IDEAS home Printed from https://ideas.repec.org/p/ekd/008007/8417.html
   My bibliography  Save this paper

Policy reforms and efficiency analysis in domestic agricultural markets. Evidence from an econometric analysis in Rwanda

Author

Listed:
  • Tharcisse NKUNZIMANA
  • Tharcisse NKUNZIMANA
  • Jean-Baptiste HABYARIMANA

Abstract

Recent changes and reforms in Rwandan agricultural policies, formulated since 2000, have emphasized on transforming agriculture sector from subsistence level into a modernized and more industrial and market oriented agricultural sector. The Vision 2020 launched in 2000, considers the agricultural sector as the principal source of economic growth of the country with an anticipated growth of between 5 - 6 percent each year to reach an overall economic growth of 7 – 8 percent projected in 2020, it has therefore placed greater emphasis on improving agriculture productivity (MINAGRI, 2009). As widely documented in the economic literature, policy changes and reforms adoption should improve the functioning of agricultural markets and enhance commodity market performance. Therefore, agricultural policy changes and reforms in the country have contributed to the decline of yield and crop production instability, improvement in food distribution system, investing in new products that can generate more revenues and has facilitated households to access foodstuff at fair prices. Nonetheless, food prices from 1998 to 2014 show sudden and sometimes tremendous food price variations. Rwanda agricultural markets represent a vital opportunity for commodity modelers to analyze and provide information on food price volatility and transmission. The findings from this paper will help to inform policymakers and decision takers on food market dynamics and their causes. In order to handle volatility in selected 5-6 crop commodities prices, the generalized autoregressive conditional heteroskedasticity (GARCH) developed by Bollerslev's (1986) as extension from Engle (1982) is used. Price transmission will be measured by vector error correction model (VEC) developed by Engle and Granger (1987). This econometric model allows to establish relationship (long-run equilibrium, short-run dynamics) between prices from different crop markets in different provinces of Rwanda. The time series properties of each of the price variables will be examined by using the Augmented Dickey-Fuller (ADF) test (Fuller, 1976). Taking into account the order of integration between markets, VECMs or vector auto-regressions (VARs) are specified and estimated. Several criteria like Akaike Information Criterion (AIC) for lag lengths are verified before the VEC and VAR models. At this stage, we do not have yet results but we have some assumptions/hypothesis. The first part on the literature review is finished. As now, we have the time series data on different 5-6 crop commodities in different provinces of Rwanda, we will quickly try to have some results and submit as soon as possible our full paper.

Suggested Citation

  • Tharcisse NKUNZIMANA & Tharcisse NKUNZIMANA & Jean-Baptiste HABYARIMANA, 2015. "Policy reforms and efficiency analysis in domestic agricultural markets. Evidence from an econometric analysis in Rwanda," EcoMod2015 8417, EcoMod.
    • Handle: RePEc:ekd:008007:8417
    as

    Download full text from publisher

    File URL: http://ecomod.net/system/files/papier_Nkunzimana%20T_ECOMOD2015.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-927, July.
    2. Brooks,Chris, 2008. "RATS Handbook to Accompany Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9780521896955, November.
    3. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Murindahabi, Theodore & Li, Qiang & Ekanayake, E.M.B.P., 2017. "Economic Analysis of Growth Performance of Various Grains Crops During Agricultural Reform in Rwanda," MPRA Paper 90484, University Library of Munich, Germany, revised 2017.

    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. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    2. Phillip A. Cartwright & Natalija Riabko, 2016. "Further evidence on the explanatory power of spot food and energy commodities market prices for futures prices," Review of Quantitative Finance and Accounting, Springer, vol. 47(3), pages 579-605, October.
    3. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
    4. Prosper Dovonon, 2013. "Conditionally Heteroskedastic Factor Models With Skewness And Leverage Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1110-1137, November.
    5. İbrahim Korkmaz KAHRAMAN, Habib KÜÇÜKŞAHİN, Emin ÇAĞLAK, 2019. "The Volatility Structure of Cryptocurrencies: The Comparison of GARCH Models," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 2.
    6. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    8. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
    9. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Mean and variance causality between the Cyprus Stock Exchange and major equity markets," Working Papers 0501, University of Crete, Department of Economics.
    10. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    11. McMillan, David G. & Speight, Alan E. H., 2001. "Non-ferrous metals price volatility: a component analysis," Resources Policy, Elsevier, vol. 27(3), pages 199-207, September.
    12. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2016. "Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 582-594.
    13. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    14. Prelorentzos, Arsenios-Georgios N. & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Xidonas, Panos & Goutte, Stephane & Thomakos, Dimitrios D., 2024. "Introducing the GVAR-GARCH model: Evidence from financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    15. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
    16. Francq, Christian & Zakoian, Jean-Michel, 2014. "Estimating multivariate GARCH and stochastic correlation models equation by equation," MPRA Paper 54250, University Library of Munich, Germany.
    17. Hakim, Abdul & McAleer, Michael, 2009. "Forecasting conditional correlations in stock, bond and foreign exchange markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2830-2846.
    18. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    19. Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo.
    20. Anders Tolver Jensen & Theis Lange, 2009. "On IGARCH and convergence of the QMLE for misspecified GARCH models," CREATES Research Papers 2009-06, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Rwanda; Agricultural issues; Modeling: new developments;
    All these keywords.

    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:ekd:008007:8417. 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: Theresa Leary (email available below). General contact details of provider: https://edirc.repec.org/data/ecomoea.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.