ARIMA Modeling Research Paper

ARIMA Modeling
A look at time series analysis, focusing on the autoregressive integrated moving average (ARIMA) technique.
# 129205 | 2,610 words | 6 sources | APA | 2004 | US
Published on Sep 17, 2010 in Sociology (General) , Mathematics (General)


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Description:

This paper discusses ARIMA modeling as a technique for analyzing time series data. First, the paper introduces the topic and then proceeds to analyze the random shocks (or stochastic component) that lie at the core of ARIMA modeling. Finally, the paper addresses the intervention component--the actual effect of the intervention on the time series, focusing on the types of effects that occur in time series data.

Outline:
Introduction
ARIMA modeling
Conclusion

From the Paper:

"There are a number of ways to deal with the problem of autocorrelation. One of the easier and more practical ways is to control for it by modeling it. Autocorrelation is one form of noise that, if not controlled, will confound the analysis. Noise may also result from trend (tendency for the series to drift up or down), seasonality (systematic cyclical 'spikes'), or random error (around a mean level) (McDowall, McCleary, Meidinger, & Hay, 1980). By modeling these potential sources of variance, ARIMA modeling accounts for all sources of noise, thereby enabling a rigorous analysis of the effect of the intervention. The general ARIMA model is delineated as Yt = Nt + It, where Yt is the time series, Nt is noise, and It is the intervention."

Sample of Sources Used:

  • Britt, C.L; Kleck, G.; & Bordua, D.J. (1996). A reassessment of the D.C. gun law: some cautionary notes on the use of interrupted time series designs for policy impact assessment. Law & Society Review, v. 30, 2, 361-380.
  • Chamlin, M.B. & Cochran, J.K. (1998). Causality, economic conditions, & burglary. Criminology, v. 36, 2, 425-440.
  • McDowall, D.; McCleary, R.; Meidinger, E.E.; & Hay, R.A., Jr. (1980). Interrupted time series analysis. Sage Publications: Newbury Park, CA.
  • McFarland, S.G. (1983). Is capital punishment a short-term deterrent to homicide? A study of the effects of four recent American executions. Journal of Criminal Law & Criminal Justice, v. 74, 3, 1014-1032.
  • Shadish, W.R; Cook, T.D.; & Campbell, D.T. (2003). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company: Boston.

Cite this Research Paper:

APA Format

ARIMA Modeling (2010, September 17) Retrieved September 19, 2021, from https://www.academon.com/research-paper/arima-modeling-129205/

MLA Format

"ARIMA Modeling" 17 September 2010. Web. 19 September. 2021. <https://www.academon.com/research-paper/arima-modeling-129205/>

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