A failure in the measurement of inflation: results from a hedonic and matched experiment using scanner data

Statistical offices use the matched models method to compile consumer price indices (CPIs) to measure inflation. The prices of a sample of models are recorded, and then price collectors visit the same stores each subsequent month to record the prices of the same matched sample of models. The matched models method is designed to control for quality changes. But new, unmatched models launched in subsequent months have their prices ignored as do old unmatched models no longer available. The paper uses retailer's bar-code scanner data on several consumer durables to show that serious sample degradation can take place and that the quality-adjusted prices of unmatched items differ from those of matched ones, leading to substantial underestimates of inflation. Hedonic indices use the whole sample. They are argued to be more useful to price measurement in markets with a rapid turnover of models in order to avoid the demonstrated bias. JEL Classification: C43, E43, O47Cost of living indices, Superlative index numbers

The effect of (mis-specified) GARCH filters on the finite sample distribution of the BDS test

This paper considers the effect of using a GARCH filter on the properties of the BDS test statistic as well as a number of other issues relating to the application of the test. It is found that, for certain values of the user-adjustable parameters, the finite sample distribution of the test is far-removed from asymptotic normality. In particular, when data generated from some completely different model class are filtered through a GARCH model, the frequency of rejection of iid falls, often substantially. The implication of this result is that it might be inappropriate to use non-rejection of iid of the standardised residuals of a GARCH model as evidence that the GARCH model ‘fits’ the data

Structural breaks in the real exchange rate adjustment mechanism

We show that the behaviour of the real exchange rates of the UK, Germany, France and Japan has been characterised by structural breaks which changed the adjustment mechanism. In the context of a Time-Varying Smooth Transition AutoRegressive of the kind introduced by Lundbergh et al (2003), we show that the real exchange rate process shifted in the aftermath of Black Wednesday in the case of the Pound, in 1984-5 in the case of the Franc and, more tentatively, during the Asian crisis of 1997-8 in the case of the Ye

Hedonic Imputation versus Time Dummy Hedonic 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 the prices and quantities of unmatched new and old models. The two main approaches to hedonic indexes are hedonic imputation (HI) indexes and dummy time hedonic (HD) indexes. This study provides a formal analysis of the difference between the two approaches for alternative implementations of an index that uses weighting that is comparable to the weighting used by the Törnqvist superlative index in standard index number theory. This study shows exactly why the results may differ and discusses the issue of choice between these approaches. An illustrative study for desktop PCs is provided.

Bootstrapping the log-periodogram estimator of the long-memory parameter: is it worth weighting?

Estimation of the long-memory parameter from the log-periodogram (LP) regression, due to Geweke and Porter-Hudak (GPH), is a simple and frequently used method of semi-parametric estimation. However, the simple LP estimator suffers from a finite sample bias that increases with the dependency in the short-run component of the spectral density. In a modification of the GPH estimator, Andrews and Guggenberger, AG (2003) suggested a bias-reduced estimator, but this comes at the cost of inflating the variance. To avoid variance inflation, Guggenberger and Sun (2004, 2006) suggested a weighted LP (WLP) estimator using bands of frequencies, which potentially improves upon the simple LP estimator. In all cases a key parameter in these methods is the need to choose a frequency bandwidth, m, which confines the chosen frequencies to be in the ‘neighbourhood’ of zero. GPH suggested a ‘square-root’ rule of thumb that has been widely used, but has no optimality characteristics. An alternative, due to Hurvich and Deo (1999), is to derive the root mean square error (rmse) optimising value of m, which depends upon an unknown parameter, although that can be consistently estimated to make the method feasible. More recently, Arteche and Orbe (2009a,b), in the context of the GPH estimator, suggested a promising bootstrap method, based on the frequency domain, to obtain the rmse value of m that avoids estimating the unknown parameter. We extend this bootstrap method to the AG and WLP estimators and to consideration of bootstrapping in the frequency domain (FD) and the time domain (TD) and, in each case, to ‘blind’ and ‘local’ versions. We undertake a comparative simulation analysis of these methods for relative performance on the dimensions of bias, rmse, confidence interval width and fidelity