Özlale, Ü.Özcan, K. M.2016-02-082016-02-0820070378-4371http://hdl.handle.net/11693/23427This paper utilizes an early warning system in order to measure the likelihood of a financial crisis in an emerging market economy. We introduce a methodology, where we can both obtain a likelihood series and analyze the time-varying effects of several macroeconomic variables on this likelihood. Since the issue is analyzed in a non-linear state space framework, the extended Kalman filter emerges as the optimal estimation algorithm. Taking the Turkish economy as our laboratory, the results indicate that both the derived likelihood measure and the estimated time-varying parameters are meaningful and can successfully explain the path that the Turkish economy had followed between 2000 and 2006. The estimated parameters also suggest that overvalued domestic currency, current account deficit and the increase in the default risk increase the likelihood of having an economic crisis in the economy. Overall, the findings in this paper suggest that the estimation methodology introduced in this paper can also be applied to other emerging market economies as well. © 2007 Elsevier B.V. All rights reserved.EnglishEmerging marketsExtended Kalman filterFinancial crisesAlgorithmsMaximum likelihoodParameter estimationRisk analysisStatistical mechanicsEmerging marketsTime-varying effectsEconomic analysisArticle10.1016/j.physa.2007.03.0311873-2119