Forecasting Agricultural Production: A Chaotic Dynamic Approach

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Bunyamin Demir
Nesrin Alptekin
Yilmaz Kilicaslan
Mehmet Ergen
Nilgun Caglarirmak Uslu

Abstract

The aim of this study is to examine the existence of chaotic structure in agricultural production in Turkey by using Chaotic Dynamic Analysis (CDA) and to provide accurate forecasts of agricultural production. The data of wheat, barley and rice production in Turkey obtained from Turkish Statistical Institute (TURKSTAT) covers the period of 1991 to 2009. Our analysis shows that the supply of the selected agricultural products has a chaotic structure. Our dynamic system constructed predicted the supply of year 2010 with % 0.5 error for wheat, %5 error for barley, and %2.5 error ratio for rice. This study is the first attempt using CDA to forecast future agricultural product supply in Turkey. The findings of this study will help to produce effective policies to prevent supply disequilibrium, and excess price fluctuations.

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How to Cite
Demir, B., Alptekin, N., Kilicaslan, Y., Ergen, M. and Caglarirmak Uslu, N. (2015) “Forecasting Agricultural Production: A Chaotic Dynamic Approach”, World Journal of Applied Economics, 1(1), pp. 65-80. doi: 10.22440/EconWorld.J.2015.1.1.BD.0007.
Section
Research Articles

References

Abhyankar, A., & Copeland L.S. (1997). Uncovering nonlinear structure in real-time stock market indices: The S&P 500, The DAX, The NIKKEI 225 and The FTSE 100. Journal of Business and Economic Statistics, 15(1), 1-14. DOI: 10.1080/ 07350015. 1997.10524681.

Bacsi, Z. (1997). Modelling chaotic behaviour in agricultural prices using a discrete deterministic nonlinear price model. Agricultural Systems, 55(3), 445-459. DOI: 10.1016/S0308-521X(97)00003-6.

Barnett, W.A., & Serletis, A. (2000). Martingales, nonlinearity and chaos. Journal of Economics Dynamics and Control, 24, 703-724. DOI: 10.1016/S0165-1889(99)00023-8.

Birkhauser, B., Cromwell, J.B., & Labys, W.C. (1993). Testing for Nonlinear Dynamics and Chaos in Agricultural Commodity Prices. Institute for Labor Study, West Virginia University.

Brown, R. (1993). Calculating lyapunov exponents for short and/or noisy data sets. Physical. Review E, 47(6), 3962-3969. DOI: 10.1103/PhysRevE.47.3962.

Brock, W. A., 1986. Distinguishing random and deterministic systems: abridged version. Journal of Economic Theory, 40(1), 168-195. DOI: 10.1016/0022-0531(86)90014-1.

Brock, W.A., Dechert, W., & Schienkman, J. A. (1986). Test for independence based on the correlation dimension. Working Paper, University of Wisconsin at Madison and University of Chicago.

Bryant, P., & Brown, R. (1990). Lyapunov Exponents from Observed Time Series. Physical Review Letters, 65(13), 1523-1526. DOI: 10.1103/PhysRevLett.65.1523.

Burton, M. (1993). Some illustrations of chaos in commodity models. Journal of Agricultural Economics, 44(1), 38-50. DOI: 10.1111/j.1477-9552.1993.tb00249.x.

Casdagli, M. (1989). Nonlinear prediction of chaotic time series. Physica D: Nonlinear Phenomena, 35(3), 335-356. DOI: 10.1016/0167-2789(89)90074-2.

Chavas, J. P., & Holt, M. T. (1993). Market instability and nonlinear dynamics. American Journal of Agricultural Economics, 75(1), 113-120. DOI: 10.2307/1242959.

Chiarella, C. (1988). The cobweb model, its instability and the onset of chaos. Economic Modelling, 5(4), 376-384. DOI: 10.1016/0264-9993(88)90010-7.

Collett, P., & Eckmann, J. P. (1980). Iterated Maps on the Interval as Dynamical Systems.

Cetin, N. (1994). EEG’de Kaotik Boyut (Unpublished Ph.D. Thesis). Eskisehir: Anadolu University, Institute of Natural Sciences.

Demir, B. (1999). Dinamik Sistemler ve Noron Sikluslari (Unpublished Ph.D. Thesis). Eskisehir: Anadolu University, Institute of Natural Sciences.

Degrauwe, P., Dewachter, H., & Embrechts, M. (1993). Exchange Rate Theory, Chaotic Models of Foreign Exchange Markets. London: Blackwell Publishers.

