A dynamic econometric model of Nigeria’s rice market was designed to serve as a base for future policy analyses. Using time-series data spanning 38 years, the model contains four structural equations representing paddy area harvested, paddy yield, per capita demand, and producer price variables. Estimates for these equations were obtained using the autoregressive distributed lag (ARDL) cointegration approach. Results of the paddy production and yield sub-models showed that paddy area harvested, and paddy yield was price inelastic. Furthermore, the paddy area harvested responded favourably to technological advancement. For the demand sub-model, estimated own price and cross-price elasticities showed that rice has an inelastic demand response, with wheat being a substitute. A series of validation tests strengthened the reliability of the model for use as an empirical framework for forecasting and analysing the effects of changes in policies such as rice import tariff reforms on production, consumption, retail price, and imports.

• Alabi, R. A., & Adams, O. O. (2020). The impact of e-wallet fertilizer subsidy scheme and its implication on food security in Nigeria. African Economic Research Consortium.

• Ammani, A. A. (2012). An investigation into the relationship between agricultural production and formal credit supply in Nigeria. International Journal of Agriculture and Forestry, 2(1), 46-52. https://doi.org/10.5923/j.ijaf.20120201.08

• Ayinde, O. E., Bessler, D. A., & Oni, F. E. (2014). Analysis of supply response and price risk on rice production in Nigeria [Paper presentation]. Agricultural & Applied Economics Association’s 2014 AAEA Annual Meeting, Minneapolis, USA. http://dx.doi.org/10.22004/ag.econ.170347

• Bahşi, N., & Çetin, E. (2020). Determining of agricultural credit impact on agricultural production value in Turkey. Ciência Rural, 50(11), 1-13. http://dx.doi.org/10.1590/010 3-8478cr20200003

• Boansi, D. (2014). Yield response of rice in Nigeria: A co-integration analysis. American Journal of Agriculture and Forestry, 2(2), 15-24. http://dx.doi.org/10.11648/j.ajaf.2014 0202.11

• Carpentier, A., Gohin, A., Sckokai, P., & Thomas, A. (2015). Economic modelling of agricultural production: Past advances and new challenges. Review of Agricultural and Environmental Studies-Revue d’Etudes en Agriculture et Environnement (RAEStud), 96(1), 131-166. http://dx.doi.org/10.22004/ag.econ.276763

• Chandio, A. A., Jiang, Y., Joyo, M. A., & Pickson, R. B. (2018). Research on factors influencing the grain production in Pakistan: An ARDL approach. European Online Journal of Natural and Social Sciences, 7(3), 538-553.

• Christ, C. F. (1994). The Cowles commission’s contributions to econometrics at Chicago, 1939-1955. Journal of Economic Literature, 32(1), 30-59.

• Chu, F. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740-751. https://doi.org/10.1016/j.tourman.2008.10.016

• Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057-1072. https://doi.org/10.2307/1912517

• Dorosh, P. A., & Malek, M. (2016). Rice imports, prices, and challenges for trade policy. In the Nigerian rice economy: Policy options for transforming production, marketing, and trade. In K. Gyimah-Brempong, M. Johnson, & H. Takeshima (Eds.), The Nigerian rice economy: Policy options for transforming production, marketing, and trade (pp. 159-180). International Food Policy Research Institute (IFPRI), University of Pennsylvania Press. https://doi.org/10.2499/9780812248951

• Egwuma, H., Shamsudin, M. N., Mohamed, Z., Kamarulzaman, N. H., & Wong, K. K. S. (2016). A model for the palm oil market in Nigeria: An econometrics approach. International Journal of Food and Agricultural Economics, 4(2), 69-85. http://dx.doi.org/10.22004/ag.econ.234911

• Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 55(2), 251-276. https://doi.org/10.2307/1913236

• Essaten, S., Mekki, A. A. E., & Serghini, M. (2018). Econometric analysis of cereal demand in Morocco using the almost ideal demand system model. International Journal of Food and Agricultural Economics, 6(2), 63-71. http://dx.doi.org/10.22004/ag.econ.283768

• Ghaffar, R. A. (1986). Specification and estimation of a commodity market model for the U.S. soybeans and soybean oil incorporating palm oil imports. Centre for Agricultural Policy Studies, Universiti Putra Malaysia. http://myagric.upm.edu.my/id/eprint/10165/

• Granger, C. W. J. (1983). Co-integrated variables and error correcting models (Diego Discussion Paper, 83/13). University of California.

• Granger, C. W., & Weiss, A. A. (2001). Time series analysis of error-correction models. In essays in econometrics: Collected papers of Clive WJ Granger (pp. 129-144). Cambridge University Press.

• Global Rice Science Partnership. (2013). Rice almanac (4th ed.). International Rice Research Institute.

• Hallam, D. (1990). Econometric modelling of agricultural commodity markets. Routledge.

• Johnson, M. E., & Dorosh, P. (2017). Tariffs, smuggling and economic welfare: A spatial analysis of Nigerian rice policy options. Journal of African Economies, 26(4), 516-538. https://doi.org/10.1093/jae/ejx008

• Kozicka, M., Kalkuhl, M., Saini, S., & Brockhaus, J. (2015). Modelling Indian wheat and rice sector policies (ZEF Discussion Papers on Development Policy, No. 197). University of Bonn, Center for Development Research (ZEF).

• Labys, W. C. (1973). Dynamic commodity models: Specification, estimation, and simulation. Lexington Books Lexington.

