject to Nonlinear Constraints.
Log-periodogram regression in asymmetric long memory
The long memory property of a time series has long been studied and several estimates of the memory or persistence parameter at zero frequency, where the spectral density function is symmetric, are now available. Perhaps the most popular is the log periodogram regression introduced by Geweke and Porter–Hudak [gewe]. In this paper we analyse the asymptotic properties of this estimate in the seasonal or cyclical long memory case allowing for asymmetric spectral poles or zeros. Consistency and asymptotic...
Long memory properties and covariance structure of the EGARCH model
The EGARCH model of Nelson [29] is one of the most successful ARCH models which may exhibit characteristic asymmetries of financial time series, as well as long memory. The paper studies the covariance structure and dependence properties of the EGARCH and some related stochastic volatility models. We show that the large time behavior of the covariance of powers of the (observed) ARCH process is determined by the behavior of the covariance of the (linear) log-volatility process; in particular, a...
Long memory properties and covariance structure of the EGARCH model
The EGARCH model of Nelson [29] is one of the most successful ARCH models which may exhibit characteristic asymmetries of financial time series, as well as long memory. The paper studies the covariance structure and dependence properties of the EGARCH and some related stochastic volatility models. We show that the large time behavior of the covariance of powers of the (observed) ARCH process is determined by the behavior of the covariance of the (linear) log-volatility process; in particular,...
Long memory time series models
Longitudinal K-sets analysis using lagged variables.
We present an application of nonlinear Generalised Canonical Analysis (GCA) for analysing longitudinal data. The application uses lagged versions of variables to accomodate the time-dependence in the measurements. The usefulness of the proposed method is illustrated in an example from developmental psychology, in which we explore the relationship between mother and child dyadic interaction during the first six months after birth, demonstrating how child behaviour can elicit mother behaviour. We...
Loss protection in pairs trading through minimum profit bounds: A cointegration approach.