Araştırma Makalesi
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Eşbütünleşme için Tahmin ve Test: Spektral Bir Regresyon Yaklaşımı

Yıl 2013, Cilt: 10 Sayı: 1, 95 - 111, 15.07.2013

Öz

Ekonometri ve zaman serileri alanındaki popüler bir konu, vektör otoregressif zaman serilerinin bileşenleri arasındaki eşbütünleşme ilişkisidir. Engle ve Granger (1987)’in çalışmalarından sonra problem önemli hale gelmiş ve Johansen (1988), Stock ve Watson gibi başka pek çok yazar tarafından da bu probleme işaret edilmiştir. Engle ve Granger’in en küçük kareler metodu ile Johansen’in koşullu maksimum olabilirlik metodu en çok dikkati çekenlerdendir. Bu testler ekonomik zaman serilerine rutin olarak uygulanmıştır çünkü eşbütünleşme nosyonu doğal bir yoruma sahiptir. Bizim metodumuz, eşbütünleşmiş zaman serileri arasındaki eşbütünleşme ilişkisini tahmin etmek için çapraz periyodogramın düşük frekansı bileşenlerini kullanır. Bazı durumlarda bu metod, Engle ve Granger tarafından önerilen sıradan en küçük kareler metodunun sonuçlarını geliştirir.

Kaynakça

  • Akdi, Y., Dickey, D. A., 1998. Periodograms of Unit Root Time Series: Distributions and Tests, Communications in Statistics: Theory and Methods, 27, 69-87.
  • Beaulieu, J., Miron, J. A., 1993. Seasonal Unit Roots in Aggregate US Data, Journal of Econometrics, 55, 305-328.
  • Bloomfield, P., 1976. Fourier Analysis of Time Series: An Introduction, Wiley, New York.
  • Boswijk, H. P., Lucas, A., 2002. Semi-nonparametric Cointegration Testing, Journal of Econometrics, 108, 253-280.
  • Breitung, J., 2002. Nonparametric Tests for Unit Roots and Cointegration, Journal of Econometrics, 108, 343-368.
  • Chambers, M. J., 2001. Temporal Aggregation and the Finite Sample Performance of Spectral Regression Estimators in the Cointegrated System: A Simulation Study, Econometric Theory, Vol. 17, Number 3, 591-607.
  • Chen, W. W., Hurvich, C. M., 2003. Estimating Fractional Cointegration in the Presence of Polynomial Trends, Journal of Econometrics, 117, 95-121.
  • Choi, I., Phillips, P. B. C., 1993. Testing for a Unit Root by Frequency Domain Regression, Journal of Economics, 59, 263-286.
  • Deo, R. S., Hurvich, C. M., 2001. On the log Periodogram Regression Estimators of the memory Parameters in the Long Memory Volatility Models, Econometric Theory, Vol. 17, Number 4, 686-710.
  • Dickey, D. A., Fuller, W. A., 1979. Distributions of Estimators for Autoregressive Times Series with a Unit Root, Journal of American Statistical Association, 74, 427-431.
  • Dickey, D. A., Pantula, S. G., 1987. Determining the order of Differencing in Autoregressive Processes, Journal of Business and Economics Statistics, 5, 455-461.
  • Dickey D. A., Hasza, D. P., Fuller, W. A., 1984. Testing For Unit Roots in Seasonal Time Series, Journal of American Statistical Association, 79, 355-367.
  • Engle, R.F., C.W.J. Granger., 1987. Cointegration and Error Correction: Representation, Estimation, and Testing, Econometrica, 55, 251-276.
  • Fuller, W.A., 1996. Introduction to Statistical Time Series, Wiley, New York.
  • Hylleberg, S., Engle, R. F., Granger, C. W. J., Yoo, B. S., 1990. Seasonal Integration and Cointegration, Journal of Econometrics, 99, 215-238.
  • Johansen, S., 1988. Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12, 231-254.
  • Levy, D., 2002. Cointegration in Frequency Domain, Journal of Time Series Analysis, Vol.23, No.3, 333-339.
  • Marunicci, D., 2000. Spectral regression For Cointegrated Time Series With Long-Memory Innovations, Journal of Time Series Analysis, Vol. 21, Issue 6, 685-705.
  • Stock, J. H., 1987. Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors, Econometrica, 55, 1035-1056.
  • Stock, J. H., Watson, M. W., 1993. A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems, Econometrica, 61, 783-820.

