autocorrelation

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autocorrelation

A measure of how closely a signal in a time series resembles a time-delayed image of itself—periodic signals are highly autocorrelated; random signals are not.
References in periodicals archive ?
I now choose intrinsic persistence, [rho], and the relative volatility of the NKPC shock, [[sigma].sub.u]/[[sigma].sub.s], to match the sample first-order autocorrelation coefficient of inflation, Corr ([^.[pi].sub.t], [^.[pi].sub.t-1]) = 0.88, and the contemporaneous correlation of inflation and marginal cost, Corr ([^.[pi].sub.t], [^.s.sub.t]) = 0.33.
For detection of the deterministic components masked in a random background the autocorrelation function (ACF) may be used, because autocorrelation functions of deterministic data persist over all time displacements, while autocorrelation functions of stochastic processes tend to zero for large time displacement.
As discussed by various authors that various economic players at times do follow the same trends because of the presence of significant autocorrelation. Moreover an economy also observes the possible co-movements and cross-correlations of two or more trends such as a fall in the aggregate demand would mean less consumption of goods and services leading to laying off workers.
(7) Another well-known measure of spatial autocorrelation is Getis and Ord's G statistic [see Anselin (1995a), p.
For a real Gaussian process Van Vleck and Middleton [13] have shown that the autocorrelation coefficient ([R.sub.t] with t = [t.sub.2] - [t.sub.1]) of the output from a hard limiter is related with the input autocorrelation coefficient (here denoted r) by the well-known arcsine law:
The sample autocorrelation functions are analyzed in order to examine the reasons for the lack of independence.
The relation between the oscillation frequency and autocorrelation is defined by power spectrum.
Sarwate, "Bounds on crosscorrelation and autocorrelation of sequences," IEEE Transactions on Information Theory, vol.
A zero mean, single mean, and trend ADF along with autocorrelation, partial autocorrelation and cross correlation diagnostics can be used.
In ARIMA modeling the order of AR(p) is identified by partial autocorrelation function (PACF) while the order of MA(q) is identified by autocorrelation function (ACF) (Tsay 2002).
For 30 and 60 minute price distributions we recognize that the volatility autocorrelation is low and due to the significant departures in the price distribution we consider a non-clustering mechanism to be the key reason for the departures from normality.
Our method, which involves a two-tiered estimation of the autocorrelation function to refine its estimate, decreases the root mean square error (RMSE) of the estimated effective sample size by as much as a factor of 3-4 compared to the original formula for AR1 time series with large persistence (i.e., large lag-1 autocorrelation coefficient).