Abstract
This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets might be efficient) or alternatively I(d) with d
Highlights
This paper re-examines the statistical properties of a number of US financial series contained in the well-known dataset which can be downloaded fromG
We briefly describe the methodology used in this paper for testing fractional integration and cointegration in the case of Shiller’s financial time series data
In this paper we have examined bivariate relationships among various financial variables using fractional integration and cointegration methods
Summary
This paper re-examines the statistical properties of a number of US financial series (such as stock market prices, dividends, earnings, consumer prices and long-term interest rates) contained in the well-known dataset which can be downloaded from. As already mentioned, the discrete options I(1) and I(0) of classical cointegration analysis are rather restrictive: the equilibrium errors might be a fractionally integrated I(d)-type process, with stock and dividends being fractionally cointegrated This is stressed by Caporale and Gil-Alana (2004), who propose a simple two-step residuals-based strategy for fractional cointegration based on the approach of Robinson (1994): first the order of integration of the individual series is tested, and the degree of integration of the estimated residuals from the cointegrating regression.
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