• Labys, W. C., & Pollak P. K. (1984). Commodity models for forecasting and policy analysis. Nichols Publishing Co.

• Makama, S. A., Ilu, I. Y., Suleiman, N. J., Isiaku, S., & Isah, M. A. (2017). Demand analysis of rice in Nigeria. Nigerian Journal of Agricultural Extension, 18(3), 70-75.

• Michael, A., Tashikalma, A. K., & Maurice, D. C. (2018). Agricultural inputs subsidy in Nigeria: An overview of the growth enhancement support scheme (GESS). Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 781-789. https://doi.org/10.11118/actaun201866030781

• Moon, H. R., & Perron B. (2008). Seemingly unrelated regressions. In Palgrave Macmillan (Eds.), The new palgrave dictionary of economics. Palgrave Macmillan. https://doi.org/10.1057/978-1-349-95121-5_2296-1

• Narayan, P. K. (2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37(17), 1979-1990. https://doi.org/10.1080/0 0036840500278103

• Nicholson, W., & Snyder C. (2011). Microeconomic theory: Basic principles and extensions (11th ed.). South-Western Cengage Learning.

• Nkoro, E., & Uko, A. K. (2016). Autoregressive distributed lag (ARDL) cointegration technique: Application and interpretation. Journal of Statistical and Econometric Methods, 5(4), 63-91.

• Obilor, S. (2013). The impact of commercial banks’ credit to agriculture on agricultural development in Nigeria: An econometric analysis. International Journal of Business, Humanities and Technology, 3(1), 85-94.

• Okpe, A. E., Abu, O., & Odoemenem, I. U. (2018). Rice output response to commercial loan to agriculture in Nigeria from 1966 to 2015. International Journal of Food and Agricultural Economics, 6(4), 71-85. http://dx.doi.org/10.22004/ag.econ.283875

• Omoregie, O. K., Ikpesu, F., & Abraham, E. O. (2018). Credit supply and rice output in Nigeria: Empirical insight from vector error correction model approach. International Journal of Economics and Financial Issues, 8(5), 68-74.

• Paul, M., Molua, E. L., Nzie, J. R. M., & Fuh, G. L. (2020). Production and supply of tomato in Cameroon: Examination of the comparative effect of price and non-price factors. Scientific African, 10(e00574), 1-13. https://doi.org/10.1016/j.sciaf.2020.e00574

• Pesaran, M. H., Shin, Y., & Smith, R. J. (1996). Testing for the existence of a long-run relationship (Cambridge Working Papers in Economics 9622). Faculty of Economics, University of Cambridge.

• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001), Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326. https://doi.org/10.1002/jae.616

• Phillips, P., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335

• PricewaterhouseCoopers Limited. (2008). Boosting rice production through increased mechanisation. https://www.pwc.com/ng/en/assets/pdf/boosting-rice-production.pdf

• Sembiring, S. A., & Hutauruk, J. (2018). Econometric model of rice policy based on presidential instruction. In IOP Conference Series: Materials science and engineering (Vol. 300). IOP Publishing Ltd. https://iopscience.iop.org/article/10.1088/1757-899X/300/1/012028/meta

• Shamsudin, M. N. (2008). Econometric modelling for agricultural policy analysis and forecasting: Between theory and reality. Journal of Quantitative Methods, 4(2), 1-18. http://myagric.upm.edu.my/id/eprint/6812

• Shrestha, M. B., & Bhatta, G. R. (2018). Selecting appropriate methodological framework for time series data analysis. The Journal of Finance and Data Science, 4(2), 71-89. https://doi.org/10.1016/j.jfds.2017.11.001

• Takeshima, H. (2016). Policy options for inducing a supply response. In K. Gyimah-Brempong, M. Johnson, & H. Takeshima (Eds.), The Nigerian rice economy: Policy options for transforming production, marketing, and trade (pp. 85-110). University of Pennsylvania Press.

• Van Tongeren, F., Van Meijl, H., & Surry, Y. (2001). Global models applied to agricultural and trade policies: A review and assessment. Agricultural economics, 26(2), 149-172. https://doi.org/10.1111/j.1574-0862.2001.tb00060.x

• Wossen, T., Abdoulaye, T., Alene, A., Feleke, S., Ricker-Gilbert, J., Manyong, V., & Awotide, B. A. (2017). Productivity and welfare effects of Nigeria’s e-voucher-based input subsidy program. World Development, 97, 251-265. https://doi.org/10.1016/j.worldde v.2017.04.021

• Yazdanshenas, L., Moghadasi, R., & Yazdani, S. (2011). A model for the wheat market in Iran. International Journal of Agricultural Science and Research, 2(2), 49-55.

• Yazdanshenas, L., Moghadasi, R., & Yazdani, S. (2015). Estimating Iranian wheat market (A comparative study between ARDL and SUR). European Online Journal of Natural and Social Sciences, 4(4), 626-637.

• Yusuf, W. A., Yusuf, S. A., Adesope, A. A. A., & Adebayo, O. Z. (2020). Determinants of rice import demand in Nigeria. Journal of Applied Sciences and Environmental Management, 24(5), 923-931. https://doi.org/10.4314/jasem.v24i5.30

• Zakaree, S. S. (2014). Impact of agricultural credit guarantee scheme fund (ACGSF) on domestic food supply in Nigeria. Journal of Economics, Management and Trade, 4(8), 1273-1284. https://doi.org/10.9734/BJEMT/2014/6441