{"id":11013,"date":"2019-03-20T14:36:54","date_gmt":"2019-03-20T12:36:54","guid":{"rendered":"https:\/\/modecon.mnau.edu.ua\/?p=11013"},"modified":"2019-03-29T12:25:26","modified_gmt":"2019-03-29T10:25:26","slug":"early-warning-system-logit-probit-introduction","status":"publish","type":"post","link":"https:\/\/modecon.mnau.edu.ua\/en\/early-warning-system-logit-probit-introduction\/","title":{"rendered":"Shvets Serhii, Early Warning System: Logit\/Probit introduction for Ukraine"},"content":{"rendered":"

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JEL<\/strong> Classification:<\/strong> D 11; M 31; \u041e 31.<\/td>\nDOI<\/b>: https:\/\/doi.org\/10.31521\/modecon.V13(2019)-41<\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_column_text]Shvets Serhii<\/strong>, Candidate of Economic Sciences, Associate Professor, Institute for Economics and Forecasting of National Academy of Sciences of Ukraine, Department of Modeling and Forecasting of Economic Development, Kyiv, Ukraine<\/p>\n

ORCID ID<\/strong>: 0000-0002-3102-9784<\/a>
\ne-mail<\/strong>: smserg@ukr.net<\/a><\/p>\n

Early Warning System: Logit\/Probit introduction for Ukraine<\/h2>\n

Abstract. Introduction. <\/strong>There have been several crises in the world economy since the end of the last century. The developing economies were ones that have suffered the most considering the level of openness, the weak institutional framework, and the market vulnerability to unpredictable shocks. One of the instruments widely used to prevent crises is the Early Warning System models.<\/p>\n

Purpose. <\/strong>The paper pursues the goal to develop Early Warning System model using logit\/probit regression to determine early warning arguments and their appropriate thresholds for Ukraine.<\/p>\n

Results. <\/strong>The regression algorithm corresponds to the determination of the dependent binary variable associated with the output gap followed by the selection of independent early warning components. The method of integral composite coincident and leading indicators employs to reproduce the quarterly dynamics of real GDP.<\/p>\n

    \n
  1. The arranged coincident indicator depends on the industrial production, agriculture, construction, and the domestic retail trade. The expanded to monthly data quarterly GDP is exploited for the evaluation of the output gap using multivariate filter and Okun\u2019s law definition. The 2% difference between the actual and potential GDP is applied for generating binary data of the output gap.<\/li>\n<\/ol>\n
      \n
    1. The two components of the integral leading index, the world prices of wheat and Russian gas plus the world price of steel figure out one independent variable of logit\/probit regression marked as the world price of raw materials. Another one independent variable is arranged by monitoring the demand-supply gap.<\/li>\n
    2. The obtained probit results are more statistically significant in comparison to the logit model. The marginal effects are 1% for the world price of raw materials and 3% for the demand-supply gap.<\/li>\n<\/ol>\n

      Conclusions. <\/strong>Considering the higher marginal grade, between two early warning components the demand-supply gap is more sensitive for predicting crises in Ukraine. In the following study, the other early warning components have to be examined for higher predicting capability.<\/p>\n

      Keywords: <\/strong>early warning systems; logit\/probit modeling; integral composite indicators; output gap; Okun\u2019s law.<\/p>\n

      References:<\/strong><\/p>\n

        \n
      1. Kaminsky, G., Lizondo, S. & Reinhart, C. (1998). Leading indicators of currency crisis. IMF Staff Papers,<\/em> 45(1), 1 48.<\/li>\n
      2. Frankel, J. & Rose, A. (1996). Currency crashes in emerging markets: an empirical treatment. Journal of International Economics,<\/em> 41(3\/4), 351 366. DOI: https:\/\/doi.org\/10.1016\/S0022-1996(96)01441-9.<\/li>\n
      3. Percic, S., Apostoaie, C-M. & Cocris, V. (2013). Early Warning Systems for financial crises. A critical approach. CES Working Papers,<\/em> 5(1), 77 88.<\/li>\n
      4. Valencia, F. & Laeven, L. (2018). Systemic banking crises revisited. IMF Working Papers, <\/em>WP\/18\/206, International Monetary Fund, 48.<\/li>\n
      5. Frankel, J. & Saravelos, G. (2012). Can leading indicators assess country vulnerability? Evidence from the 2008\u201309 global financial crisis. Journal of International Economics,<\/em> 87(2), 216-231. DOI: https:\/\/doi.org\/10.1016\/j.jinteco.2011.12.009.<\/li>\n
      6. Csortos, O. & Szalai, Z. (2014). Early warning indicators: financial and macroeconomic imbalances in Central and Eastern European countries. MNB Working Papers,<\/em> 2, Magyar Nemzeti Bank (Central Bank of Hungary), 40.<\/li>\n
      7. Domonkos, T. et al. (2017). Analyzing the Relevance of the MIP Scoreboard\u2019s Indicators. National Institute Economic Review,<\/em> 239(1), 32\u201352. DOI: https:\/\/doi.org\/10.1177\/002795011723900112.<\/li>\n
      8. Alichi, A. (2015). A New Methodology for Estimating the Output Gap in the United States. IMF Working Paper,<\/em> WP\/15\/144, International Monetary Fund, 17.<\/li>\n
      9. Bohdan, \u0406., Konovalenko, D. & Yara, \u0422. (2018). Economic and mathematical models for fiscal policy testing. Finansy Ukrayiny,<\/em> 3, 43 66.<\/li>\n
      10. Mongardini, J. & Saadi-Sedik, T. (2003). Estimating indexes of coincident and leading indicators: an application to Jordan. IMF Working Papers,<\/em> WP\/03\/170, International Monetary Fund, 33.<\/li>\n
      11. Cerovic, S. et al. (2018). Predicting fiscal crises. IMF Working Paper,<\/em> WP\/18\/181, International Monetary Fund, 42.<\/li>\n<\/ol>\n

        [\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Received:<\/strong> 18 January 2019<\/p>\n

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        Shvets, S. M. (2019). Early Warning System: Logit\/Probit introduction for Ukraine. Modern Economics<\/em>, 13(2019), 266-271. DOI: https:\/\/doi.org\/10.31521\/modecon.V13(2019)-41.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        [\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<< Back to contents<\/a>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/section>","protected":false},"excerpt":{"rendered":"

        [vc_row][vc_column][vc_row_inner][vc_column_inner][vc_column_text] JEL Classification: D 11; M 31; \u041e 31. DOI: https:\/\/doi.org\/10.31521\/modecon.V13(2019)-41 [\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_column_text]Shvets Serhii, Candidate of Economic Sciences, Associate Professor, Institute for Economics and Forecasting of National Academy of Sciences of Ukraine, Department of Modeling and Forecasting of Economic Development, Kyiv, Ukraine ORCID ID: 0000-0002-3102-9784 e-mail: smserg@ukr.net Early Warning System: Logit\/Probit introduction for Ukraine Abstract. Introduction.
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