Yıl: 2021 Cilt: 16 Sayı: 61 Sayfa Aralığı: 228 - 247 Metin Dili: İngilizce İndeks Tarihi: 07-06-2021

G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models

Öz:
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.
Anahtar Kelime:

G7 Ülkeleri İşsizlik Oranı Tahminleri: SARIMA-GARCH Model Karşılaştırması

Öz:
İşsizlik verilerinin yakın zamanda mevsimsellikten arındırılmış olarak yayınlanmış olmasına rağmen,mevsimsellik hareketli ortalama (MA) veya oto-regresif (AR) terimlerde hala var olabilir. Bu, oto-korelasyonfonksiyonu (ACF) ve kısmi ACF (PACF) diyagramlarında düzenli bir model arayarak tespit edilebilir. Bunedenle, işsizlik oranlarını tahmin etmeyi amaçlayan modeller, daha iyi ortalama denklem tahminleri elde etmekiçin mevsimsellik özelliklerini dikkate almalıdır. Tek değişkenli modeller çoğunlukla entegre ARMA (ARIMA)veya genelleştirilmiş oto-regresif heteroskedastik (GARCH) modelleri veya bunların herhangi birkombinasyonunu kullanır. Ortalama denklemler daha iyi yapılandırıldıktan sonra, GARCH varyans denklemitahminlerinin tahminlerde daha doğru sonuçlar vermesi beklenir. Bu çalışmada ilk olarak, 1995-2019 dönemiiçin G-7 ülkelerindeki mevsimsellikten arındırılmış işsizlik oranı verilerinin ACF'leri ve PACF'leriincelenmektedir. Daha sonra, GARCH'ın mevsimsel ARIMA (SARIMA) bağlı oynaklık modellerinin ortalama,mutlak değer GARCH, GJR-GARCH, üstel GARCH ve asimetrik GARCH modellerinin 4 çeyrek ve 8 çeyrekileriye dönük tahmin performansını karşılaştırır. Bu modellerin performansı da SARIMA ve MA filtreli volatilitemodelleriyle karşılaştırılmıştır. Sonuçlar, mevsimselliğin mevsimsellikten arındırılmış işsizlik verilerinde bileyeniden incelenmesi gerektiğini göstermektedir, çünkü SARIMA modelleri örneklem dışı tahmin hatalarıaçısından ARIMA modellerinden daha iyi performans göstermektedir. SARIMA-GARCH modellerinin yanı sıradaha iyi örneklem dışı tahmin doğruluğu sağlar.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Askitas, N., & Zimmermann, K. F. 2009. Google econometrics and unemployment forecasting.
  • Barnichon, R., Nekarda, C. J., HATZIUS, J., STEHN, S. J., & PETRONGOLO, B. 2012. The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market [with Comments and Discussion]. Brookings Papers on Economic Activity, 83-131.
  • Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Box, G. E., & Pierce, D. A. 1970. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509-1526.
  • Caiado, J. 2009. Performance of combined double seasonal univariate time series models for forecasting water demand. Journal of Hydrologic Engineering, 15(3), 215-222.
  • Chang, T., & Lee, C. H. 2011. Hysteresis in unemployment for G-7 countries: Threshold unit root test. Romanian Journal of Economic Forecasting, 4, 5-14.
  • Crawford, G. W., & Fratantoni, M. C. 2003. Assessing the forecasting performance of regime‐switching, ARIMA and GARCH models of house prices. Real Estate Economics, 31(2), 223-243.
  • D’Amuri, F. 2009. Predicting unemployment in short samples with internet job search query data.
  • D’Amuri, F., & Marcucci, J. 2010. 'Google it!'Forecasting the US unemployment rate with a Google job search index.
  • Datta, G. S., Lahiri, P., Maiti, T., & Lu, K. L. 1999. Hierarchical Bayes estimation of unemployment rates for the states of the US. Journal of the American Statistical Association, 94(448), 1074-1082.
  • Ding, Z., Granger, C. W., & Engle, R. F. 1993. A long memory property of stock market returns and a new model. Journal of empirical finance, 1(1), 83-106.
  • Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  • Floros, C. 2005. Forecasting the UK unemployment rate: model comparisons. International Journal of Applied Econometrics and Quantitative Studies, 2(4), 57-72.
  • Fondeur, Y., & Karamé, F. 2013. Can Google data help predict French youth unemployment?. Economic Modelling, 30, 117-125.
  • Funke, M. 1992. Time‐series forecasting of the German unemployment rate. Journal of Forecasting, 11(2), 111- 125.
  • Gustavsson, M., & Österholm, P. 2010. The presence of unemployment hysteresis in the OECD: what can we learn from out-of-sample forecasts?. Empirical Economics, 38(3), 779-792.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
  • Johnes, G. 1999. Forecasting unemployment. Applied Economics Letters, 6(9), 605-607.
  • Jones, S. A., Joy, M. P., & Pearson, J. O. N. 2002. Forecasting demand of emergency care. Health care management science, 5(4), 297-305.
  • Khan Jaffur, Z. R., Sookia, N. U. H., Nunkoo Gonpot, P., & Seetanah, B. 2017. Out-of-sample forecasting of the Canadian unemployment rates using univariate models. Applied Economics Letters, 24(15), 1097- 1101.
