Estimating TFP in the Presence of Outliers and Leverage Points: An Examination of the KLEMS Dataset
AbstractThis 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.
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Bibliographic InfoPaper provided by Statistics Canada, Analytical Studies Branch in its series Economic Analysis (EA) Research Paper Series with number 2007047e.
Date of creation: 05 Dec 2007
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Data analysis; Economic accounts; Productivity accounts; Statistical methods; Time series;
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