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Development of a Bayesian estimator for non-stationary Markov transition probabilities and its application to EU farm structural change
The agricultural sector has experienced substantial structural changes in the past and faces continuing adjustments in the future. The implications of structural change are not only relevant for the sector itself but have broader social, economic and environmental consequences for a region. An understanding of this process is required in order to assess how (agricultural-) policy affects or, if a specific social outcome is desired, can influence this development. A common approach to gain understanding of the process is to model structural change as a Markov process. One problem in the analysis of structural change in the EU is that farm level (micro) data is rarely available such that inference about behaviour of individual farms has to be derived from aggregated (macro) data. Recently, the generalized cross entropy estimator gained popularity in this context since it allows considering prior information such that the often underdetermined "macro data" Markov models can be estimated. However, the way prior information is considered is also the greatest drawback of the approach. Therefore, the project aims to develop a Bayesian framework as an alternative estimator that allows to consider prior information in a more efficient and transparent way. The project will further provide an evaluation of the statistical properties of the estimator as well as an exemplifying application analyzing the effects of single farm payments on agricultural structural change in the EU.
Publications and conference contributions related to the projectJournal articles
Storm, H., T. Heckelei and R.C. Mittelhammer (in press): Bayesian estimation of non-stationary Markov models combining micro and macro data. European Review of Agricultural Economics doi:10.1093/erae/jbv018
Storm, H., T. Heckelei, M. Espinosa and S. Gomez y Paloma (2015): Short Term Prediction of Agricultural Structural Change using Farm Accountancy Data Network and Farm Structure Survey Data. German Journal of Agricultural Economics, 64(3).
Storm, H., K. Mittenzwei and T. Heckelei (2015): Direct payments, spatial competition and farm survival in Norway. American Journal of Agricultural Economics, 97(4):1192-1205. doi:10.1093/ajae/aau085Working papers and conference contributions
Storm, H., T. Heckelei and R.C. Mittelhammer (2014): Bayesian estimation of non-stationary Markov models combining micro and macro data. Paper presented at the 2014 EAAE International Congress, August 26-29, 2014, Ljubljana, Slovenia. Link
Storm, H., K. Mittenzwei and T. Heckelei (2013): Direct payments, spatial competition and farm survival in Norway. Paper to be presented at the 2013 AAEA Annual Meetings, Washington, DC, August 4-6, 2013. Link
Storm, H. and K. Mittenzwei (2013): Farm survival and direct payments in the Norwegian farm sector, Norwegian Agricultural Economics Research Institute, Discussion paper 2013-5 Link
Storm, H. and T. Heckelei (2012): Predicting agricultural structural change using census and sample data. Poster presented at the 2012 AAEA Annual Meetings, Seattle, Washington, August 12-14 . Link
Storm, H., T. Heckelei and R.C. Mittelhammer (2011): Bayesian estimation of non-stationary Markov models combining micro and macro data. Discussion Paper 2011:2, Institute for Food and Resource Economics, University of Bonn.
Storm, H. and T. Heckelei (2011): Bayesian estimation of non-stationary Markov models combining micro and macro data. Poster presented at the 2011 AAEA Annual Meetings, Pittsburgh, USA, July 24-26. LinkMonographs
Storm, H. (2014): Methods of analysis and empirical evidence of farm structural change. Dissertation, University of Bonn, URN: urn:nbn:de:hbz:5n-37174.
Last updated: Monday, September 07, 2015
- Research meetings must be more sustainable, Sanz-Cobena, A., Alessandrini, R., Bodirsky, B. L., Springmann, M., Aguilera, E., Amon, B., Bartolini, F., Geupel, M., Grizzetti, B., Kugelberg, S., Latka, C., Liang, X., Milford, A. B., Musinguzi, P., Ng, E. L., Suter, H., Leip, A. (2020): Nature Food 1: 187-189.Modelling food security: Bridging the gap between the micro and the macro scale, Müller, B., Hoffmann, F., Heckelei, T., Müller, C., Hertel, T. W., Polhill, J. G., van Wijk, M., Achterbosch, T., Alexander, P., Brown, C., Kreuer, D., Ewert, F., Ge, J., Millington, J. D. A., Seppelt, R., Verburg, P. H., Webber, H. (2020): Global Environmental Change 63: 16 pages.Forecasting International Sugar Prices: A Bayesian Model Average Analysis, Amrouk, E. M., Heckelei, T. (2020): Sugar Tech: 11 pages.Brexit: an economy-wide impact assessment on trade, immigration, and foreign direct investment, Jafari, Y., Britz, W. (2020): Empirica 47(1): 17-52.Efficiency differentials in resource-use among smallholder cassava farmers in southwestern Cameroon, Molua, E. L., Tabe-Ojong, M. P., Meliko, M. O., Nkenglefac, M. F., Akamin, A. (2019): Development in Practice: 11 pages.New insights on efficiency and productivity analysis: Evidence from vegetable-poultry integration in rural Tanzania, Habiyaremye, N., Tabe-Ojong, M. P., Ochieng, J., Chagomoka, T. (2019): Scientific African 6(e00190): 11 pages.Explaining farm structural change in the European agriculture: a novel analytical framework, Neuenfeldt, S., Gocht, A., Heckelei, T., Ciaian, P. (2019): European Review of Agricultural Economics 46(5): 713-768.Machine learning in agricultural and applied economics Storm, H., Baylis, K., Heckelei, T. (2019): European Review of Agricultural Economics, jbz033: 44 pagesInterdependence between cash crop and staple food international prices across periods of varying financial market stress Amrouk, E. M., Grosche, S., Heckelei, T. (2019): Applied Economics: 16 pages.