Estimating TFP in the Presence of Outliers and Leverage Points: An Examination of the KLEMS Dataset
Economic Analysis (EA) Research Paper Series from Statistics Canada, Analytical Studies Branch
This paper examines the effect of aberrant observations in the Capital, Labour, Energy, Materials and Services (KLEMS) database and a method for dealing with them. The level of disaggregation, data construction and economic shocks all potentially lead to aberrant observations that can influence estimates and inference if care is not exercised. Commonly applied pre-tests, such as the augmented Dickey-Fuller and the Kwaitkowski, Phillips, Schmidt and Shin tests, need to be used with caution in this environment because they are sensitive to unusual data points. Moreover, widely known methods for generating statistical estimates, such as Ordinary Least Squares, may not work well when confronted with aberrant observations. To address this, a robust method for estimating statistical relationships is illustrated.
Keywords: Statistical methods; Economic accounts; Time series; Data analysis; Productivity accounts (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:stc:stcp5e:2007047e
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