2019 | 24 | 2 | 177-201
Article title

Concerns on the issue of defence expenditure in the post-crisis Greece

Title variants
Languages of publication
The paper aims to tackle a controversial issue, namely the anticipated developments regarding defence expenditure once the Greek economy returns to growth. Such a comeback is expected to occur following a prolonged recessionary period during which defence spending cuts were a top priority, as recommended by the IMF, the ECB and the EC, members of the so-called “Troika”. The paper uses both conventional econometrics as well as neural networks to consider and evaluate the hierarchy’s ordering of the determinants used in such a demand for defence expenditure based on their explanatory power. While the role of property resources is certainly pronounced, as expected, human resources variables also seem to be able to explain defence spending developments, especially in the recent past. A forecasting investigation based on this background points to a number of interesting conclusions on the anticipated developments concerning defence spending in the future as well as on the determinants of such developments which might represent a threat to NATO cohesion.
Physical description
  • Adya, M. and Collopy F, 1998. How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation, Journal of Forecasting, 17 (5-6), pp. 481-495.
  • Andreou, A, S and Zombanakis, G., A., 2000. Financial versus human resources in the Greek-Turkish arms race: A forecasting investigation using artificial neural networks. Defence and Peace Economics, 11(2), pp. 403-426.
  • Andreou, A. S., Parsopoulos, K. E., Vrachatis, M. N., Zombanakis, G. A., 2002. Optimal Versus Required Defence Expenditure: The Case of the Greek-Turkish Arms Race, Defence and Peace Economics, 13, pp. 329–347.
  • Andreou A. S. and G. A. Zombanakis 2006, The Arms Race between Greece and Turkey: Commenting on a Major Unresolved Issue, Peace Economics, Peace Science and Public Policy, 12(1).
  • Andreou A. G. A. Zombanakis, 2011. Financial Versus Human Resources In The Greek--Turkish Arms Race 10 Years On: A Forecasting Investigation Using Artificial Neural Networks, Defence and Peace Economics, Taylor & Francis Journals, 22(4), pp. 459-469.
  • Azoff , E. M., 1994. Neural Network Time Series Forecasting of Financial Markets. John Wiley and Sons, N.Y.
  • Bahrammirzaee, A., 2010. A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), pp. 1165–1195.
  • Beck, N., King, G., and Zeng, L., 2004. Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi and Grynaviski. American Political Science Review 98(2), pp. 379-389.
  • Biswas B. and Ram R., 1986. Military Expenditures and Economic Growth in Less Developed Countries: An Augmented Model and Further Evidence, Economic Development and Cultural Change 34(2), pp. 361-372.
  • Botev, A., Lever, G., and Barber, D. 2017. Nesterov’s accelerated gradient and momentum as approximations to regularised update descent. In International joint conference on neural networks, pp. 1899–1903.
  • Bottou, L. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of compstat’, pp. 177–186.
  • Brauer, J. 2002. Survey and Review of the Defense Economics Literature on Greece and Turkey: What Have We Learned? Defence and Peace Economics 13(2), pp. 85-107.
  • Chollet, F., 2015. Keras., 2015.
  • Dunne, P. and Perlo-Freeman, S. 2003. Th e Demand for Military Spending in Developing Countries. International Review of Applied Economics. 17 (1).
  • Fumitaka F. Mikio O. and Mohd A. K. 2016. Military expenditure and economic development in China: an empirical inquiry, Defence and Peace Economics, 27 (1), pp. 137-160, DOI: 10.1080/10242694.2014.898383.
  • Hahnloser, R. H., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J., and Seung, H. S. 2000. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature, 405(6789), pp. 947-951.
  • Hartley K., 2012. The Economics of Defence Policy: A New Perspective, Routledge .
  • Hartley K., and N. Hooper, 1990. Th e Economics of Defence, Disarmament and Peace - An Annotated Bibliography, Edward Elgar, Albershot.
  • Hartley, K., Sandler, T. (ed.), 1995. Handbook of Defense Economics, Elsevier, volume 1.
