scholarly journals Comments on the presence of serial correlation in the random coefficients of an autoregressive process

2021 ◽  
Vol 170 ◽  
pp. 108988
Author(s):  
Frédéric Proïa ◽  
Marius Soltane
Keyword(s):  
Serial Correlation ◽  
Autoregressive Process ◽  
Random Coefficients

scholarly journals On the number of segregating sites for populations with large family sizes

2006 ◽  
Vol 38 (03) ◽  
pp. 750-767 ◽  
Author(s):  
M Möhle
Keyword(s):  
Large Family ◽  
Autoregressive Process ◽  
Random Coefficients ◽  
Exponential Integral ◽  
Genealogical Tree ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Multiple Collisions ◽  
Explicit Representations ◽  
Large Families

We present recursions for the total number,Sn, of mutations in a sample ofnindividuals, when the underlying genealogical tree of the sample is modelled by a coalescent process with mutation rater>0. The coalescent is allowed to have simultaneous multiple collisions of ancestral lineages, which corresponds to the existence of large families in the underlying population model. For the subclass of Λ-coalescent processes allowing for multiple collisions, such that the measure Λ(dx)/xis finite, we prove thatSn/(nr) converges in distribution to a limiting variable,S, characterized via an exponential integral of a certain subordinator. When the measure Λ(dx)/x2is finite, the distribution ofScoincides with the stationary distribution of an autoregressive process of order 1 and is uniquely determined via a stochastic fixed-point equation of the formwith specific independent random coefficientsAandB. Examples are presented in which explicit representations for (the density of)Sare available. We conjecture thatSn/E(Sn)→1 in probability if the measure Λ(dx)/xis infinite.

scholarly journals On the number of segregating sites for populations with large family sizes

2006 ◽  
Vol 38 (3) ◽  
pp. 750-767 ◽  
Author(s):  
M Möhle
Keyword(s):  
Large Family ◽  
Autoregressive Process ◽  
Random Coefficients ◽  
Exponential Integral ◽  
Genealogical Tree ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Multiple Collisions ◽  
Explicit Representations ◽  
Large Families

We present recursions for the total number, Sn, of mutations in a sample of n individuals, when the underlying genealogical tree of the sample is modelled by a coalescent process with mutation rate r>0. The coalescent is allowed to have simultaneous multiple collisions of ancestral lineages, which corresponds to the existence of large families in the underlying population model. For the subclass of Λ-coalescent processes allowing for multiple collisions, such that the measure Λ(dx)/x is finite, we prove that Sn/(nr) converges in distribution to a limiting variable, S, characterized via an exponential integral of a certain subordinator. When the measure Λ(dx)/x2 is finite, the distribution of S coincides with the stationary distribution of an autoregressive process of order 1 and is uniquely determined via a stochastic fixed-point equation of the form with specific independent random coefficients A and B. Examples are presented in which explicit representations for (the density of) S are available. We conjecture that Sn/E(Sn)→1 in probability if the measure Λ(dx)/x is infinite.

scholarly journals Interdecadal Trend and ENSO-Related Interannual Variability in Southern Hemisphere Blocking

2008 ◽  
Vol 21 (12) ◽  
pp. 3068-3077 ◽  
Author(s):  
Li Dong ◽  
Timothy J. Vogelsang ◽  
Stephen J. Colucci
Keyword(s):  
Statistical Model ◽  
White Noise ◽  
Interannual Variability ◽  
Southern Hemisphere ◽  
Serial Correlation ◽  
Error Term ◽  
Autoregressive Process ◽  
Atmospheric Blocking ◽  
White Noise Process ◽  
The Past

Abstract The interdecadal trend and ENSO-related interannual variability in the frequency and intensity of atmospheric blocking in the Southern Hemisphere are analyzed by a statistical model that takes account of serial correlation in the datasets. Results suggest that an autoregressive process AR(1) fits the error term of the Southern Hemisphere blocking occurrence series, and a white-noise process AR(0) fits the error term of the Southern Hemisphere blocking intensity series reasonably well. It is found that the Southern Hemisphere blocking days have decreased over the past 52 yr (1948–99) but with an enhanced intensity. In addition, the Southern Hemisphere atmospheric blocking is found to occur more frequently in the warm phase of ENSO cycles, whereas the intensity of the Southern Hemisphere atmospheric blocking does not appear to be affected by ENSO cycles.

Exact tests for serial correlation in vector processes

1956 ◽  
Vol 52 (3) ◽  
pp. 482-487 ◽  
Author(s):  
E. J. Hannan
Keyword(s):  
Multivariate Analysis ◽  
Canonical Correlation ◽  
Serial Correlation ◽  
Autoregressive Process ◽  
Second Order ◽  
Exact Tests ◽  
Serial Independence ◽  
Auto Regression

ABSTRACTExact tests for serial independence in vector Markoff processes have been obtained by considering the system of regressions of the vectors observed at time 2t on the vectors observed at times (2t − 1) and (2t + 1). The tests then reduce to those obtained from canonical correlation procedures in multivariate analysis. Two particular cases are(1) The test for serial independence of a vector of residuals from a system of regressions in which the regressors are all independent of the residuals. At the same time a test of the hypothesis that the regressor and regrediend vectors are independent is obtained.(2) The test for serial correlation or partial serial correlation in a multiple Markoff process (autoregressive process).An investigation of the efficiency of the test so obtained, of the hypothesis that a process is a simple Markoff process (against the alternative that the process is a second-order auto-regression) suggests that the efficiency of all of these tests will be low.

Refining financial analysts’ forecasts by predicting earnings forecast errors

2017 ◽  
Vol 25 (2) ◽  
pp. 256-272 ◽  
Author(s):  
Tatiana Fedyk
Keyword(s):  
Serial Correlation ◽  
Design Methodology ◽  
Financial Analysts ◽  
Earnings Forecasts ◽  
Autoregressive Process ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Content Type ◽  
Quarterly Earnings ◽  
Prior Literature

Purpose The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors. Design/methodology/approach Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors. Findings Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus. Originality/value These refinements were not presented in prior literature and can be useful to financial analysts and investors.

Serial Correlation between the Ultrasonographic and Pathologic Findings of Intramuscular Hemorrhaging in an Experimental Rabbit

2008 ◽  
Vol 58 (5) ◽  
pp. 519 ◽  
Author(s):  
Kyungran Ko ◽  
Kyung Nam Ryu ◽  
Ji Seon Park ◽  
Wook Jin ◽  
Dong Wook Sung ◽  
...  
Keyword(s):  
Serial Correlation ◽  
Pathologic Findings

On the generation and origin of I/f noise

1999 ◽  
Vol 4 ◽  
pp. 87-96 ◽  
Author(s):  
B. Kaulakys ◽  
T. Meškauskas
Keyword(s):  
Point Process ◽  
Solvable Model ◽  
Autoregressive Process ◽  
Wide Range ◽  
Occurrence Times

Simple analytically solvable model exhibiting 1/f spectrum in any desirably wide range of frequency is analysed. The model consists of pulses (point process) whose interevent times obey an autoregressive process with small damping. Analysis and generalizations of the model indicate to the possible origin of 1/f noise, i.e. random increments between the occurrence times of particles or pulses resulting in the clustering of the pulses.

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