Estimation of ARMA Models

Klaus Neusser ()

Chapter 5 in Time Series Econometrics, 2016, pp 87-108 from Springer

Abstract: Abstract The specification and estimation of an ARMA(p,q) model ARMA model estimation ARMA model identification for a given realization involves several intermingled steps. First one must determine the orders p and q. Given the orders one can then estimate the parameters ϕ j , $$\theta _{j}$$ and $$\sigma ^{2}$$ . Finally, the model has to pass several robustness checks in order to be accepted as a valid model. These checks may involve tests of parameter constancy, forecasting performance or tests for the inclusion of additional exogenous variables. This is usually an iterative process in which several models are examined. It is rarely the case that one model imposes itself. All too often, one is confronted in the modeling process with several trade-offs, like simple versus complex models or data fit versus forecasting performance. Finding the right balance among the different dimensions therefore requires some judgement based on experience.

Keywords: Likelihood Function; Bayesian Information Criterion; Maximum Likelihood Estimator; Forecast Performance; ARMA Model (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-319-32862-1_5

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DOI: 10.1007/978-3-319-32862-1_5

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