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G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models

Yıl 2021, , 228 - 247, 31.01.2021

Erhan Muğaloğlu , Edanur Kılıç

https://doi.org/10.19168/jyasar.803807

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-korelasyon fonksiyonu (ACF) ve kısmi ACF (PACF) diyagramlarında düzenli bir model arayarak tespit edilebilir. Bu nedenle, işsizlik oranlarını tahmin etmeyi amaçlayan modeller, daha iyi ortalama denklem tahminleri elde etmek için mevsimsellik özelliklerini dikkate almalıdır. Tek değişkenli modeller çoğunlukla entegre ARMA (ARIMA) veya genelleştirilmiş oto-regresif heteroskedastik (GARCH) modelleri veya bunların herhangi bir kombinasyonunu kullanır. Ortalama denklemler daha iyi yapılandırıldıktan sonra, GARCH varyans denklemi tahminlerinin tahminlerde daha doğru sonuçlar vermesi beklenir. Bu çalışmada ilk olarak, 1995-2019 dönemi için G-7 ülkelerindeki mevsimsellikten arındırılmış işsizlik oranı verilerinin ACF'leri ve PACF'leri incelenmektedir. 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 çeyrek ileriye dönük tahmin performansını karşılaştırır. Bu modellerin performansı da SARIMA ve MA filtreli volatilite modelleriyle karşılaştırılmıştır. Sonuçlar, mevsimselliğin mevsimsellikten arındırılmış işsizlik verilerinde bile yeniden incelenmesi gerektiğini göstermektedir, çünkü SARIMA modelleri örneklem dışı tahmin hataları açısından ARIMA modellerinden daha iyi performans göstermektedir. SARIMA-GARCH modellerinin yanı sıra daha iyi örneklem dışı tahmin doğruluğu sağlar.

Anahtar Kelimeler

seasonality garch sarima unemployment forecast

Kaynakça

  • 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.
  • 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.

G7 Ülkeleri İşsizlik Oranı Tahminleri: SARIMA-GARCH Model Karşılaştırması

Yıl 2021, , 228 - 247, 31.01.2021

Erhan Muğaloğlu , Edanur Kılıç

https://doi.org/10.19168/jyasar.803807

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-korelasyon fonksiyonu (ACF) ve kısmi ACF (PACF) diyagramlarında düzenli bir model arayarak tespit edilebilir. Bu nedenle, işsizlik oranlarını tahmin etmeyi amaçlayan modeller, daha iyi ortalama denklem tahminleri elde etmek için mevsimsellik özelliklerini dikkate almalıdır. Tek değişkenli modeller çoğunlukla entegre ARMA (ARIMA) veya genelleştirilmiş oto-regresif heteroskedastik (GARCH) modelleri veya bunların herhangi bir kombinasyonunu kullanır. Ortalama denklemler daha iyi yapılandırıldıktan sonra, GARCH varyans denklemi tahminlerinin tahminlerde daha doğru sonuçlar vermesi beklenir. Bu çalışmada ilk olarak, 1995-2019 dönemi için G-7 ülkelerindeki mevsimsellikten arındırılmış işsizlik oranı verilerinin ACF'leri ve PACF'leri incelenmektedir. 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 çeyrek ileriye dönük tahmin performansını karşılaştırır. Bu modellerin performansı da SARIMA ve MA filtreli volatilite modelleriyle karşılaştırılmıştır. Sonuçlar, mevsimselliğin mevsimsellikten arındırılmış işsizlik verilerinde bile yeniden incelenmesi gerektiğini göstermektedir, çünkü SARIMA modelleri örneklem dışı tahmin hataları açısından ARIMA modellerinden daha iyi performans göstermektedir. SARIMA-GARCH modellerinin yanı sıra daha iyi örneklem dışı tahmin doğruluğu sağlar.

Anahtar Kelimeler

mevsimsellik SARIMA GARCH tahmin işsizlik

Kaynakça

  • 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.
  • 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.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Erhan Muğaloğlu 0000-0001-5362-6259

Edanur Kılıç 0000-0002-0873-0011

Yayımlanma Tarihi 31 Ocak 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Muğaloğlu, E., & Kılıç, E. (2021). G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models. Yaşar Üniversitesi E-Dergisi, 16(61), 228-247. https://doi.org/10.19168/jyasar.803807
AMA Muğaloğlu E, Kılıç E. G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models. Yaşar Üniversitesi E-Dergisi. Ocak 2021;16(61):228-247. doi:10.19168/jyasar.803807
Chicago Muğaloğlu, Erhan, ve Edanur Kılıç. “G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models”. Yaşar Üniversitesi E-Dergisi 16, sy. 61 (Ocak 2021): 228-47. https://doi.org/10.19168/jyasar.803807.
EndNote Muğaloğlu E, Kılıç E (01 Ocak 2021) G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models. Yaşar Üniversitesi E-Dergisi 16 61 228–247.
IEEE E. Muğaloğlu ve E. Kılıç, “G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models”, Yaşar Üniversitesi E-Dergisi, c. 16, sy. 61, ss. 228–247, 2021, doi: 10.19168/jyasar.803807.
ISNAD Muğaloğlu, Erhan - Kılıç, Edanur. “G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models”. Yaşar Üniversitesi E-Dergisi 16/61 (Ocak 2021), 228-247. https://doi.org/10.19168/jyasar.803807.
JAMA Muğaloğlu E, Kılıç E. G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models. Yaşar Üniversitesi E-Dergisi. 2021;16:228–247.
MLA Muğaloğlu, Erhan ve Edanur Kılıç. “G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models”. Yaşar Üniversitesi E-Dergisi, c. 16, sy. 61, 2021, ss. 228-47, doi:10.19168/jyasar.803807.
Vancouver Muğaloğlu E, Kılıç E. G7 Countries Unemployment Rate Predictions Using Seasonal Arima-Garch Coupled Models. Yaşar Üniversitesi E-Dergisi. 2021;16(61):228-47.