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Noisy information, distance and law of one price dynamics across US cities

Author

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  • Mario J. Crucini
  • Mototsugu Shintani
  • Takayuki Tsuruga
Abstract
Using US micro price data at the city level, we provide evidence that both the volatility and the persistence of deviations from the law of one price (LOP) are rising in the distance between US cities. A standard, two-city, stochastic equilibrium model with trade costs can predict the relationship between volatility and distance but not between persistence and distance. To account for the latter fact, we augment the standard model with noisy signals about the state of nominal aggregate demand that are asymmetric across cities. We further show that the main predictions of the model continue to hold even if we allow for the interaction of imperfect information, sticky prices, and multiple cities.

Suggested Citation

  • Mario J. Crucini & Mototsugu Shintani & Takayuki Tsuruga, 2014. "Noisy information, distance and law of one price dynamics across US cities," Globalization Institute Working Papers 216, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddgw:216
    DOI: 10.24149/gwp216
    Note: Published as: Crucini, Mario E., Motosugu Shintani and Takayuki Tsuruga (2015), "Noisy Information, Distance and Law of One Price Dynamics Across US Cities," Journal of Monetary Economics 74: 52-66.
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    3. José‐María Montero & Tiziana Laureti & Román Mínguez & Gema Fernández‐Avilés, 2020. "A Stochastic Model with Penalized Coefficients for Spatial Price Comparisons: An Application to Regional Price Indexes in Italy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 512-533, September.
    4. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    5. Craig Benedict & Mario J. Crucini & Anthony Landry, 2020. "On What States Do Prices Depend? Answers From Ecuador," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(8), pages 1909-1935, December.
    6. Choi, Chi-Young & Choi, Horag, 2016. "The role of two frictions in geographic price dispersion: When market friction meets nominal rigidity," Journal of International Money and Finance, Elsevier, vol. 63(C), pages 1-27.
    7. Tatsushi Okuda & Tomohiro Tsuruga & Francesco Zanetti, 2021. "Imperfect information, heterogeneous demand shocks, and inflation dynamics," CAMA Working Papers 2021-29, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Ryo Okui & Takahide Yanagi, 2020. "Kernel estimation for panel data with heterogeneous dynamics," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 156-175.
    9. Hayakawa, Kazunobu & Tsubota, Kenmei, 2022. "The impact of highways on commodity prices: The price of butter in Japan," Journal of Asian Economics, Elsevier, vol. 81(C).
    10. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    11. Malloy Brandon, 2018. "The Supply Network and Price Dispersion in the Canadian Gasoline Market," Review of Network Economics, De Gruyter, vol. 17(2), pages 75-107, June.
    12. Yazgan, M. Ege & Yilmazkuday, Hakan, 2011. "Price-level convergence: New evidence from U.S. cities," Economics Letters, Elsevier, vol. 110(2), pages 76-78, February.
    13. Kato, Ryo & Okuda, Tatsushi & Tsuruga, Takayuki, 2021. "Sectoral inflation persistence, market concentration, and imperfect common knowledge," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 500-517.
    14. Shintani, Mototsugu & Ueda, Kozo, 2023. "Identifying the source of information rigidities in the expectations formation process," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    15. Choi, Chi-Young & Choi, Horag, 2014. "Does distance reflect more than transport costs?," Economics Letters, Elsevier, vol. 125(1), pages 82-86.
    16. Chi‐Young Choi & Anthony Murphy & Jyh‐Lin Wu, 2017. "Segmentation of consumer markets in the US: What do intercity price differences tell us?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(3), pages 738-777, August.
    17. TAKECHI Kazutaka, 2016. "Daily Gravity," Discussion papers 16095, Research Institute of Economy, Trade and Industry (RIETI).
    18. Yoon J. Jo & Misaki Matsumura & David E. Weinstein, 2019. "The Impact of E-Commerce on Relative Prices and Consumer Welfare," NBER Working Papers 26506, National Bureau of Economic Research, Inc.
    19. David Fielding & Christopher Hajzler & James Macgee, 2015. "Distance, Language, Religion, and the Law of One Price: Evidence from Canada and Nigeria," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 1007-1029, August.
    20. Guo, Zi-Yi, 2017. "Information heterogeneity, housing dynamics and the business cycle," EconStor Preprints 168561, ZBW - Leibniz Information Centre for Economics.
    21. Guo, Zi-Yi, 2017. "Information heterogeneity, housing dynamics and the business cycle," Economics Discussion Papers 2017-17, Kiel Institute for the World Economy (IfW Kiel).

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    More about this item

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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