  • Kurita, T. 2010. A Forecasting Model for Japan''s Unemployment Rate. Eurasian Journal of Business and Economics, 3(5), 127-134.
  • Ljung, G. M., & Box, G. E. 1978. On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
  • Makridakis, S. 1993. Accuracy measures: theoretical and practical concerns. International journal of forecasting, 9(4), 527-529.
  • Milas, C., & Rothman, P. 2008. Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts. International Journal of Forecasting, 24(1), 101-121.
  • Montgomery, A. L., Zarnowitz, V., Tsay, R. S., & Tiao, G. C. 1998. Forecasting the US unemployment rate. Journal of the American Statistical Association, 93(442), 478-493.
  • Moshiri, S., & Brown, L. 2004. Unemployment variation over the business cycles: a comparison of forecasting models. Journal of Forecasting, 23(7), 497-511.
  • Nkwatoh, L. 2012. Forecasting unemployment rates in Nigeria using univariate time series models. International Journal of Business and Commerce, 1(12), 33-46.
  • Nyoni, T. 2018. Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis. Dimorian Review, 5(6), 16-40.
  • Nyoni, T., & Nathaniel, S. P. 2018. Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models.
  • Proietti, T. 2003. Forecasting the US unemployment rate. Computational Statistics & Data Analysis, 42(3), 451- 476.
  • Phillips, P. C., & Perron, P. 1988. Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
  • Said, S. E., & Dickey, D. A. 1984. Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
  • Sigauke, C., & Chikobvu, D. 2011. Prediction of daily peak electricity demand in South Africa using volatility forecasting models. Energy Economics, 33(5), 882-888.
  • Simionescu, M. 2013. The Performance of Unemployment Rate Predictions in Romania. Strategies to Improve the Forecasts Accuracy. Review of Economic Perspectives, 13(4), 161-175.
  • Tan, Z., Zhang, J., Wang, J., & Xu, J. 2010. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Applied Energy, 87(11), 3606-3610.
  • The World Bank. DataBank. Available at: https://databank.worldbank.org/reports.aspx?source=2&series=SL.UEM.TOTL.ZS&country=. [Accessed 29 September 2020].
  • Tran, Q. T., Ma, Z., Li, H., Hao, L., & Trinh, Q. K. 2015. A multiplicative seasonal ARIMA/GARCH model in EVN traffic prediction. International Journal of Communications, Network and System Sciences, 8(04), 43.
  • Tsay, R. S., & Tiao, G. C. 1984. Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARMA models. Journal of the American Statistical Association, 79(385), 84-96.
  • Tsay, R. S. 2005. Analysis of financial time series (Vol. 543). John wiley & sons.
  • Xu, W., Li, Z., Cheng, C., & Zheng, T. 2013. Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 33-42.
  • Zhang, Y., Haghani, A., & Zeng, X. 2014. Component GARCH models to account for seasonal patterns and uncertainties in travel-time prediction. IEEE Transactions on Intelligent Transportation Systems, 16(2), 719-729.
APA Mugaloglu E, KILIÇ E (2021). G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. , 228 - 247.
Chicago Mugaloglu Erhan,KILIÇ Edanur G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. (2021): 228 - 247.
MLA Mugaloglu Erhan,KILIÇ Edanur G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. , 2021, ss.228 - 247.
AMA Mugaloglu E,KILIÇ E G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. . 2021; 228 - 247.
Vancouver Mugaloglu E,KILIÇ E G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. . 2021; 228 - 247.
IEEE Mugaloglu E,KILIÇ E "G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models." , ss.228 - 247, 2021.
ISNAD Mugaloglu, Erhan - KILIÇ, Edanur. "G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models". (2021), 228-247.
APA Mugaloglu E, KILIÇ E (2021). G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. Journal of Yasar University, 16(61), 228 - 247.
Chicago Mugaloglu Erhan,KILIÇ Edanur G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. Journal of Yasar University 16, no.61 (2021): 228 - 247.
MLA Mugaloglu Erhan,KILIÇ Edanur G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. Journal of Yasar University, vol.16, no.61, 2021, ss.228 - 247.
AMA Mugaloglu E,KILIÇ E G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. Journal of Yasar University. 2021; 16(61): 228 - 247.
Vancouver Mugaloglu E,KILIÇ E G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models. Journal of Yasar University. 2021; 16(61): 228 - 247.
IEEE Mugaloglu E,KILIÇ E "G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models." Journal of Yasar University, 16, ss.228 - 247, 2021.
ISNAD Mugaloglu, Erhan - KILIÇ, Edanur. "G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models". Journal of Yasar University 16/61 (2021), 228-247.