1999
DOI: 10.1111/1467-9957.00148
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Testing for Purchasing Power Parity: Econometric Issues and an Application to Developing Countries

Abstract: There is now a vast literature on testing purchasing power parity (PPP). Any test is conditional on a particular econometric speci¢cation which embodies a set of auxiliary assumptions. This paper reviews the issues involved in econometric speci¢cation and estimation in the time series and panel models used to test PPP. We start from a general model and then systematically examine the implicit restrictions that are imposed to obtain the standard procedures and discuss the implications of these procedures for es… Show more

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Cited by 51 publications
(21 citation statements)
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“…13 See Boyd and Smith (1999), who conclude there is very little heterogeneity in real exchange rates dynamics between countries.…”
Section: Some Intuitionmentioning
confidence: 99%
“…13 See Boyd and Smith (1999), who conclude there is very little heterogeneity in real exchange rates dynamics between countries.…”
Section: Some Intuitionmentioning
confidence: 99%
“…Coakley et al (1996), the stationarity of the real exchange rate e t in the purchasing power parity literature, e.g. Boyd and Smith (1999), and the stationarity of deviations of domestic from foreign output, y t = y* t in the convergence literature, e.g. Lee et al (1997).…”
Section: The Modelmentioning
confidence: 99%
“…The econometric issues which arise when testing for PPP are analysed by Boyd and Smith (1999). They emphasize that, although PPP does appear to hold in the long run (in the developing countries, in their case) there are major difficulties in measuring the speed of convergence in cross-country panels, unless data for very long time periods are available and the structural parameters are constant, both across countries and over time.…”
mentioning
confidence: 99%
“…The literature has identified several other biases that affect both methodologies, such as the time aggregation bias (see, e.g., Choi et al 2006) and the lagged dependent variable bias (see, e.g., Boyd and Smith, 1999). We do not address these biases here.…”
Section: A2 Monte Carlo Simulationsmentioning
confidence: 99%