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Serial Correlation and Serial Dependence [PDF]

, 2008
Serial correlation and serial dependence have been central to time series econometrics. The existence of serial correlation complicates statistical inference of econometric models; and in time series analysis, inference of serial correlation, or more generally, serial dependence, is crucial to characterize the dynamics of time series processes. Lack of
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Serial Correlation

Psychometrika, 1946
Formulas are presented for triserial correlation, quadriserial correlation, etc., and for serial correlation in general. These formulas are based on well-known procedures outlined by Kelley, Peters and Van Voorhis, and others, and involve Pearson's correction for “broad categories.” The formula for biserial correlation also may be developed following ...
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SERIAL CORRELATION IN MULTIREGIONAL MIGRATION MODELS*

Journal of Regional Science, 1990
ABSTRACTIn this paper, we outline the specification and estimation of a time series of multiregional net‐migration equations subject to first‐order serial correlation. We show that the necessary nonstochastic adding‐up constraint, which requires that net migration in the system sum to zero, imposes restrictions on the serial‐correlation coefficients ...
D K, Foot, W J, Milne
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Serial correlation of detrended time series

Physical Review E, 2008
A preliminary essential procedure in time series analysis is the separation of the deterministic component from the random one. If the signal is the result of superposing a noise over a deterministic trend, then the first one must estimate and remove the trend from the signal to obtain an estimation of the stationary random component.
Călin, Vamoş, Maria, Crăciun
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Serial transform optical correlator design principles

Applied Optics, 2001
Optical correlators such as the 4f and VanderLugt optical systems have been an active area of research for many years; we refer to these types of optical system collectively as serial transform correlators (STCs). Despite being well known, misconceptions regarding the design of STCs are not uncommon.
M J, O'Callaghan, S H, Perlmutter
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Tests for Serial Correlation in Regression Models with Lagged Dependent Variables and Serially Correlated Errors

Econometrica, 1973
The paper compares the power of two tests for serial correlation in regression models with lagged dependent variables, recently suggested by Durbin, with that of the likelihood ratio test by means of two sets of Monte-Carlo experiments-one in which the exogenous series is taken to be the quarterly GNP series for the USA and the other in which the ...
Maddala, G S, Rao, A S
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Multiple Bi-Serial and Multiple Point Bi-Serial Correlation

Psychometrika, 1947
Normal equations, using data in various forms, are presented for securing the regression weights for prediction of a dichotomized criterion, and a simplified equation for the estimation of the multiple bi-serial or multiple point bi-serial, depending upon the proper assumption as to the nature of the distribution of the criterion, on the basis of these
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Heteroscedasticity and serial correlation

1986
There are many situations occurring in practice when the simple structure of random variation assumed in (1.1) does not hold; examples will be given below. Suppose that instead of (1.1) we have $$\left. {{}_{V\left( Y \right)\, = \,V{\sigma ^2},}^{E\left( Y \right)\, = \,a\theta ,}} \right\}$$ (9.1) where V is a known n × n positive definite ...
G. Barrie Wetherill, P. Duncombe, M. Kenward, J. Köllerström, S. R. Paul, B. J. Vowden   +5 more
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with Serially Correlated Inflows

Technometrics, 1963
This paper outlines a method of extending Moran's theory of finite reservoirs so as to take account of serial correlation in the sequence of inflows. The technique developed is to assume that the structure of this sequence can be adequately approximated by a Markov chain, and then to work with the bivariate Markov process describing the joint ...
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Robust designs for serially correlated observations

Biometrika, 1989
The construction of optimal experimental designs is based on a special model and on distributional assumptions for the errors of this model. Usually identically and independently distributed error terms are assumed. But successive observations are likely to be correlated, while observations that are further apart are likely not to be.
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