Ekonomi Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/410

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Citation - WoS: 17
Citation - Scopus: 20
Assessing the Impact of COVID-19 Pandemic in Turkey With a Novel Economic Uncertainty Index
(Emerald Group Publishing Ltd, 2021) Mugaloglu, Erhan; Polat, Ali Yavuz; Tekin, Hasan; Kilic, Edanur
Purpose This study aims to measure economic uncertainty in Turkey by a novel economic uncertainty index (EUI) employing principal component analysis (PCA). We assess the impact of Covid-19 pandemic in Turkey with our constructed uncertainty index. Design/methodology/approach In order to obtain the EUI, this study employs a dimension reduction method of PCA using 14 macroeconomic indicators that spans from January 2011 to July 2020. The first principal component is picked as a proxy for the economic uncertainty in Turkey which explains 52% of total variation in entire sample. In the second part of our analysis, with our constructed EUI we conduct a structural vector autoregressions (SVAR) analysis simulating the Covid-19-induced uncertainty shock to the real economy. Findings Our EUI sensitively detects important economic/political events in Turkey as well as Covid-19-induced uncertainty rising to extremely high levels during the outbreak. Our SVAR results imply a significant decline in economic activity and in the sub-indices as well. Namely, industrial production drops immediately by 8.2% and cumulative loss over 8 months will be 15% on average. The losses in the capital and intermediate goods are estimated to be 18 and 25% respectively. Forecast error variance decomposition results imply that uncertainty shocks preserve its explanatory power in the long run, and intermediate goods production is more vulnerable to uncertainty shocks than overall industrial production and capital goods production. Practical implications The results indicate that monetary and fiscal policy should aim to decrease uncertainty during Covid-19. Moreover, since investment expenditures are affected severely during the outbreak, policymakers should impose investment subsidies. Originality/value This is the first study constructing a novel EUI which sensitively captures the critical economic/political events in Turkey. Moreover, we assess the impact of Covid-19-driven uncertainty on Turkish Economy with a SVAR model.
G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models
(2021) MUĞALOĞLU Erhan; KILIÇ Edanur; Kılıç, Edanur; Mugaloglu, Erhan
Despite the unemployment data have been recently released as seasonally adjusted, seasonality may still exist in moving average (MA) or auto-regressive (AR) terms. This can be detected by searching for a regular pattern in auto-correlation function (ACF) and partial ACF (PACF) diagrams. Therefore, models that aim to forecast unemployment rates should consider their seasonal properties so as to obtain better mean equation estimations. Univariate models mostly employ integrated ARMA (ARIMA) or generalized auto regressive conditional heteroscedastic (GARCH) models or any combination of them. Once the mean equations are structured better, GARCH estimations of variance equation is expected to perform better accuracy in forecasts. This study first examines the ACF's and PACF's of seasonally adjusted unemployment rate data in G-7 countries for 1995-2019 period. Then it compares the 4-quarter and 8-quarter ahead forecast performance of the seasonal ARIMA (SARIMA) coupled volatility models of GARCH in mean, absolute value GARCH, GJR-GARCH, exponential GARCH and asymmetric GARCH models. The performance of these models is also compared to SARIMA and MA filtered volatility models. The results show that seasonality should be re-examined even in seasonally adjusted unemployment data, since SARIMA models outperform ARIMA models in terms of out of sample forecast errors. Besides SARIMA-GARCH models provide better out of sample prediction accuracy.

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Browsing Ekonomi Bölümü Koleksiyonu by Author "Mugaloglu, Erhan"