IDEAS home Printed from https://ideas.repec.org/p/bdm/wpaper/2009-14.html
   My bibliography  Save this paper

A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico

Author

Listed:
  • Sidaoui José Julián
  • Capistrán Carlos
  • Chiquiar Daniel
  • Ramos Francia Manuel

Abstract

This note studies the causal relationship that may exist between the producer price index (PPI) and the consumer price index (CPI). In contrast with previous international studies, the results suggest that, in the case of Mexico, information on the PPI seems to be useful to improve forecasts of CPI inflation. In particular, CPI inflation responds significantly to disequilibrium errors with respect to the long-run relationship between consumer and producer prices. These results are based on in-sample and out-of-sample tests of Granger causality, using an error correction model.

Suggested Citation

  • Sidaoui José Julián & Capistrán Carlos & Chiquiar Daniel & Ramos Francia Manuel, 2009. "A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico," Working Papers 2009-14, Banco de México.
    • Handle: RePEc:bdm:wpaper:2009-14
    as

    Download full text from publisher

    File URL: https://www.banxico.org.mx/publications-and-press/banco-de-mexico-working-papers/%7BD9641147-AD8F-73A8-5938-D3140F7E09EC%7D.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clive, W.J. & Lin, Jin-Lung, 1995. "Causality in the Long Run," Econometric Theory, Cambridge University Press, vol. 11(3), pages 530-536, June.
    2. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    3. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    4. Stock, James H & Watson, Mark W, 1993. "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, Econometric Society, vol. 61(4), pages 783-820, July.
    5. Carlos Capistrán & Manuel Ramos‐Francia, 2009. "Inflation Dynamics In Latin America," Contemporary Economic Policy, Western Economic Association International, vol. 27(3), pages 349-362, July.
    6. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    7. Daniel Chiquiar & Antonio Noriega & Manuel Ramos-Francia, 2010. "A time-series approach to test a change in inflation persistence: the Mexican experience," Applied Economics, Taylor & Francis Journals, vol. 42(24), pages 3067-3075.
    8. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    9. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    10. Antonio Noriega & Carlos Capistrán & Manuel Ramos-Francia, 2013. "On the dynamics of inflation persistence around the world," Empirical Economics, Springer, vol. 44(3), pages 1243-1265, June.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Todd E. Clark, 1995. "Do producer prices lead consumer prices?," Economic Review, Federal Reserve Bank of Kansas City, vol. 80(Q III), pages 25-39.
    13. S. Brock Blomberg & Ethan S. Harris, 1995. "The commodity-consumer price connection: fact or fable?," Economic Policy Review, Federal Reserve Bank of New York, vol. 1(Oct), pages 21-38.
    14. Capistrán Carlos & Constandse Christian & Ramos Francia Manuel, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.
    15. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    16. Richard Dion, 1999. "Indicator Models of Core Inflation for Canada," Staff Working Papers 99-13, Bank of Canada.
    17. Leybourne Stephen & Kim Tae-Hwan & Taylor A.M. Robert, 2007. "Detecting Multiple Changes in Persistence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(3), pages 1-34, September.
    18. Hansen, Bruce E., 1992. "Efficient estimation and testing of cointegrating vectors in the presence of deterministic trends," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 87-121.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yusuf V. Topuz & Hassan Yazdifar & Sunil Sahadev, 2018. "The relation between the producer and consumer price indices: a two-country study," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 122-130, June.
    2. Guerrero Santiago & Juárez-Torres Miriam & Sámano Daniel & Kochen Federico & Puigvert Jonathan, 2016. "Price Transmission in Food and Non-Food Product Markets: Evidence from Mexico," Working Papers 2016-18, Banco de México.
    3. Shahbaz, Muhammad & Tiwari, Aviral Kumar & Tahir, Mohammad Iqbal, 2012. "Does CPI Granger-cause WPI? New extensions from frequency domain approach in Pakistan," Economic Modelling, Elsevier, vol. 29(5), pages 1592-1597.
    4. Aviral Kumar Tiwari & Muhammad Shahbaz, 2013. "Modelling the Relationship between Whole Sale Price and Consumer Price Indices: Cointegration and Causality Analysis for India," Global Business Review, International Management Institute, vol. 14(3), pages 397-411, September.
