Semiparametric inference in correlated long memory signal plus noise models

This paper proposes an extension of the log periodogram regression in perturbed long memory series that accounts for the added noise, also allowing for correlation between signal and noise, which represents a common situation in many economic and financial series. Consistency (for d < 1) and asymptotic normality (for d < 3/4) are shown with the same bandwidth restriction as required for the original log periodogram regression in a fully observable series, with the corresponding gain in asymptotic efficiency and faster convergence over competitors. Local Wald, Lagrange Multiplier and Hausman type tests of the hypothesis of no correlation between the latent signal and noise are also proposed.long memory, signal plus noise, semiparametric inference, log-periodogram regression

Gaussian Semiparametric Estimation in Long Memory in Stochastic Volatility and Signal Plus Noise Models

This paper considers the persistence found in the volatility of many financial time series by means of a local Long Memory in Stochastic Volatility model and analyzes the performance of the Gaussian semiparametric or local Whittle estimator of the memory parameter in a long memory signal plus noise model which includes the Long Memory in Stochastic Volatility as a particular case. It is proved that this estimate preserves the consistency and asymptotic normality encountered in observable long memory series and under milder conditions it is more efficient than the estimator based on a log-periodogram regression. Although the asymptotic properties do not depend on the signal-to-noise ratio the finite sample performance rely upon this magnitude and an appropriate choice of the bandwidth is important to minimize the influence of the added noise. I analyze the effect of the bandwidth via Monte Carlo. An application to a Spanish stock index is finally included.long memory, stochastic volatility, semiparametric estimation, frequency domain

Semiparametric estimation in perturbed long memory series

The estimation of the memory parameter in perturbed long memory series has recently attracted attention motivated especially by the strong persistence of the volatility in many financial and economic time series and the use of Long Memory in Stochastic Volatility (LMSV) processes to model such a behaviour. This paper discusses frequency domain semiparametric estimation of the memory parameter and proposes an extension of the log periodogram regression which explicitly accounts for the added noise, comparing it, asymptotically and in finite samples, with similar extant techniques. Contrary to the non linear log periodogram regression of Sun and Phillips (2003), we do not use a linear approximation of the logarithmic term which accounts for the added noise. A reduction of the asymptotic bias is achieved in this way and makes possible a faster convergence in long memory signal plus noise series by permitting a larger bandwidth. Monte Carlo results confirm the bias reduction but at the cost of a higher variability. An application to a series of returns of the Spanish Ibex35 stock index is finally included.long memory, stochastic volatility, semiparametric estimation

Selection of the number of frequencies using bootstrap techniques in log-periodogram regression

The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean squared error and compare its performance with other existing techniques for optimal bandwidth selection in a mean squared error sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analyzed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.bootstrap, long memory, log-periodogram regression, bandwidth selection

Semiparametric estimation in perturbed long memory series

The estimation of the memory parameter in perturbed long memory series has recently attracted attention motivated especially by the strong persistence of the volatility in many financial and economic time series and the use of Long Memory in Stochastic Volatility (LMSV) processes to model such a behaviour. This paper discusses frequency domain semiparametric estimation of the memory parameter and proposes an extension of the log periodogram regression which explicitly accounts for the added noise, comparing it, asymptotically and in finite samples, with similar extant techniques. Contrary to the non linear log periodogram regression of Sun and Phillips (2003), we do not use a linear approximation of the logarithmic term which accounts for the added noise. A reduction of the asymptotic bias is achieved in this way and makes possible a faster convergence in long memory signal plus noise series by permitting a larger bandwidth. Monte Carlo results confirm the bias reduction but at the cost of a higher variability. An application to a series of returns of the Spanish Ibex35 stock index is finally included.Research supported by the University of the Basque Country grant 9/UPV 00038.321-13503/2001 and the Spanish Ministerio de Ciencia y Tecnología and FEDER grant BEC2003-02028

