Eşbütünleşme için Tahmin ve Test: Spektral Bir Regresyon Yaklaşımı

Year 2013, Volume: 10 Issue: 1, 95 - 111, 15.07.2013

Yılmaz Akdi

Abstract

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.

Keywords

Zaman Serileri, Eşbütünleşme, Periyodogram Ordinat, Spektral Regresyon

Estimation and Testing for Cointegration: A Spectral Regression Approach

Abstract

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.

Keywords

Time Series, Cointegration, Periodogram Ordinate, Spectral Regression

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