2005
DOI: 10.2139/ssrn.865204
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Non-Linear Adjustment in Law of One Price Deviations and Physical Characteristics of Goods

Abstract: At a level of individual goods, heterogeneity of marginal transaction costs, proxied by price-to-weight ratios and stowage factors, explains a large part of the variation in thresholds of no-adjustment and conditional half-lives of law of one price deviations. Prices of heavier (more voluminous) goods deviate further before becoming mean-reverting. Moreover, after becoming mean-reverting, prices of heavier goods converge more slowly. Together with measures of pricing power, market size, distance and exchange r… Show more

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Cited by 2 publications
(4 citation statements)
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“…(2003) find non-linear adjustment at sectoral level and show that the heterogeneity relates to distance and nominal exchange rate volatility. Berka (2002) shows that for disaggregated law of one price deviations between US and Canada, TAR(2,p,d) threshold estimates as well as conditional convergence speeds are significantly negatively correlated to price-to-weight and price-to-volume ratios for individual products. Price differences for heavier (or more bulky) goods sustain larger deviations before becoming mean-reverting.…”
Section: Introductionmentioning
confidence: 94%
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“…(2003) find non-linear adjustment at sectoral level and show that the heterogeneity relates to distance and nominal exchange rate volatility. Berka (2002) shows that for disaggregated law of one price deviations between US and Canada, TAR(2,p,d) threshold estimates as well as conditional convergence speeds are significantly negatively correlated to price-to-weight and price-to-volume ratios for individual products. Price differences for heavier (or more bulky) goods sustain larger deviations before becoming mean-reverting.…”
Section: Introductionmentioning
confidence: 94%
“…In order to compute the share of the good that is used up in transportation, I use the the dataset of physical weights and average prices in 2001 used in Berka (2002). Approximately 24% of the goods in that dataset require refrigerating for transport.…”
Section: Calibrationmentioning
confidence: 99%
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