Transaction-price residential (house) and commercial property price indexes (RPPIs and CPPIs) have inherent problems of sparse data on heterogeneous properties, more so CPPIs. In an attempt to control for heterogeneity, (repeat-sales and hedonic) panel data regression frameworks are typically used for estimating overall price change. We address the problem of sparse data, demonstrate the need to include spatial price spillovers to remove bias, and propose an innovative approach to effectively weight regional CPPIs along with improvements to higher-level weighting systems. The study uses spatial panel regressions on granular CPPIs for the United States (US).
The IMF’s main uses of the International Comparison Program’s (ICP) estimates of purchasing power parity (PPP)-adjusted Gross Domestic Product (GDP) are as an element of the formula used to help guide decisions on its members’ quotas and in the World Economic Outlook (WEO). The paper outlines these uses and considers measurement issues particularly salient to IMF usage including: PPP imputations for member countries not participating in the ICP; PPP estimates for non-benchmark years; timeliness and periodicity of PPP estimates; economy groupings; and transparency. The paper was written as a chapter on ?IMF uses of PPPs? for the 2011 ICP Handbook.
Unit value export and import indices compiled from returns to customs authorities are often used as surrogates for price indices in the measurement of inflation transmission, terms of trade (effects), and to deflate import and export value series to derive volume series. Their widespread use is mainly due to their relatively low cost compared with establishment price surveys. This paper provides evidence of substantial bias in their representation of such price changes. Their continued use would mislead economic analysis. The paper considers the efficacy of alternative strategies for their improvement, and argues for a move to establishment-based price surveys.
Consumer price indexes (CPIs) are compiled at the higher (weighted) level using Laspeyres-type arithmetic averages. This paper questions the suitability of such formulas and considers two counterpart alternatives that use geometric averaging, the Geometric Young and the (price-updated) Geometric Lowe. The paper provides a formal decomposition and understanding of the differences between the two. Empirical results are provided using United States CPI data. The findings lead to an advocacy of variants of a hybrid formula suggested by Lent and Dorfman (2009) that substantially reduces bias from Laspeyres-type indexes.
Statistical offices try to match item models when measuring inflation between two periods. However, for product areas with a high turnover of differentiated models, the use of hedonic indexes is more appropriate since they include unmatched new and old models. There are two main competing approaches to hedonic indexes are hedonic imputation (HI) indexes and dummy time hedonic (HD) indexes. This study provides a formal analysis of exactly why the results from the two approaches may differ and discusses the issue of choice between these approaches. An illustrative study for desktop PCs is provided.
A key element in the build-up to the global recession and subsequently was the movement in house price indexes (HPIs). These indexes are particularly prone to methodological and coverage differences which can undermine both within-country and cross-country economic analysis. The paper outlines key measurement issues and reports on empirical work using an international panel data set that (i) considers whether differences in HPI measurement matter and, if so, in what way, and (ii) revisits the measurement of global house price inflation and the modeling of the determinants of house price inflation using HPIs corrected for differences in measurement practice.
Index number theory informs us that if data on matched prices and quantities are available, a superlative index number formula is best to aggregate heterogeneous items, and a unit value index to aggregate homogeneous ones. The formulas can give very different results. Neglected is the practical case of broadly comparable items. This paper provides a formal analysis as to why such formulas differ and proposes a solution to this index number problem.
The 2005 International Comparison Program''s (ICP) estimates of economy-wide purchasing power parity (PPP) are based on parity estimates for 155 basic expenditure headings, mainly estimated using country product dummy (CPD) regressions. The estimates are potentially inefficient and open to omitted variable bias for two reasons. First, they use average prices across outlets as the left-hand-side variable. Second, quality-adjusted prices of non-comparable replacements, required when products in outlets do not match the required specifications, cannot be effectively included. This paper provides an analytical framework based on panel data and hedonic CPD regressions for ameliorating these sources of bias and inefficiency.
This paper provides an overview of statistical measurement issues relating to alternative measures of core inflation, and the criteria for choosing among them. The approaches to measurement considered include exclusion-based methods, imputation methods, limited influence estimators, reweighting, and economic modeling. Criteria for judging which approach to use include credibility, control, deviations from a smoothed reference series, volatility, predictive ability, causality and cointegration tests, and correlation with money supply. Country practice can differ in how the approaches are implemented and how their appropriateness is assessed. There is little consistency in the results of country studies to readily suggest guidelines on accepted methods.
The Consumer Price Index Manual (2004) provides guidelines for aggregation formulas that are promulgated at IMF training courses and technical assistance missions. This paper develops elementary level aggregation theory to better inform users and compilers. Most countries use either the Dutot or Jevons index formula. These formulas generally give different results; advice on choice of formula matters. Using an approach based on sample estimators, and an illustration based on scanner data, the paper shows how differences in these formulas can be explained by changes in price dispersion and, in turn, by product heterogeneity. Implications for choice of formula are considered.