IDEAS home Printed from https://ideas.repec.org/p/ags/pugtwp/332525.html

Food Price Volatility implications for Trade and Monetary Policy between Nigeria and CEMAC: a Bayesian DSGE model approach

Author

Listed:
  • Kame Babilla, Thierry

Abstract

Food price volatility has become a major challenge for trade in many commodity-oriented developing countries. In Africa, the growing concern is the suitable exchange rate regime to tackle the effect of volatility for trade intensification. According to the theory of international trade, exchange rate stability, by the reduction of transaction costs, stimulates investments and therefore intensify cross-border trade. The proposed research aims thus to emphasize food price volatility implications on the bilateral trade between Nigeria and CEMAC in presence of fixed exchange regime relatively to floating exchange regime. We develop a two-country Dynamic Stochastic General Equilibrium (DSGE) model with different monetary policy regimes. The model departures of the canonical model via some keys extensions, such as, net food importer, vulnerability to supply shock, large spending allocated to food consumption, the money preference and Engel’s Law in the food market. The calibration and estimations of the DSGE model would be obtain using Bayesian method by means of Monte-Carlo simulations and metropolis-hasting algorithm. The following findings could be suggested: the designed DSGE model provides a good point of departure for the examination of the effect of food prices volatility on trade between food-oriented economies in Africa facing different exchange rate regimes. Moreover, results reveal that Nigeria encompasses a huge degree of vulnerability to food prices shock than CEMAC region. Furthermore, result suggests that food prices volatility accentuate the low level of bilateral trade between Nigeria and CEMAC. In addition, results show an incomplete pass-through of exchange rate to domestic inflation in both economies. More importantly, results reveal that in the presence of adverse foreign food prices shocks, floating exchange rate regime is dominant than fixed exchange rates regime for each economy to tackle the shocks. Through welfare analysis, results confirm that, facing external food price shocks, the floating exchange rate regime is best for bilateral trade intensification between Nigeria and CEMAC. As policy implication, to enhance trade, monetary policymakers in both economies should cooperate by establishing mechanism able to offset the impact of food price volatility.

Suggested Citation

  • Kame Babilla, Thierry, 2014. "Food Price Volatility implications for Trade and Monetary Policy between Nigeria and CEMAC: a Bayesian DSGE model approach," Conference papers 332525, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    • Handle: RePEc:ags:pugtwp:332525
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/332525/files/6699.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gollier, Christian & Weitzman, Martin L., 2010. "How should the distant future be discounted when discount rates are uncertain?," Economics Letters, Elsevier, vol. 107(3), pages 350-353, June.
    2. McKibbin, Warwick J. & Wilcoxen, Peter J., 2009. "Uncertainty and climate change policy design," Journal of Policy Modeling, Elsevier, vol. 31(3), pages 463-477, May.
    3. Pizer, William A., 1999. "The optimal choice of climate change policy in the presence of uncertainty," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 255-287, August.
    4. Geweke, John, 2001. "A note on some limitations of CRRA utility," Economics Letters, Elsevier, vol. 71(3), pages 341-345, June.
    5. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
    6. Masako Ikefuji & Roger J. A. Laeven & Jan R. Magnus & Chris Muris, 2011. "Weitzman meets Nordhaus: Expected utility and catastrophic risk in a stochastic economy-climate model," ISER Discussion Paper 0825, Institute of Social and Economic Research, The University of Osaka.
    7. Bernhard Eckwert & Itzhak Zilcha, 2003. "Incomplete risk sharing arrangements and the value of information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 21(1), pages 43-58, January.
    8. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    9. Keen, Michael, 1990. "Welfare analysis and intertemporal substitution," Journal of Public Economics, Elsevier, vol. 42(1), pages 47-66, June.
    10. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    11. Stéphane Hallegatte & Philippe Ambrosi & Jean Charles Hourcade, 2007. "Using Climate Analogues for Assessing Climate Change Economic Impacts in Urban Areas," Post-Print hal-00164627, HAL.
    Full references (including those not matched with items on IDEAS)

