KaydedilenlerDerginizi ekleyin

Dergiler / Gazi University Journal of Science / 2018 / Cilt: 31 - Sayı: 4

An AdaptedApproach for Self-ExcitingThreshold Autoregressive Disturbances in Multiple Linear Regression

Barış AŞIKGİL
Sayfa
1268–1282
Yayın Tarihi
2022-10-19
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to numerical results, the proposed method can outperform under the typeof self-exciting threshold autoregressive autocorrelation problem when compared to ordinaryleast squares and two-stage least squares.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Ordinary least squares method is usually used for parameter estimation in multiple linearregression models when all regression assumptions are satisfied.
📊
Temel Bulgular & Veri:For this purpose, a nonlinear time series processknown as self-exciting threshold autoregressive model is used to generate disturbances inmultiple linear regression models.
💡
Sonuç & Katkı:According to numerical results, the proposed method can outperform under the typeof self-exciting threshold autoregressive autocorrelation problem when compared to ordinaryleast squares and two-stage least squares.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Ordinary least squares method is usually used for parameter estimation in multiple linearregression models when all regression assumptions are satisfied. One of the problems inmultiple linear regression analysis is the presence of serially correlated disturbances. Serialcorrelation can be formed by autoregressive or moving average models. There are manystudies in the literature including parameter estimation in regression models especially withautoregressive disturbances. The motivation of this study is that whether serially correlateddisturbances are defined by a different type of nonlinear process and how this process isanalyzed in multiple linear regression. For this purpose, a nonlinear time series processknown as self-exciting threshold autoregressive model is used to generate disturbances inmultiple linear regression models. Two-stage least squares method used in the presence ofautoregressive disturbances is adapted for dealing with this new situation and comprehensiveexperiments are performed in order to compare efficiencies of the proposed method with theothers. According to numerical results, the proposed method can outperform under the typeof self-exciting threshold autoregressive autocorrelation problem when compared to ordinaryleast squares and two-stage least squares.

🔖

Bu Makaleye Atıf Yap

AŞIKGİL, B. (2022). An AdaptedApproach for Self-ExcitingThreshold Autoregressive Disturbances in Multiple Linear Regression. Gazi University Journal of Science, Cilt: 31 - Sayı: 4, 1268–1282. https://ajindex.com/articles/521401

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (16)

  1. Cochrane, D., Orcutt, G.H., “Application of least squares regression to relationships containing autocorrelated error terms”, Journal of the American Statistical Association, 44(245): 32-61, (1949).
  2. Beach, C.M., MacKinnon, J.G., “A maximum likelihood procedure for regression with autocorrelated errors”, Econometrica, 46(1): 51-58, (1978).
  3. Gallant, A.R., Goebel, J.J., “Nonlinear regression with autocorrelated errors”, Journal of the American Statistical Association, 71(356): 961-967, (1976).
  4. Glasbey, C.A., “Correlated residuals in nonlinear regression applied to growth data”, Applied Statistics, 28(3): 251-259, (1979).
  5. Glasbey, C.A., “Nonlinear regression with autoregressive time series errors”, Biometrics, 36(1): 135-140, (1980).
  6. Glasbey, C.A., “Examples of regression with serially correlated errors”, The Statistician, 37(3): 277-291, (1988).
  7. Huang, M.N.L., Huang, M.K., “A parameter-elimination method for nonlinear regression with linear parameters and autocorrelated errors”, Biometrical Journal, 33(8): 937-950, (1991).
  8. Asikgil, B., Erar, A., “Polynomial tapered two-stage least squares method in nonlinear regression”, Applied Mathematics and Computation, 219(18): 9743-9754, (2013).
  9. Asikgil, B., “A novel approach for estimating seemingly unrelated regressions with high-order autoregressive disturbances”, Communications in Statistics: Simulation and Computation, 43(9): 2061-2080, (2014).
  10. Tong, H., “On a threshold model”, In: Chen, C. H. ed., Pattern Recognition and Signal Processing, Sijhoff and Noordhoff, Amsterdam, (1978).
  11. Tong, H., Threshold Models in Nonlinear Time Series Analysis, Lecture Notes in Statistics, Springer-Verlag, New York, (1983).
  12. Tsay, R.S., “Testing and modeling threshold autoregressive processes”, Journal of the American Statistical Association, 84(405): 231-240, (1989).
  13. Tsay, R.S., Analysis of Financial Time Series, John Wiley and Sons, Canada, (2002).
  14. Yadav, P.K., Pope, P.F., Paudyal, K., “Threshold autoregressive modeling in finance: the price differences of equivalent assests”, Mathematical Finance, 4(2): 205-221, (1994).
  15. Feucht, M., Möller, U., Witte, H., Schmidt, K., Arnold, M., Benninger, F., Steinberger, K., Friedrich, M.H., “Nonlinear dynamics of 3 Hz spike-and-wave discharges recorded during typical absence seizures in children”, Cerebral Cortex, 8(6): 524-533, (1998).
  16. Chan, W.S., Wong, A.C.S., Tong, H., “Some nonlinear threshold autoregressive time series models for actuarial use”, North American Actuarial Journal, 8(4): 37-61