Testing for Trend Stationarity Using Spectral Density Estimators

• 영문명: Testing for Trend Stationarity Using Spectral Density Estimators
• 발행기관: 한국계량경제학회
• 저자명: Jin Lee
• 간행물 정보: 『한국계량경제학회 학술대회 논문집』2008년 하계학술대회, 1~33쪽, 전체 33쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2008.08.30

영문 초록

We propose a new test statistic for trend stationarity against di¤erence stationarity using spectral density estimators. The spectral density of the …rst di¤erenced process equals to zero at the zero frequency under the null of trend stationarity, whereas di¤erence stationarity yields positive spectrum near zero frequency. With this one-sided nature of the spectrum, we construct valid testing procedures based on kernel-based spectral density estimators. Note that the spectral density estimator becomes degenerate under the null, where one do not simply apply standard results in the literature of heteroskedasticity and autocorrelation consistent (HAC) estimation. We provide new results on asymptotic distribution of the spectral density estimator under degeneracy. It is found that the convergence rates ensuring nondegenerating asymptotic variance of the estimator are much faster than the rate required for conventional HAC estimators. Consistency of the proposed test is also discussed. Simulation studies show that our spectrum-based test is competitive in terms of power in comparison with well-known KPSS test. Applications to some US macroeconomic series are presented.

목차

1. Introduction

2. Spectral representation of the hypothesis

3. TEST STATISTICS

4. Local asymptotic power

5. Simulation studies

6. Empirical applications

7. Conclusion

키워드

Trend stationarity Spectral density Kernel estimator Degeneracy