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EXTRAPOLATION OF PURCHASING POWER PARITIES USING MULTIPLE BENCHMARKS AND AUXILIARY INFORMATION: A NEW APPROACH
Author(s) -
Rao D. S. Prasada,
Rambaldi Alicia,
Doran Howard
Publication year - 2010
Publication title -
review of income and wealth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.024
H-Index - 57
eISSN - 1475-4991
pISSN - 0034-6586
DOI - 10.1111/j.1475-4991.2010.00386.x
Subject(s) - econometrics , benchmark (surveying) , heteroscedasticity , economics , purchasing power , extrapolation , regression , computer science , statistics , mathematics , macroeconomics , geodesy , geography
The paper presents an econometric framework for the construction of a consistent panel of purchasing power parities (PPPs) which makes it possible to combine all the PPP benchmark data from various phases of the International Comparison Program with the data on national price movements in the form of implicit deflators from national accounts. The method improves upon the current practice used in the construction of the Penn World Tables (PWT), and similar tables produced by the World Bank which tend to be anchored on a selected benchmark. The econometric formulation is based on a regression model for the national price levels where the disturbances are assumed to be heteroskedastic and spatially correlated across countries. The regression model along with data on country specific price movements are combined using a state–space formulation and optimum predictions of PPPs are obtained. As a property of the method presented in the paper, we show that the resulting PPP predictions are weighted averages of extrapolations of PPPs from different benchmarks—thus the method provides a formal approach which has a simple intuitive interpretation. The smoothed PPP predictions (and standard errors) obtained through the state–space are produced for both ICP‐participating and non‐participating countries and non‐benchmark years. A complete tableau of PPPs for 141 countries spanning the period 1970 to 2005 is compiled using the method. Results for some selected countries are presented and the new series are compared and contrasted with the currently available PWT series. Extrapolated series for the remaining countries are available from the authors upon request.