The nexus between fuel and food prices: A time series regression with Newey-West standard errors

Bahati Ilembo

doi:10.51867/AQSSR.3.2.35

Authors

Bahati Ilembo Mzumbe University, Tanzania Author https://orcid.org/0000-0001-5991-2571

DOI:

https://doi.org/10.51867/AQSSR.3.2.35

Keywords:

Beans, Fuel, Maize, Newey-West Regression, Rice

Abstract

This study examines the relationship between diesel prices and the prices of three staple food crops (maize, beans, and rice) using a time-series regression approach with Newey–West standard errors. It tests the common assumption that rising diesel costs increase agricultural production and transportation expenses, thereby driving up food prices. This study draws on cost-push inflation theory, agricultural household production theory, and time-series econometric theory. Using monthly time series data, the study applies unit root tests, Granger causality analysis, and Newey-West regression estimation to assess both dynamic and simultaneous relationships. The unit root tests confirm that all variables are stationary at levels, making them suitable for time series analysis. Granger causality results reveal heterogeneous transmission effects between diesel and food prices. Diesel prices Granger-cause rice and beans prices at the 5% significance level, while no causal effect is found on maize prices. In addition, there are interdependencies among food crops, with evidence of causality running from beans to maize and from maize to rice, indicating partial price transmission within agricultural markets. However, no reverse causality from food crops to diesel prices is observed, supporting the exogeneity of diesel prices in the system. The Newey-West regression results further show a statistically significant positive relationship between diesel prices and all three food crop prices. These findings highlight the critical role of fuel costs in driving food price inflation through production and transportation channels, although the magnitude of impact varies across commodities. The study concludes that energy price shocks are an important determinant of food price dynamics in Tanzania, with implications for both agricultural and macroeconomic policy coordination. Coordinated energy–agriculture policy integration is essential for enhancing food price stability and protecting consumers from external price shocks.

References

Abbott, P. C., Hurt, C., & Tyner, W. E. (2008). What's driving food prices? Farm Foundation Issue Report. https://doi.org/10.22004/ag.econ.37951

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705 DOI: https://doi.org/10.1109/TAC.1974.1100705

Baffes, J. (2007). Oil spills on other commodities. Resources Policy, 32(3), 126-134.

https://doi.org/10.1016/j.resourpol.2007.08.004 DOI: https://doi.org/10.1016/j.resourpol.2007.08.004

Barrett, C. B. (2001). Measuring integration and efficiency in international agricultural markets. Review of Agricultural Economics, 23(1), 19-32. https://doi.org/10.1111/1058-7195.00043 DOI: https://doi.org/10.1111/1058-7195.00043

Bashir, N. O., Isiaka, M. A., & Haruna, H. A. (2025). Impact of petroleum pump price on prices of selected food items in South-Western Nigeria. Lafia Journal of Economics and Management Sciences. Advance online publication.

Baum, C. F. (2006). An introduction to modern econometrics using Stata. Stata Press.

Baumeister, C., & Kilian, L. (2014). Do oil price increases cause higher food prices? Economic Policy, 29(80), 691-747.

https://doi.org/10.1111/1468-0327.12039 DOI: https://doi.org/10.1111/1468-0327.12039

Blanchard, O., & Johnson, D. R. (2013). Macroeconomics (6th ed.). Pearson.

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.

Campiche, J. L., Bryant, H. L., Richardson, J. W., & Outlaw, J. L. (2007). Examining the evolving correspondence between petroleum prices and agricultural commodity prices. Journal of Agricultural and Applied Economics, 39(1), 35-48.

Claeskens, G., & Hjort, N. L. (2008). Model selection and model averaging. Cambridge University Press.

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

Fackler, P. L., & Goodwin, B. K. (2001). Spatial price analysis. In B. L. Gardner & G. C. Rausser (Eds.), Handbook of Agricultural Economics (Vol. 1, pp. 971-1024). Elsevier. https://doi.org/10.1016/S1574-0072(01)10025-3 DOI: https://doi.org/10.1016/S1574-0072(01)01008-8

Gilbert, C. L., & Morgan, C. W. (2010). Food price volatility. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 3023-3034. https://doi.org/10.1098/rstb.2010.0139 DOI: https://doi.org/10.1098/rstb.2010.0139

Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438. https://doi.org/10.2307/1912791 DOI: https://doi.org/10.2307/1912791

Greene, W. H. (1997). Econometric analysis (3rd ed.). Macmillan.

Headey, D., & Fan, S. (2008). Anatomy of a crisis: The causes and consequences of surging food prices. Agricultural Economics, 39(s1), 375-391. https://doi.org/10.1111/j.1574-0862.2008.00345.x DOI: https://doi.org/10.1111/j.1574-0862.2008.00345.x

Ivanova, M., & Dospatliev, L. (2023). Effects of diesel price on changes in agricultural commodity prices in Bulgaria. Mathematics, 11(3), Article 559. https://doi.org/10.3390/math11030559 DOI: https://doi.org/10.3390/math11030559

Kirikkaleli, D., & Darbaz, I. (2021). The causal linkage between energy price and food price. Energies, 14(14), Article 4182. https://doi.org/10.3390/en14144182 DOI: https://doi.org/10.3390/en14144182

Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708. https://doi.org/10.2307/1913610 DOI: https://doi.org/10.2307/1913610

Singh, I., Squire, L., & Strauss, J. (1986). Agricultural household models: Extensions, applications, and policy. Johns Hopkins University Press.

Stock, J. H., & Watson, M. W. (2019). Introduction to econometrics (4th ed.). Pearson.

Taghizadeh-Hesary, F., Rasoulinezhad, E., & Yoshino, N. (2020). Exploring the relationship between energy prices and food prices. In Energy sustainability and development in ASEAN and East Asia (pp. 106-132). Routledge.

https://doi.org/10.4324/9781003026075-7 DOI: https://doi.org/10.4324/9781003026075-7

Wit, E., van den Heuvel, E., & Romeijn, J.-W. (2012). "All models are wrong": An introduction to model uncertainty. Statistica Neerlandica, 66(3), 217-236. https://doi.org/10.1111/j.1467-9574.2012.00530.x DOI: https://doi.org/10.1111/j.1467-9574.2012.00530.x

Wooldridge, J. M. (2013). Introductory econometrics: A modern approach (5th ed.). South-Western Cengage Learning.

Zhang, Z., Lohr, L., Escalante, C., & Wetzstein, M. (2010). Food versus fuel: What do prices tell us? Energy Policy, 38(1), 445-451. https://doi.org/10.1016/j.enpol.2009.09.034 DOI: https://doi.org/10.1016/j.enpol.2009.09.034

Zingbagba, M., Nunes, R., & Fadairo, M. (2020). The impact of diesel price on upstream and downstream food prices: Evidence from São Paulo. Energy Economics, 85, Article 104531. https://doi.org/10.1016/j.eneco.2019.104531 DOI: https://doi.org/10.1016/j.eneco.2019.104531