    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. Yongyang Cai & Kenneth L. Judd & Thomas S. Lontzek, 2013. "The Social Cost of Stochastic and Irreversible Climate Change," NBER Working Papers 18704, National Bureau of Economic Research, Inc.
    2. van den Bergh, J.C.J.M. & Botzen, W.J.W., 2015. "Monetary valuation of the social cost of CO2 emissions: A critical survey," Ecological Economics, Elsevier, vol. 114(C), pages 33-46.
    3. Newbold, Stephen & Griffiths, Charles & Moore, Chris & Wolverton, Ann & Kopits. Elizabeth, 2010. "The “Social Cost of Carbon” Made Simple," National Center for Environmental Economics-NCEE Working Papers 280887, United States Environmental Protection Agency (EPA).
    4. Carlo A. Favero, 2007. "Model Evaluation in Macroeconometrics: from early empirical macroeconomic models to DSGE models," Working Papers 327, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Andrew Foerster & Juan F. Rubio‐Ramírez & Daniel F. Waggoner & Tao Zha, 2016. "Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 7(2), pages 637-669, July.
    6. Mark Kagan, 2012. "Climate Change Skepticism in the Face of Catastrophe," Tinbergen Institute Discussion Papers 12-112/VIII, Tinbergen Institute, revised 29 Sep 2014.
    7. Aldrich Eric Mark & Kung Howard, 2021. "Computational Methods for Production-Based Asset Pricing Models with Recursive Utility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-26, February.
    8. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    9. Lemoine, Derek M. & Traeger, Christian P., "undated". "Tipping Points and Ambiguity in the Economics of Climate Change," CUDARE Working Papers 98127, University of California, Berkeley, Department of Agricultural and Resource Economics.
    10. Lim, G.C. & McNelis, Paul D., 2008. "Computational Macroeconomics for the Open Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262123061, December.
    11. Jussi Lintunen & Lauri Vilmi, 2021. "Optimal Emission Prices Over the Business Cycles," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(1), pages 135-167, September.
    12. Karp, Larry & Zhang, Jiangfeng, 2001. "Bayesian Learning and the Regulation of Greenhouse Gas Emissions," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2fr0783c, Department of Agricultural & Resource Economics, UC Berkeley.
    13. Vadym Lepetyuk & Lilia Maliar & Serguei Maliar, 2017. "Should Central Banks Worry About Nonlinearities of their Large-Scale Macroeconomic Models?," Staff Working Papers 17-21, Bank of Canada.
    14. Ikefuji, Masako & Laeven, Roger J.A. & Magnus, Jan R. & Muris, Chris, 2020. "Expected utility and catastrophic risk in a stochastic economy–climate model," Journal of Econometrics, Elsevier, vol. 214(1), pages 110-129.
    15. Posch, Olaf & Trimborn, Timo, 2013. "Numerical solution of dynamic equilibrium models under Poisson uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2602-2622.
    16. Viktors Ajevskis, 2019. "Generalised Impulse Response Function as a Perturbation of a Global Solution to DSGE Models," Working Papers 2019/04, Latvijas Banka.
    17. Stephanie Becker & Lars Grüne & Willi Semmler, 2007. "Comparing accuracy of second-order approximation and dynamic programming," Computational Economics, Springer;Society for Computational Economics, vol. 30(1), pages 65-91, August.
    18. Dario Caldara & Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Wen Yao, 2012. "Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 188-206, April.
    19. Lan, Hong & Meyer-Gohde, Alexander, 2013. "Solving DSGE models with a nonlinear moving average," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2643-2667.
    20. Sergey Ivashchenko & Semih Emre Çekin & Kevin Kotzé & Rangan Gupta, 2020. "Forecasting with Second-Order Approximations and Markov-Switching DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 747-771, December.

    More about this item

    Keywords

    ;
    ;

    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:ags:pugtwp:332525. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/gtpurus.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.