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About 10,000 papers for “serial correlation”

Serial Correlation

O. D. Anderson · Wiley StatsRef: Statistics Reference Online · 2014

Cited by 1

Serial Correlation

O. D. Anderson · Encyclopedia of Statistical Sciences · 2005

Cited by 0

Serial correlation and serial dependence

Yongmiao Hong · Palgrave Macmillan UK eBooks · 2010

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 serial correlation is also an important implication of many economic theories and economic hypotheses. For example, the efficient market hypothesis implies that asset returns are an martingale difference sequence (m.d.s.), and so are serially uncorrected. More generally, rational expectations theory implies that the expectational errors of the economic agent are serially uncorrelated. In this article we first discuss various tests for serial correlation, for both estimated model residuals and observed raw data, and we discuss their relationships. We then discuss serial dependence in a nonlinear time series context, introducing related measures and tests for serial dependence.

Cited by 9

Serial Correlation and Serial Dependence

Yongmiao Hong · The New Palgrave Dictionary of Economics · 2018

In this article we discuss serial correlation in a linear time series regression context and serial dependence in a nonlinear time series context. We first discuss various tests for serial correlation for both estimated regression residuals and observed raw data. Particular attention is paid to the impact of parameter estimation uncertainty and conditional heteroskedasticity on the asymptotic distribution of test statistics. We discuss the drawback of serial correlation in nonlinear time series models and introduce a number of measures that can capture nonlinear serial dependence and reveal useful information about serial dependence.

Cited by 3

Serial correlation coefficients.

Christoph Bauermeister (446113), Tilo Schwalger (358716), David F. Russell (197345) et al. · Figshare · 2015

<p><b>A</b>: SCC value (ranging from −1 to +1) as a function of the lag of interspike intervals, for different values of the frequency ratio: (A1), (A2), (A3), (A4), (A5), or (A6). Parameters: , , and . Dots: simulation. Lines: theory. <b>B</b>: SCC at lag , , for highly coherent harmonic noise, as a function of . Parameters: , , . Theoretical curves were computed from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003170#pcbi.1003170.e077" target="_blank">Eq.(11)</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003170#pcbi.1003170.e087" target="_blank">Eq.(12)</a>.</p>

Cited by 0Open access

On Serial Correlation

S. James Press · The Annals of Mathematical Statistics · 1969

Define \begin{equation*}\tag{1.1}P = U/V \equiv \sum^m_{i=1}\lambda_ix_i^2/\sum^r_{i=1}x_i^2,\quad 1 \leqq m \leqq r,\end{equation*} where the $x_i$'s are independent and identically distributed as $N(0, 1)$, and $0 < \lambda_1 \leqq \lambda_2 \leqq \cdots \leqq \lambda_m$. We will give various representations for the distribution of $P$, and show how a special case of this distribution is useful for testing for correlation in a time series. We will also consider independence of the errors in the normal regression problem. Let $n$ be the number of observations, and $k$ the number of coefficient parameters in a univariate linear regression model. Take $r = n - k$ and $m = r - 1$. Suppose the $x_i$'s are the linear functions of the observations of the dependent variable obtained by a "Theil transformation" (see Theil [15]). It will be shown in Section 2 that under these conditions, $P$ is an appropriate test statistic for independence. In Section 3, two different characteristic function representations are developed for $P$; one involves an infinite series of complex valued gamma functions, while the other involves a doubly infinite series of real valued gamma functions. Section 4 discusses the fact that for $r = m + 1, P$ is distributed as a linear combination of correlated beta variates. An appropriate beta distribution approximation is given for comparison. A numerical tabulation of the distribution of $P$ for the case of $r = m + 1$ is in preparation. The cdf. of $P$ is found by numerically inverting the characteristic function of a related linear combination of central chi-square variates.

Cited by 7Open access

AND SERIAL CORRELATION

Craig W. Holden, Avanidhar Subrahmanyam, Mike Fishman et al. · 2000

eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide.

Cited by 0

Serial Correlation Robust LM

Jingjing Yang, Timothy J. Vogelsang

We analyze Lagrange Multiplier (LM) tests for a shift in trend of a univariate time series at an unknown date. We focus on the class of LM statistics based on nonparametric kernel estimates of the long run variance. Extending earlier work for models with nontrending data, we develop a fixed-b asymptotic theory for the statistics. The fixed-b theory suggests that, for a given statistic, kernel, and significance level, there usually exists a bandwidth such that the fixed-b asymptotic critical value is the same for both I(0) and I(1) errors. These “robust” bandwidths are calculated using simulation methods for a selection of well-known kernels. We find when the robust bandwidth is used, the supremum statistic configured with either the Bartlett or Daniell kernel gives LM tests with good power. When testing for a slope change, we obtain the surprising finding that less trimming of potential shift dates leads to higher power, which contrasts the usual relationship between trimming and power. Finite sample simulations indicate that the robust LM statistics have stable size with good power.

Cited by 0

Serial Correlation Robust LM

Jingjing Yang, Timothy J. Vogelsang, Dek Terrell et al.

We analyze Lagrange Multiplier (LM) tests for a shift in trend of a univariate time series at an unknown date. We focus on the class of LM statistics based on nonparametric kernel estimates of the long run variance. Extending earlier work for models with nontrending data, we develop a fixed-b asymptotic theory for the statistics. The fixed-b theory suggests that, for a given statistic, kernel, and significance level, there usually exists a bandwidth such that the fixed-b asymptotic critical value is the same for both I(0) and I(1) errors. These “robust” bandwidths are calculated using simulation methods for a selection of well-known kernels. We find when the robust bandwidth is used, the supremum statistic configured with either the Bartlett or Daniell kernel gives LM tests with good power. When testing for a slope change, we obtain the surprising finding that less trimming of potential shift dates leads to higher power, which contrasts the usual relationship between trimming and power. Finite sample simulations indicate that the robust LM statistics have stable size with good power.

Cited by 0

Serial correlation test results.

Dagmara Panas (396915), Urszula Malinowska (396916), Tadeusz Piotrowski (396917) et al. · Figshare · 2015

&lt;p&gt;Results of Wald-Wolfowitz serial correlation test. 0 indicates that the null hypothesis cannot be rejected, i.e. the intervals are random and independent; 1 indicates rejection of the null hypothesis, i.e. there is indication of serial dependence.&lt;/p&gt;

Cited by 0Open access