Long Memory in Economics by Gilles Teyssière, Alan P. Kirman

By Gilles Teyssière, Alan P. Kirman

While utilising the statistical conception of lengthy variety based (LRD) methods to economics, the robust complexity of macroeconomic and fiscal variables, in comparison to commonplace LRD strategies, turns into obvious. for you to get a greater figuring out of the behaviour of a few fiscal variables, the ebook assembles 3 varied strands of lengthy reminiscence research: statistical literature at the houses of, and checks for, LRD procedures; mathematical literature at the stochastic techniques concerned; types from fiscal concept delivering believable micro foundations for the occurence of lengthy reminiscence in economics. each one bankruptcy of the ebook will provide a complete survey of the state-of-the-art and the instructions that destiny advancements tend to take. Taken as a complete the booklet offers an summary of LRD procedures that is obtainable to economists, econometricians and statisticians.

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Long Memory in Economics

While utilising the statistical conception of lengthy diversity established (LRD) strategies to economics, the powerful complexity of macroeconomic and fiscal variables, in comparison to common LRD strategies, turns into obvious. for you to get a greater figuring out of the behaviour of a few financial variables, the booklet assembles 3 assorted strands of lengthy reminiscence research: statistical literature at the homes of, and checks for, LRD approaches; mathematical literature at the stochastic techniques concerned; versions from monetary concept offering believable micro foundations for the occurence of lengthy reminiscence in economics.

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Granger, C. W. J. and Hyung, N. (1999). Occasional structural breaks and long memory. Discussion paper 99-14, Department of Economics. University of California, San Diego. 58. Hall, P. and Yao, Q. (2003). Inference in ARCH and GARCH models with heavy-tailed errors. Econometrica, 71, 285–317. 59. Hamilton, J. D. and Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64, 307–333. 60. Harvey, A. (1998). Long memory in stochastic volatility.

On the other hand, the limit process in (51) has independent increments while the summands have (covarance) long memory, meaning that this long memory does not persist in the distributional limit. A similar lack of persistence of long memory seems characteristic to some other econometric models, in particular to Parke’s (1999) model (see Davidson and Sibbertsen, 2002). Similar properties were proved in Mikosch et al. (2002) for some models arising in telecommunications. A general renewal regime switching scheme leading to a similar ”increase of variability” and stable limit distribution of partial sums is discussed in Leipus et al.

Journal of Time Series Analysis, 22, 197–220. 45. , Kokoszka, P. and Leipus, R. (2000a). Stationary ARCH models: dependence structure and Central Limit Theorem. Econometric Theory, 16, 3–22. 46. , Leipus, R. and Teyssi`ere, G. (2000b). Semiparametric estimation of the intensity of long memory in conditional heteroskedasticity. Statistical Inference for Stochastic Processes, 3, 113–128. 47. , Robinson, P. M. and Surgailis, D. (2000c). A model for long memory conditional heteroskedasticity. Annals of Applied Probability, 10, 1002–1024.

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