Exact Local Whittle estimation in long memory time series with multiple poles

A generalization of the Exact Local Whittle estimator in Shimotsu and Phillips (2005) is proposed for jointly estimating all the memory parameters in general long memory time series that possibly display standard, seasonal and/or other cyclical strong persistence. Consistency and asymptotic normality are proven for stationary, non-stationary and noninvertible series, permitting straightforward standard inference of interesting hypotheses such as the existence of unit roots and equality of memory parameters at some or all seasonal frequencies, which can be used as a prior test for the application of seasonal differencing filters. The effects of unknown deterministic terms are also discussed. Finally, the finite sample performance is analysed in an extensive Monte Carlo exercise and an application to an U.S. Industrial Production index.Research was supported by the Spanish Ministry of Science and Innovation and ERDF grant ECO2016-76884-P, and UPV/EHU Econometrics Research Group, Basque Government grant IT1359-19

Semiparametric inference in correlated long memory signal plus noise models

This paper proposes an extension of the log periodogram regression in perturbed long memory series that accounts for the added noise, also allowing for correlation between signal and noise, which represents a common situation in many economic and financial series. Consistency (for d < 1) and asymptotic normality (for d < 3/4) are shown with the same bandwidth restriction as required for the original log periodogram regression in a fully observable series, with the corresponding gain in asymptotic efficiency and faster convergence over competitors. Local Wald, Lagrange Multiplier and Hausman type tests of the hypothesis of no correlation between the latent signal and noise are also proposed.Research supported by Spanish Ministerio de Ciencia y Tecnología and FEDER grant SEJ2007-61362/ECON and Basque Government grant IT-334-07 (UPV/EHU Econometrics Research Group)

Frequency Domain Local Bootstrap in long memory time series

Bootstrap techniques in the frequency domain have been proved to be effective instruments to approximate the distribution of many statistics of weakly dependent (short memory) series. However their validity with long memory has not been analysed yet. This paper proposes a Frequency Domain Local Bootstrap (FDLB) based on resampling a locally studentised version of the periodogram in a neighbourhood of the frequency of interest. A bound of the Mallows distance between the distributions of the original and bootstrap periodograms is offered for stationary and non-stationary long memory series. This result is in turn used to justify the use of FDLB for some statistics such as the average periodogram or the Local Whittle (LW) estimator. Finally, the finite sample behaviour of the FDLB in the LW estimator is analysed in a Monte Carlo, comparing its performance with rival alternatives.Research supported by the Spanish Ministry of Science and Innovation and ERDF grants ECO2016-76884-P, ID2019-105183GB-I00 and UPV/EHU Econometrics Research Group (Basque Government grantIT1359-1

Frequency Domain Local Bootstrap in long memory time series

Bootstrap techniques in the frequency domain have been proved to be effective instruments to approximate the distribution of many statistics of weakly dependent (short memory) series. However their validity with long memory has not been analysed yet. This paper proposes a Frequency Domain Local Bootstrap (FDLB) based on resampling a locally studentised version of the periodogram in a neighbourhood of the frequency of interest. A bound of the Mallows distance between the distributions of the original and bootstrap periodograms is offered for stationary and non-stationary long memory series. This result is in turn used to justify the use of FDLB for some statistics such as the average periodogram or the Local Whittle (LW) estimator. Finally, the finite sample behaviour of the FDLB in the LW estimator is analysed in a Monte Carlo, comparing its performance with rival alternatives.Research supported by the Spanish Ministry of Science and Innovation and ERDF grants ECO2016-76884-P, ID2019-105183GB-I00 and UPV/EHU Econometrics Research Group (Basque Government grantIT1359-1

Selection of the number of frequencies using bootstrap techniques in log-periodogram regression

The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean squared error and compare its performance with other existing techniques for optimal bandwidth selection in a mean squared error sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analyzed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.Research supported by the Ministerio de Ciencia y Tecnología and FEDER grant SEJ2007-61362, and by the Department of Education of the Basque Government grant IT-334-07 (UPV/EHU Econometrics Research Group)