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- Title
INTEGRATION VERSUS TREND STATIONARITY IN TIME SERIES.
- Authors
DeJong, David N.; Nankervis, John C.; Savin, N. E.; Whiteman, Charles H.
- Abstract
This article presents a study which utilized an integration approach to model the macroeconomic time series. A well-known approach to modeling macroeconomic time series is to assume that the natural logarithm of the series can be represented by the sum of a deterministic time trend and a stochastic term. The trend need not literally be part of the data generation process, but may be viewed as a substitute for a complicated and unknown function of population, capital accumulation, technical progress, etc. Within this approach there are two competing models; in the trend-stationary specification the stochastic term follows a stationary process, while in the integrated specification the stochastic term follows a random walk. The essential difference between the models is the nature of the process driving the stochastic component, not whether the series is trended. The conclusion of this study is that it is difficult to discriminate between the two models using classical testing methods. This is the consequence of low power: the powers of integration tests against plausible trend-stationary alternatives can be quite low, as can the powers of trend-stationarity tests against integrated alternatives.
- Subjects
MATHEMATICAL models of economics; MATHEMATICAL economics; FUNCTIONAL integration; FUNCTIONAL analysis; STOCHASTIC processes; GENERALIZED integrals
- Publication
Econometrica, 1992, Vol 60, Issue 2, p423
- ISSN
0012-9682
- Publication type
Article
- DOI
10.2307/2951602