A Note on Michelacci and Zaffaroni, Long Memory, and Time Series of Economic Growth
Gerald Silverberg () and
No 31, Research Memorandum from Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT)
In a recent paper in The Journal of Monetary Economics, Michelacci and Zaffaroni (2000)estimate long memory parameters for GDP per capita of 16 OECD countries. In this note weargue that these estimations are questionable for the purposes of clarifying the time seriesproperties of these data (presence of unit roots, mean reversion, long memory) because theauthors a) filter out a deterministic linear-in-logs trend instead of first-differencing in logs,and manipulate the data in other highly questionable ways, b) rely on the semiparametricGeweke and Porter-Hudak (GPH) method as modified by Robinson, which is known to behighly biased in small samples. We re-examine these results using Berans nonparametricFGN estimator and Sowells exact maximum likelihood ARFIMA estimator. These methodsavoid the small-sample bias and arbitrariness of the cut-off parameters of Robinsons methodand allow us to control for short memory effects, although the parametric ARFIMA estimatorintroduces specification problems of its own. We also look at the influence of the choice ofsub-periods on the results. Finally, we apply Robinsons method to our treatment of the dataand show that MZs results no longer hold, nor are their cut-off parameter and filteringinsensitivity claims substantiated.
Keywords: economics of technology (search for similar items in EconPapers)
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