Eckmann, J. P., & Ruelle, D. (1985). Ergodic theory of chaos and strange attractors. Reviews of. Modern Physics, 57(3), 617-656. DOI: 10.1103/RevModPhys.57.617.

Eckmann, J. P., Kamphorst, S. O., Ruelle, D., & Ciliberto, S. (1986). Liapunov exponents from time series. Physical Review A, 34(6), 4971-4979. DOI: 10.1103/PhysRevA.34.4971.

Frank, M., & Stengos, T. (1989). Measuring the strangeness of gold and silver rates of return. Review of Economic Studies. 56(4), 553-567. DOI: 10.2307/2297500.

Gleick, J., 1997. Chaos: Making a New Science. Vintage, London.

Harrison, R., Yu, D., Oxley, L., Lu, W., & Donald, G. (1999). Non-linear noise reduction and detecting chaos: some evidence from the S&P composite price index. Mathematics and Computers in Simulation, 48, 497-502. DOI: 10.1016/S0378-4754(99)00029-4.

Hsieh, D. (1989). Testing for nonlinear dependence in daily foreign exchange rates. The Journal of Business, 62(3), 339-368. DOI: 10.1086/296466.

Lee, I. S., Sewell, S.P., & S.R. Stansell. (1993). Nonlinearities in emerging foreign capital markets. Journal of Business Finance and Accounting, 20(2), 237-248. DOI: 10.1111/j.1468-5957.1993.tb00662.x.

Lorenz, E. (1963). Deterministic nonperiodic flow. Journal of Atmospheric Sciences, 20(2), 130-140. DOI: 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

May, R. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459-467. DOI: 10.1007/978-0-387-21830-4_7.

Medio, A. (1992). Chaotic Dynamics: Theory and Applications to Economics. Cambridge University Press.

Nychka, D., Ellner, S., Gallant, R., & McCalrey, D. (1992). Finding chaos in noisy systems. Journal of the Royal Statistical Society B, 54(2), 399-426.

Panas, E. (2001). Long memory and chaotic models of prices on the London Metal Exchange. Resources Policy, 27, 235–246. DOI: 10.1016/S0301-4207(02)00008-9.

Rosenstin, M. T., Collins, J. J., & De Luca, C. JA. (1993). Practical Method for Calculating Largest Lyapunov Exponents from Small Data Sets. Physica D: Nonlinear Phenomena, 65, 117-134. DOI: 10.1016/0167-2789(93)90009-P.

Sakai, K. (2001). Nonlinear Dynamics and Chaos in Agricultural Systems. Netherlands: Elsevier.

Sakai, K., Noguchi, Y., & Asada, S. (2008). Detecting chaos in a citrus orchard: reconstruction of nonlinear dynamics from very short ecological time series. Chaos, Solitons and Fractals, 38(5), 1274-1282. DOI: 10.1016/j.chaos.2007.01.144.

Serletis, A. (1995). Random Walks, Breaking Trend Functions and the Chaotic Structure of the Velocity of the Money. Journal of Business and Economic Statistics, 13(4), 453-458. DOI: 10.1080/07350015.1995.10524619.

Serletis, A., & Gogas, P. (1997). Chaos in East European black market Exchange rates. Research in Economics, 51(4), 359-385. DOI: 10.1006/reec.1997.0050.

Takens, F. (1981). Detecting Strange Attractors in Turbulance. in. Rand DA, Young LS (eds.) Dynamical Systems and Turbulance, Warwick 1980, Springer Verlag: Lecture Notes in Mathematics, 898.

Tilman, D., & Vedin, D. (1993). Oscillation and in the dynamics of a perennial grass. Nature, 353, 653-655. DOI: 10.1038/353653a0.

Tsonis, A. A. (1992). Chaos: From Theory to Applications. New York and London: Plenum Press. DOI: 10.1007/978-1-4615-3360-3.

TURKSTAT, (2010). Tahillar ve Diger Bitkisel Urunler, Tarim Istatistikleri. Ankara: Turkiye Istatistik Kurumu.

Turchin, P., & Taylor, A.D. (1992). Complex dynamics in ecological time series. Ecology, 73(1), 289-305. DOI: 10.2307/1938740.

Wei, A., & Leuthold, R. M. (1998). Long agricultural futures prices. ARCH, Long Memory Chaos Processes, OFOR Paper Number 98(03) DOI: 10.2139/ssrn.126951.

Wolf, A., Swift, J. B., Swinney, H. L., & Vastano, J. A. (1985). Determining lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 16(3), 285-317. DOI: 10.1016/0167-2789(85)90011-9.