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Noisy Information, Distance and Law of One Price Dynamics Across US Cities

Author

Listed:
  • 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," CAMA Working Papers 2014-77, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2014-77
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    1. is not listed on IDEAS
    2. Tatsushi Okuday & Tomohiro Tsurugaz & Francesco Zanetti, 2019. "Imperfect Information, Shock Heterogeneity, and Inflation Dynamics," BCAM Working Papers 1906, Birkbeck Centre for Applied Macroeconomics.
    3. Jochmans, Koen & Weidner, Martin, 2024. "Inference On A Distribution From Noisy Draws," Econometric Theory, Cambridge University Press, vol. 40(1), pages 60-97, February.
    4. 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.
    5. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    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. 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.
    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. 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.
    14. 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.
    15. 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).
    16. Choi, Chi-Young & Choi, Horag, 2014. "Does distance reflect more than transport costs?," Economics Letters, Elsevier, vol. 125(1), pages 82-86.
    17. repec:upd:utmpwp:030 is not listed on IDEAS
    18. 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.
    19. Kazutaka TAKECHI, 2016. "Daily Gravity," Discussion papers 16095, Research Institute of Economy, Trade and Industry (RIETI).
    20. 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.
    21. 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.
    22. Guo, Zi-Yi, 2017. "Information heterogeneity, housing dynamics and the business cycle," EconStor Preprints 168561, ZBW - Leibniz Information Centre for Economics.
    23. 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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    Keywords

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    JEL classification:

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

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