Estimation and Testing for Cointegration: A Spectral Regression Approach

Yıl 2013, Cilt: 10 Sayı: 1, 95 - 111, 15.07.2013

Öz

A popular topic in the econometrics and time series area is the cointegrating relationship among the components of a vector autoregressive time series. The problem became important after the work of Engle and Granger (1987) and has been addressed by many authors: Johansen (1988), Stock and Watson among many others. Engle and Granger’s least squares method and Johansen’s conditional maximum likelihood method have received the most attention. These tests are routinely applied to economic time series because the notion of cointegration has a natural interpretation. Our method uses low frequency components of the cross periodogram to estimate the cointegration relationship between cointegrated time series. The method improves the results of ordinary least squares method proposed by Engle and Granger in some cases.

Kaynakça

  • Akdi, Y., Dickey, D. A., 1998. Periodograms of Unit Root Time Series: Distributions and Tests, Communications in Statistics: Theory and Methods, 27, 69-87.
  • Beaulieu, J., Miron, J. A., 1993. Seasonal Unit Roots in Aggregate US Data, Journal of Econometrics, 55, 305-328.
  • Bloomfield, P., 1976. Fourier Analysis of Time Series: An Introduction, Wiley, New York.
  • Boswijk, H. P., Lucas, A., 2002. Semi-nonparametric Cointegration Testing, Journal of Econometrics, 108, 253-280.
  • Breitung, J., 2002. Nonparametric Tests for Unit Roots and Cointegration, Journal of Econometrics, 108, 343-368.
  • Chambers, M. J., 2001. Temporal Aggregation and the Finite Sample Performance of Spectral Regression Estimators in the Cointegrated System: A Simulation Study, Econometric Theory, Vol. 17, Number 3, 591-607.
  • Chen, W. W., Hurvich, C. M., 2003. Estimating Fractional Cointegration in the Presence of Polynomial Trends, Journal of Econometrics, 117, 95-121.
  • Choi, I., Phillips, P. B. C., 1993. Testing for a Unit Root by Frequency Domain Regression, Journal of Economics, 59, 263-286.
  • Deo, R. S., Hurvich, C. M., 2001. On the log Periodogram Regression Estimators of the memory Parameters in the Long Memory Volatility Models, Econometric Theory, Vol. 17, Number 4, 686-710.
  • Dickey, D. A., Fuller, W. A., 1979. Distributions of Estimators for Autoregressive Times Series with a Unit Root, Journal of American Statistical Association, 74, 427-431.
  • Dickey, D. A., Pantula, S. G., 1987. Determining the order of Differencing in Autoregressive Processes, Journal of Business and Economics Statistics, 5, 455-461.
  • Dickey D. A., Hasza, D. P., Fuller, W. A., 1984. Testing For Unit Roots in Seasonal Time Series, Journal of American Statistical Association, 79, 355-367.
  • Engle, R.F., C.W.J. Granger., 1987. Cointegration and Error Correction: Representation, Estimation, and Testing, Econometrica, 55, 251-276.
  • Fuller, W.A., 1996. Introduction to Statistical Time Series, Wiley, New York.
  • Hylleberg, S., Engle, R. F., Granger, C. W. J., Yoo, B. S., 1990. Seasonal Integration and Cointegration, Journal of Econometrics, 99, 215-238.
  • Johansen, S., 1988. Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12, 231-254.
  • Levy, D., 2002. Cointegration in Frequency Domain, Journal of Time Series Analysis, Vol.23, No.3, 333-339.
  • Marunicci, D., 2000. Spectral regression For Cointegrated Time Series With Long-Memory Innovations, Journal of Time Series Analysis, Vol. 21, Issue 6, 685-705.
  • Stock, J. H., 1987. Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors, Econometrica, 55, 1035-1056.
  • Stock, J. H., Watson, M. W., 1993. A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems, Econometrica, 61, 783-820.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Zaman Serileri Analizi
Bölüm Araştırma Makaleleri
Yazarlar

Yılmaz Akdi

Yayımlanma Tarihi 15 Temmuz 2013
Yayımlandığı Sayı Yıl 2013 Cilt: 10 Sayı: 1

Kaynak Göster

APA Akdi, Y. (2013). Estimation and Testing for Cointegration: A Spectral Regression Approach. İstatistik Araştırma Dergisi, 10(1), 95-111.