**See also:** Autonomy Autocratic Autonomous Autocracy Autotroph Automation Autobiography Autopsy Autonomously Auto Automated Autophagy Automatic Automobile Autochthonous Autoimmune Autoestima Autonomía

**1.** This is a generalization of longhor1 in which a vector autoregression rather than an autoregression is used to compute ** Autocovariances** of the variables whose lags are in X t − 1.In the exchange rate example (2), one might suppose that sharper estimates of the moments of i t − i t ∗ will result from use

Autoregression, An, Autocovariances, Are

**2.** It is thus more convenient to use the autocorrelations, which are the ** Autocovariances** normalised by dividing by the variance (6.6) The series τ s now has the standard property of correlation coefficients that the values are bounded to lie between ±1

Autocorrelations, Are, Autocovariances

**3.** It seems that I still do not understand properly how ACF works My R calculations > a1 [1] 5.0 1.5 2.0 3.5 1.0 ** Autocovariances** of series ‘a1’, by lag 0 1 2 3 4 2

Acf, Autocovariances

**4.** All the ** Autocovariances** and autocorrelations are zero beyond displacement zero since white noise is uncorrelated over time

All, Autocovariances, And, Autocorrelations, Are

**5.** Which shows that the ** Autocovariances** depend on lag, but not on time

Autocovariances

**6.** Consider the weighted average: Yon=h,x, +hx-1++hxc-n n a) Show that for y the ** Autocovariances** are given by: 7;=22hhyx+ where y, is the j'th autocovariance of x

Average, Autocovariances, Are, Autocovariance

**7.** Shapes of stationary ** Autocovariances** - Volume 43 Issue 4

Autocovariances

**8.** Hierarchical Clustering for Smart Meter Electricity Loads Based on Quantile** Autocovariances** Abstract: In order to

Autocovariances, Abstract, And, Are, Advanced

**9.** Many of the fundamental results in time series analysis depend on the joint asymptotic normality of a fixed number m of the sample *Autocovariances*

Analysis, Asymptotic, Autocovariances

**10.** In this paper a Berry‐Esseen type result is proved for m(n) ** Autocovariances** for m growing at a certain rate.

Autocovariances, At

**11.** 1 day ago · In finance, academics use ** Autocovariances** as a measure of bond/stock illiquidity

Ago, Academics, Autocovariances, As

**12.** Abstract** The problem of testing for the equality of Autocovariances of two independent high-dimensional time series is studied.** Tests based on the suprema or sums of suitable averages across the dimensions are adapted from the available literature

Abstract, Autocovariances, Averages, Across, Are, Adapted, Available

**13.** The method of splitting – essentially computing ** Autocovariances** by convolving long memory and short memory dynamics – is only tractable when a single long memory pole exists

Autocovariances, And

**14.** Theorem tells that for a stationary process with absolutely summable ** Autocovariances**, we can write it as a weighted sum of periodic functions

Absolutely, Autocovariances, As

**15.** We propose an extension called mdSOBI by using multidimensional *Autocovariances*, which can be** cal- culated for data sets with multidimensional parameterizations such as images or fMRI scans.** mdSOBI has the advantage of using the spatial data in all directions, whereas SOBI only uses a …

An, Autocovariances, As, Advantage, All

**16.** The estimator is a linear function of the usual sample ** Autocovariances** computed using the observed demeaned data

Autocovariances

**17.** The idea is to stack the usual sample ** Autocovariances** into a vector and show that the expectation of this vector is a linear combination of population

Autocovariances, And

**18.** Shapes of stationary ** Autocovariances** 1187 TheACF ρ(·) is said to be new better than used if ρ(i+j)≤ ρ(i)ρ(j), i,j+j)≤ ρ(i)ρ(j), i,j

Autocovariances

**19.** Quantile ** Autocovariances** provide information about the serial dependence structure at different pairs of quantile levels, require no moment condition and allow to identify dependence features that covariance-based methods are unable to detect

Autocovariances, About, At, And, Allow, Are

**20.** The multivariate portmanteau test proposed by Hosking (1980) for testing the adequacy of an autoregressive moving average model is based on the first s residual ** Autocovariances** of the fitted model.In practice a value for s is chosen in dependence on the sample size n, mostly s = 20 for n between 50 and 200.

