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Econometric Modelling with Time Series

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  • Martin,Vance
  • Hurn,Stan
  • Harris,David

Abstract

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

Suggested Citation

  • Martin,Vance & Hurn,Stan & Harris,David, 2013. "Econometric Modelling with Time Series," Cambridge Books, Cambridge University Press, number 9780521196604.
  • Handle: RePEc:cup:cbooks:9780521196604
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    Cited by:

    1. Souček, Michael & Todorova, Neda, 2013. "Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach," Energy Economics, Elsevier, vol. 40(C), pages 586-597.
    2. David Harris & Hsein Kew, 2014. "Portmanteau Autocorrelation Tests Under Q-Dependence And Heteroskedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 203-217, May.
    3. Alonso-Rodriguez, Agustin, 2017. "The CO2 emissions in Finland, Norway and Sweden: a dynamic relationship," EconStor Preprints 171259, ZBW - German National Library of Economics.
    4. repec:gam:jijfss:v:6:y:2018:i:2:p:45-:d:142628 is not listed on IDEAS

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