    5. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
    6. Ülke, Volkan & Ergun, Ugur, 2013. "The Relationship between Consumer Price and Producer Price Indices in Turkey," MPRA Paper 59437, University Library of Munich, Germany.
    7. Jing Sun & Jinhui Xu & Xin Cheng & Jichao Miao & Hairong Mu, 2023. "Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1279-1289, April.
    8. Tiwari, Aviral Kumar & Suresh K.G., & Arouri, Mohamed & Teulon, Frédéric, 2014. "Causality between consumer price and producer price: Evidence from Mexico," Economic Modelling, Elsevier, vol. 36(C), pages 432-440.
    9. Ivo da Rocha Lima Filho, Roberto, 2019. "Does PPI lead CPI IN Brazil?," International Journal of Production Economics, Elsevier, vol. 214(C), pages 73-79.
    10. Ramon Moreno, 2010. "Some issues in measuring and tracking prices in emerging market exonomies," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 13-51, Bank for International Settlements.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jose Sidaoui & Carlos Capistran & Daniel Chiquiar & Manuel Ramos-Francia, 2010. "On the predictive content of the PPI on CPI inflation: the case of Mexico," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 249-257, Bank for International Settlements.
    2. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
    3. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    4. Al-Sadoon, Majid M., 2014. "Geometric and long run aspects of Granger causality," Journal of Econometrics, Elsevier, vol. 178(P3), pages 558-568.
    5. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    6. Bianca Biagi & Manuela Pulina, 2009. "Bivariate VAR models to test Granger causality between tourist demand and supply: Implications for regional sustainable growth," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 231-244, March.
    7. Dierk HERZER & Felicitas NOWAK‐LEHMANN D. & Boriss SILIVERSTOVS, 2006. "Export‐Led Growth In Chile: Assessing The Role Of Export Composition In Productivity Growth," The Developing Economies, Institute of Developing Economies, vol. 44(3), pages 306-328, September.
    8. Dierk Herzer & Stephan Klasen & Felicitas Nowak-Lehmann D., 2006. "In search of FDI-led growth in developing countries," Ibero America Institute for Econ. Research (IAI) Discussion Papers 150, Ibero-America Institute for Economic Research.
    9. Catherine Bruneau & Eric Jondeau, 1999. "Long‐run Causality, with an Application to International Links Between Long‐term Interest Rates," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(4), pages 545-568, November.
    10. Hassler, Uwe, 2002. "The Effects of linear time trends on conintegration testing in single equations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 18294, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    12. Yanhua Chen & Rosario N Mantegna & Athanasios A Pantelous & Konstantin M Zuev, 2018. "A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-40, March.
    13. T. Thanh-Binh Nguyen & Kuan-Min Wang, 2010. "Causality between housing returns, inflation and economic growth with endogenous breaks," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 8(1), pages 95-115.
    14. Guillén, Osmani Teixeira & Hecq, Alain & Issler, João Victor & Saraiva, Diogo, 2015. "Forecasting multivariate time series under present-value model short- and long-run co-movement restrictions," International Journal of Forecasting, Elsevier, vol. 31(3), pages 862-875.
    15. Aliyu Alhaji Jibrilla, 2016. "Fiscal sustainability in the presence of structural breaks: Does overconfidence on resource exports hurt government’s ability to finance debt? Evidence from Nigeria," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170317-117, December.
    16. Christian MÜLLER, 2010. "Interpretation of Cointegration Coefficients - A Paradox, a Solution and Empirical Evidence," EcoMod2004 330600100, EcoMod.
    17. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    18. Massa, Ricardo & Rosellón, Juan, 2020. "Linear and nonlinear Granger causality between electricity production and economic performance in Mexico," Energy Policy, Elsevier, vol. 142(C).
    19. Edmore Mahembe & Nicholas Mbaya Odhiambo, 2019. "Foreign aid, poverty and economic growth in developing countries: A dynamic panel data causality analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1626321-162, January.
    20. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdm:wpaper:2009-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Subgerencia de desarrollo de sistemas (email available below). General contact details of provider: https://edirc.repec.org/data/bangvmx.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.