Adequacy, An, Autoregressive, Average, Autocovariances, And

**21.** Stat 8054 Lecture **Notes: Autocovariances** in MCMC Charles J

Autocovariances

**22.** Although the recursive formula for the ** Autocovariances** is well-known, the initialization of this recursion in standard treatments (such as Brockwell and Davis (1991) or Lütkepohl (2007)) is slightly nuanced; we provide explicit formulas and algorithms for the initial

Although, Autocovariances, As, And, Algorithms

**23.** All the proposed approaches take advantage of the high capability of the quantile** Autocovariances to discriminate between independent realizations from a broad range of stationary processes, including** linear, non-linear and conditional heteroskedastic models.

All, Approaches, Advantage, Autocovariances, And

**24.** This article studies tests for assessing whether two stationary and independent time series have the same dynamics – specifically, whether the ** Autocovariances** of both series coincide at all lags

Article, Assessing, And, Autocovariances, At, All

**25.** 5 Asymptotic Variance and *Autocovariances*

Asymptotic, And, Autocovariances

**26.** ** Autocovariances** of long-memory time series 407 We assume ∞ j=0 ψ2(j)<∞

Autocovariances, Assume

**27.** The problem of testing for the equality of ** Autocovariances** of two independent high-dimensional time series is studied

Autocovariances

**28.** Is the Use of ** Autocovariances** in Level the Best in Estimating the Income Processes? A Simulation Study Tak Wai Chau * School of Economics Shanghai University of Finance and Economics 777, Guoding Road, Yangpu District Shanghai, 200433 China Abstract In this simulation study, I compare the efficiency and finite sample bias of param-eter estimators for popular income dynamic models using …

Autocovariances, And, Abstract

**29.** Limit the number of ** Autocovariances** returned

Autocovariances

**30.** Setting nlag when fft is False uses a simple, direct estimator of the ** Autocovariances** that only computes the first nlag + 1 values

Autocovariances

**31.** This can be much faster when the time series is long and only a small number of ** Autocovariances** are needed

And, Autocovariances, Are

**32.** The tables consist of theoretical ** Autocovariances** calculated using the methods described earlier

Autocovariances

**33.** We suggest using a bound for ARMA ** Autocovariances**, setting m as follows: given j; m is the value such that c cðhÞ < 1 Â 10 Àj 8 h : jhj > m; jP6.

Arma, Autocovariances, As

**34.** ** Autocovariances** are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations with autocorrelation function (ACF) plots being an integral visualization tool for performance assessment

Autocovariances, Are, Autocorrelation, Acf, An, Assessment

**35.** If x is an M × N matrix, then xcov (x) returns a (2M – 1) × N2 matrix with the** Autocovariances** and cross-covariances of the columns of x

An, Autocovariances, And

**36.** When fft is False uses a simple, direct estimator of the ** Autocovariances** that only computes the first nlag + 1 values

Autocovariances

**37.** This can be much faster when the time series is long and only a small number of ** Autocovariances** are needed

And, Autocovariances, Are

**38.** In this question, we derive the ** Autocovariances** of an AR(1) process using the Yule-Walker equation:s (a) Recall that AR(1) can be written as From these expressions, derive or (b) From the MA representation, show that a2 for j 0 for j 1,2, (c) Derive the Yule-Walker equations (d) Calculate (70, ?1, ) from equations in (c)

Autocovariances, An, Ar, As

**39.** A Note on Calculating ** Autocovariances** of Periodic ARMA Models

Autocovariances, Arma

**40.** To estimate the spectrum width from 0,1-lag ** Autocovariances**, the noise power N needs to be subtracted from the signal power (at lag 0) as seen in

Autocovariances, At, As

**41.** ** Autocovariances** are not assumed zero, because we do not process the data with the optimal ﬁlter, which is unknown

Autocovariances, Are, Assumed

**42.** March 2018 Multivariate integer-valued time series with flexible ** Autocovariances** and their application to major hurricane counts

Autocovariances, And, Application

**43.** If x is an M × N matrix, then xcov(x) returns a (2M – 1) × N 2 matrix with the ** Autocovariances** and cross-covariances of the columns of x

An, Autocovariances, And