Search results for "Autocovariance"
showing 9 items of 9 documents
Alternative Diagonality Criteria for SOBI
2015
Blind source separation (BSS) is a multivariate data analysis method, whose roots are in the signal processing community. BSS is applied in diverse fields, including, for example, brain imaging and economic time series analysis. In the BSS model there are interesting latent uncorrelated variables, and the aim is to estimate the latent variables from multiple linear combinations of them. In this article we assume that these variables are weakly stationary time series, and we consider estimation methods which are based on approximate joint diagonalization of autocovariance matrices. In the popular SOBI estimator, a set of matrices is most diagonal when the sum of squares of their diagonal ele…
The effect of round-off error on long memory processes
2011
We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequently, we find that the discretized process is also long memory with the same Hurst exponent as the original process. We consider the properties of two estimators of the Hurst exponent, namely the local Whittle (LW) estimator and the Detrended Fluctuation Analysis (DFA). By using analytical considerations and numerical simulations we show that, in presence of round-off error, both…
¿SE PUEDE MEDIR LA NEGOCIACIÓN INFORMADA?: UNA REVISIÓN DE LA METODOLOGÍA BASADA EN LAS COVARIANZAS DE LAS SERIES DE PRECIOS
2009
RESUMENEl desarrollo en los modelos teóricos de microestructura ha motivado la aparición de un grupo de trabajos encaminado al estudio empírico de los costes de transacción y sus componentes dada la importancia que han tenido los mismos en el estudio del funcionamiento de los mercados y la comparación entre éstos así como sus numerosas aplicaciones en campos afines (finanzas corporativas, eficiencia de los mercados, etc.). Por otra parte, la contrastación empírica de los distintos modelos establecidos muestra resultados claramente dispares. Por ello, el objetivo de nuestro trabajo es analizar con detalle y en conjunto dichos modelos centrándonos en un grupo con características muy similares…
Rayleigh and Rice Channels
2011
This chapter contains sections titled: System Theoretical Description of Multipath Channels Formal Description of Rayleigh and Rice Channels Elementary Properties of Rayleigh and Rice Channels Statistical Properties of Rayleigh and Rice Channels Further Reading Appendix 3.A Derivation of the Jakes Power Spectral Density and the Corresponding Autocorrelation Function Appendix 3.B Derivation of the Autocorrelation Function of the Envelope Appendix 3.C Derivation of the Autocovariance Spectrum of the Envelope Under Isotropic Scattering Conditions Appendix 3.D Derivation of the Level‐Crossing Rate of Rice Processes with Different Spectral Shapes of the Underlying Gaussian Random Processes
¿SE PUEDE MEDIR LA NEGOCIACIÓN INFORMADA?: UNA REVISIÓN DE LA METODOLOGÍA BASADA EN LAS COVARIANZAS DE LAS SERIES DE PRECIOS / CAN WE MEASURE THE INS…
2009
El desarrollo en los modelos teóricos de microestructura ha motivado la aparición de un grupo de trabajos encaminado al estudio empírico de los costes de transacción y sus componentes dada la importancia que han tenido los mismos en el estudio del funcionamiento de los mercados y la comparación entre éstos así como sus numerosas aplicaciones en campos afines (finanzas corporativas, eficiencia de los mercados, etc.). Por otra parte, la contrastación empírica de los distintos modelos establecidos muestra resultados claramente dispares. Por ello, el objetivo de nuestro trabajo es analizar con detalle y en conjunto dichos modelos centrándonos en un grupo con características muy similares. Concr…
Olley–Pakes productivity decomposition: computation and inference
2016
Summary We show how a moment-based estimation procedure can be used to compute point estimates and standard errors for the two components of the widely used Olley–Pakes decomposition of aggregate (weighted average) productivity. When applied to business level microdata, the procedure allows for autocovariance and heteroscedasticity robust inference and hypothesis testing about, for example, the coevolution of the productivity components in different groups of firms. We provide an application to Finnish firm level data and find that formal statistical inference casts doubt on the conclusions that one might draw on the basis of a visual inspection of the components of the decomposition.
A more efficient second order blind identification method for separation of uncorrelated stationary time series
2016
The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel unmixing matrix estimator which selects the best lag set from a finite set of candidate sets specified by the user. The theory is illustrated by a simulation study.
Deflation-based separation of uncorrelated stationary time series
2014
In this paper we assume that the observed pp time series are linear combinations of pp latent uncorrelated weakly stationary time series. The problem is then to find an estimate for an unmixing matrix that transforms the observed time series back to uncorrelated time series. The so called SOBI (Second Order Blind Identification) estimate aims at a joint diagonalization of the covariance matrix and several autocovariance matrices with varying lags. In this paper, we propose a novel procedure that extracts the latent time series one by one. The limiting distribution of this deflation-based SOBI is found under general conditions, and we show how the results can be used for the comparison of es…
Separation of Uncorrelated Stationary time series using Autocovariance Matrices
2015
Blind source separation (BSS) is a signal processing tool, which is widely used in various fields. Examples include biomedical signal separation, brain imaging and economic time series applications. In BSS, one assumes that the observed $p$ time series are linear combinations of $p$ latent uncorrelated weakly stationary time series. The aim is then to find an estimate for an unmixing matrix, which transforms the observed time series back to uncorrelated latent time series. In SOBI (Second Order Blind Identification) joint diagonalization of the covariance matrix and autocovariance matrices with several lags is used to estimate the unmixing matrix. The rows of an unmixing matrix can be deriv…