  • Hewitt, D. 1992. Military expenditures worldwide: determinants and trends, 1972– 1988. Journal of Public Policy, 12(02), pp. 105-152.
  • Hill T., O’Connor M. and Remus W., 1996. Neural network models for time series forecasts, Management Science, vol. 42, no. 7, pp. 1082-1092.
  • IMF, 2010. Greece: First Review Under the Stand-By Arrangement, Country Report No. 10/286.
  • IMF, 2012. Greece: Request for Extended Arrangement Under the Extended Fund Facility-Staff Report; Staff Supplement; Press Release on the Executive Board Discussion; and Statement by the Executive Director for Greece, Country Report No. 12/57.
  • IMF, 2014. Greece: Fifth Review Under the Extended Arrangement Under the Extended Fund Facility and Request for Waiver of Nonobservance of Performance Criterion and Rephasing of Access; Staff Report; Press Release and Statement by the Executive.
  • Director for Greece, Country Report No. 14/151.
  • Jones-Lee M. 1990. Defence Expenditure and the Economics of Safety, Defence Economics,1 (1), pp. 13-16.
  • Knorr, K. 1985. Burden Sharing in NATO: Aspects of US Policy, Orbis 29(3), pp. 517-36.
  • Kuo, C. and Reitsch, A., 1995. Neural networks vs. conventional methods of forecasting. The Journal of Business Forecasting, 17-22.
  • Looney, R.E. and Mehay, S.L. 1990. United States defense expenditures: trends and analysis. In The Economics of Defense Spending: An International Survey, London: Routledge.
  • Ministry of Finance 2017. Mid-Term Fiscal Strategy Framework 2018-2021, Athens.
  • Murdoch, J. C. & Sandler, T., 1982. A Theoretical and Empirical Analysis of NATO, Journal of Conflict Resolution, 26(2), pp. 237-263.
  • Murdoch, J C & Sandler, T, 1985. Australian Demand for Military Expenditures: 1961-1979, Australian Economic Papers, (44), pp. 142-153.
  • Okamura, M. 1991. Estimating the Impact of the Soviet Union’s Threat on the United States-Japan Alliance: A Demand System Approach, Review of Economics and Statistics, 73, pp. 200-207.
  • Qian, N. 1999. On the momentum term in gradient descent learning algorithms. Neural networks, 12(1), pp. 145–151.
  • Ragies I.2017. Th e 2% Target: Understanding Defence Capabilities and Commitments within Transatlantic Alliance, Paper presented in the “Future of Armed Forces 2040” Conference, Defence Advanced Research Institute/ G.S. Rakovski National Defence College (RNDC) & Armed Forces Communications and Electronics Association (AFCEA) International/ SEER, 26-27 September, 2017, Sofi a, Bulgaria.
  • Sandler and Hartley (eds.) 1995. Handbook of Defence Economics, Elsevier.
  • Sezgin S., 2000. A note on defence spending in turkey: New findings, Defence and Peace Economics, 11(2), pp. 427-435.
  • Smith R. P. 1980. The Demand for Military Expenditure, The Economic Journal, 90 (360), pp. 811-820.
  • Smith R. P. 1989. Models of Military Expenditures, Journal of Applied Econometrics, 4(4), pp. 345-359.
  • Smith, R. P 1990. Defence procurement and industrial structure in the U.K, International Journal of Industrial Organization, Elsevier, 8(2), pp. 185-205.
  • Taylor, M.P. 1995. Th e Economics of Exchange Rates. Journal of Economic Literature, (33), pp. 13-47.
  • Ying Z., Rui W., and Dongqi Y., 2017. Does defence expenditure have a spillover effect on income inequality? A cross-regional analysis in China, Defence and Peace Economics, 28(6), pp. 731-749, DOI: 10.1080/10242694.2016.1245812.
  • Ying Zhang, Xiaoxing Liu, Jiaxin Xu and Rui Wang 2017 Does military spending promote social welfare? A comparative analysis of the BRICS and G7 countries, Defence and Peace Economics, 28 (6), pp. 686-702, DOI: 10.1080/10242694.2016.1144899.
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.