2017
DOI: 10.1111/bmsp.12109
|View full text |Cite
|
Sign up to set email alerts
|

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques

Abstract: Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such as the 1/T approximation method and the Bartlett's formula method may fail in finite samples and are vulnerable to non-normality. Resampling techniques such as the mo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 12 publications
(5 citation statements)
references
References 32 publications
0
4
0
1
Order By: Relevance
“…Testosterone, LH and FSH distribution was evaluated considering the date of examination by autocorrelation analyses. Autocorrelation functions were first calculated as lag 1, which is the correlation between adjacent observations in a time series (15,16). Autocorrelation function represents the statistical approach to measure the linear relationship between an observation at specific time and the observations at previous times.…”
Section: Discussionmentioning
confidence: 99%
“…Testosterone, LH and FSH distribution was evaluated considering the date of examination by autocorrelation analyses. Autocorrelation functions were first calculated as lag 1, which is the correlation between adjacent observations in a time series (15,16). Autocorrelation function represents the statistical approach to measure the linear relationship between an observation at specific time and the observations at previous times.…”
Section: Discussionmentioning
confidence: 99%
“…Jika nilai VIF < 10 dan sebaliknya maka tidak terjadi multikolinieritas (Setyadharma, 2010). Uji Autokorelasi bertujuan untuk menguji apakah terdapat korelasi antara kesalahan pengganggu pada periode t dengan periode t-1 atau sebelumnya didalam model regresi (Chen, 2016;Ke & Zhang, 2018) Hal tersebut dapat dilihat melalui nilai residual (prediction error) dari uji Durbin-Watson (Durbin & Watson, 1950;Vinod, 1973) Apabila uji DW menunjukkan angka -2< dw <2 maka tidak terjadi autokorelasi (Gujarati et al, 2010). Uji heteroskedastisitas bertujuan untuk mengetahui apakah dalam suatu model regresi terdapat perbedaan atau persamaan varians dari residual satu pengamatan ke pengamatan yang lain (Carapeto & Holt, 2003;Shen, Cui, & Wang, 2014).…”
Section: Metode Penelitianunclassified
“…Autocorrelation and Partial autocorrelation are the time series techniques to measure the linear relationship between lagged values of the time series. The higher the deviation of these coefficients from zero, indicates more dependency of the series at a specific time with its lag values (18,19).…”
Section: Autocorrelation and Partial Autocorrelationmentioning
confidence: 99%