A Reconsideration of Hedonic Price Indices with an Application to PC's


Book Description

"This paper provides a justification for hedonic price indices and details the properties of hedonic price functions. The analysis is done in a market setting in which a finite number of goods, each defined by its characteristics, interact. We note that proper hedonic indices can be constructed from the same data currently used to construct matched model indices. Since the matched model index does not incorporate price changes for goods which exit, and the goods that exited tend to be those goods whose prices fall, the matched model index has a selection problem which biases it upwards. The hedonic index does not have this problem. We illustrate with a new study of price indices for PC's. The hedonic index shows steep price declines in every year. On average, the matched model indices indicate no price fall at all and one commonly used matched model index is negatively correlated with the hedonic. We also construct and compare alternative price indices used either in research or by the federal statistical agencies. Of these the one that seems to work well is a Pasche style hedonic. Its advantage is that since it does not require computation of the current period's hedonic function, it is easier to use when monthly timetables need to be met"--National Bureau of Economic Research web site










The Application of Hedonic Methods in Quality-Adjusted Price Indices


Book Description

The measurement of price dynamics is by no means new endeavourin the official statistics but the process of establishing accurate price changes in time still remains challenging in many areas. One such demanding field is the application of appropriate techniques in price index development for providing amendments reflecting quality differences which might occur in the compared commodities. The book presents results of research on the applicability of hedonic methods in adjusting price indices to changes in the goods quality and test the techniques used for hedonic price indices construction using the data sets for various groups of heterogeneous goods, including used automobiles, appartments, household appliances and ICT goods.




A Practical Guide to Price Index and Hedonic Techniques


Book Description

This book provides an accessible guide to price index and hedonic techniques, with a focus on how to best apply these techniques and interpret the resulting measures. One goal of this book is to provide first-hand experience at constructing these measures, with guidance on practical issues such as what the ideal data would look like and how best to construct these measures when the data are less than ideal. A related objective is to fill the wide gulf between the necessarily simplistic elementary treatments in textbooks and the very complex discussions found in the theoretical and empirical measurement literature. Here, the theoretical results are summarized in an intuitive way and their numerical importance is illustrated using data and results from existing studies. Finally, while the aim of much of the existing literature is to better understand official price indexes like the Consumer Price Index, the emphasis here is more practical: to provide the needed tools for individuals to apply these techniques on their own. As new datasets become increasingly accessible, tools like these will be needed to obtain summary price measures. Indeed, these techniques have been applied for years in antitrust cases that involve pricing, where economic experts typically have access to large, granular datasets.




Hedonic Price Indexes with Unobserved Product Characteristics, and Application to PC's


Book Description

We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in the implicit prices of unobserved characteristics. Next, we show that the bias can be corrected for under fairly general assumptions using extensions of factor analysis methods. We test our methods empirically using a new comprehensive monthly data set for desktop personal computer systems. For this data we find that the standard hedonic index has a slight upward bias of approximately 1.4% per year. We also find that omitting an important characteristic (CPU benchmark) causes a large bias in the index with standard methods, but that this bias is essentially eliminated when the proposed correction is applied




Index Number Theory and Price Statistics


Book Description