Yazar "Soybilgen, Baris" seçeneğine göre listele

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    A general model for financial crises: An application to eurozone crisis
    (Elsevier, 2020) Yener, Haluk; Soybilgen, Baris; Stengos, Thanasis
    We provide a mathematical framework to spot the non-resilient periods of an economy and understand the reason why an economy becomes non-resilient. Our non-resilience indicator spots the distressful periods of sixteen European economies successfully over the course of almost thirty years. To understand why these economies became non-resilient, we solve a problem related to survival analysis and establish an analytic relationship between the leverage level of an economy and its macro fundamentals. We apply our approach to the same group of countries and show with a vector autoregressive model why certain indebted European economies still struggle years after the crisis.
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    Determinants of Turkish female labour force participation: an analysis with manufacturing firm-level data
    (Routledge Journals, Taylor & Francis Ltd, 2020) Karamollaoglu, Nazli; Soybilgen, Baris
    Compared to other developing countries, Turkey has a very low female labour participation rate. Previous studies usually focus on the labour supply side of female employment. Unlike the previous literature, this paper investigates firm-level determinants of female employment in manufacturing firms using a unique micro data set constructed using different sources. After controlling for geographical variation, firm, and industry-specific factors, our results show that larger firms, exporter firms, firms with higher part-time worker ratio, and foreign-owned firms have higher female employment rate whereas younger firms, firms with higher labour productivity, and firms with long working hours have lower female employment rate.
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    Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate
    (Elsevier Science Bv, 2018) Soybilgen, Baris; Yazgan, Ege
    The paper analyzes the point and density predictive performance of alternative nowcast combination schemes in the context of bridge equations for the Turkish unemployment rate. Furthermore, we also nowcast the unemployment rate by using dynamic factor models (DFMs). Our results indicate that most of the sophisticated forecast combination methods have better predictive accuracy than the simple forecast combinations, especially in higher forecast horizons, which constitutes a case for the nowcast combination puzzle. Furthermore, most of bridge equations with the advanced forecast combination schemes usually outperform DFMs which are assumed to be superior to the bridge equations. This latter result indicates that bridge equations augmented by advanced forecast combination schemes may be a viable alternative to the DFM. Finally, we show that real and labor variables play the most important role for nowcasting the Turkish unemployment rate, whereas financial variables and surveys do not seem to be beneficial. Overall, our results indicate that advanced combination schemes can increase the performance of nowcasting models.
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    Evaluating the effect of geopolitical risks on the growth rates of emerging countries
    (Economics Bulletin, 2019) Soybilgen, Baris; Kaya, Huseyin; Dedeoglu, Dincer
    In this study, we analyze the relationship between geopolitical risks and growth using annual panel data from 18 emerging countries for the period from 1986 to 2016. For a robustness check, we use panel data with 5-year intervals. The news-based indices of Caldara and Iacoviello (2018) were used as a proxy for geopolitical risks. Our results show that the effect of geopolitical risks on growth rates is negative and significant. A 10 point increase in the geopolitical risk index causes a 0.2-0.4% decline in the GDP growth rate.
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    Identifying Turkish business cycle regimes in real time
    (Routledge Journals, Taylor & Francis Ltd, 2020) Soybilgen, Baris
    In this study, we analyse the real-time identification performance of the BBQ method and the Markov switching (MS) model in the case of Turkey by comparing their real-time and ex-post identification results between 1997M01-2017M12. We show that both the BBQ and the MS methodologies identify the nearly same turning point dates for the Turkish economy both ex-post and in real time by using a pseudo real-time data set. We also calculate the real-time identification lag of models and show that the MS model and the BBQ method identify a turning point with a 3-4 months lag and a 6 months lag, respectively. Finally, we show that data revisions do not have a significant impact on the real-time identification performance of the models between 2005M01-2017M12.
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    Inflation aversion in Turkey
    (Elsevier, 2023) Nebioglu, Deniz; Soybilgen, Baris
    Inflation has been at the center of policy debates in Turkey since the 1970s. Although the country experienced a rapid disinflation period during the early 2000s, Turkish inflation has always remained high compared to other developing countries. Disinflationary policies require a long-run commitment of policymakers backed by public consensus and strong institutions. In this paper, we aim to understand the factors that shape the public attitudes of Turkish citizens towards inflation for the period between 2004 and 2020 using Eurobarometer data. We find that belonging to a vulnerable class increases the probability of being inflation averse, while favorable expectations for the future, being on the right of the political spectrum, and trust in politicians decrease inflation aversion.
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    Nowcasting the New Turkish GDP
    (Economics Bulletin, 2018) Soybilgen, Baris; Yazgan, Ege
    In this study, we predict year-on-year and quarter-on-quarter Turkish GDP growth rates between 2012:Q1 and 2016:Q4 with a medium-scale dataset. Our proposed model outperforms both the competing dynamic factor model (DFM) and univariate benchmark models. Our results suggest that in nowcasting current GDP, all relevant information is released within the contemporaneous quarter; hence, no predictive power is added afterwards. Moreover, we show that the inclusion of construction/ service sector variables and credit variables improves the prediction accuracy of the DFM.
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    Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors
    (Springer, 2021) Soybilgen, Baris; Yazgan, Ege
    In this study, we nowcast quarter-over-quarter US GDP growth rates between 2000Q2 and 2018Q4 using tree-based ensemble machine learning models, namely, bagged decision trees, random forests, and stochastic gradient tree boosting. To solve the ragged edge problem and reduce the dimension of the data set, we adopt a dynamic factor model. Dynamic factors extracted from 10 groups of financial and macroeconomic variables are fed to machine learning models for nowcasting US GDP. Our results show that tree-based ensemble models usually outperform linear dynamic factor models. Factors obtained from real variables appear to be more influential in machine learning models. The impact of factors derived from financial and price variables can only become important in predicting GDP after the great financial crisis of 2008-9, reflecting the effect extra loose monetary policies implemented in the period following the crisis.