nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒07‒25
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. On Robust Trend Function Hypothesis Testing By Harvey, David I; Leybourne, Stephen J; Taylor, A.M. Robert
  2. The Real Part of a Complex ARMA Process By Bailey, Ralph
  3. Policy-Induced Mean Reversion in the Real Interest Rate? By Zisimos Koustas; Jean-Francois Lamarche
  4. Using the Correlation Dimension to Detect non-linear dynamics By Theodore Panagiotidis; David Chappell
  5. An extension of time series tests to panel data By EVA RAQUEL PORRAS

  1. By: Harvey, David I; Leybourne, Stephen J; Taylor, A.M. Robert
    Abstract: In this paper we build upon the robust procedures proposed in Vogelsang (1998) for testing hypotheses concerning the deterministric trend function of a univariate time series. Vogelsang proposes statistics formed from taking the product of a (normalised) Wald statistic for the trend function hypothesis under test with a specific function of a separate variable addition Wald statistic. The function of the second statistic is explicitly chosen such that the resultant product statistic has pivotal limiting null distributions, coincident at a chosen level, under I(0) or I(1) errors. The variable addition statistic in question has also been suggested as a unit root statistic, and we propose corresponding tests based on other well-known unit root statistics. We find that, in the case of the linear trend model, a test formed using the familiar augmented Dickey-Fuller [ADF] statistic provides a useful complement to Vogelsang's original tests, demonstrating generally superior power when the errors display strong serial correlation with this pattern tending to reverse as the degree of serial correlation in the errors lessens. Importantly for practical considerations, the ADF-based tests also display significantly less finite sample over-size in the presence of weakly dependent errors than the original tests.
    Keywords: Wald tests; trend function hypotheses; unit root statistics
    JEL: C22
    Date: 2005–02
  2. By: Bailey, Ralph
    Keywords: Complex ARMA processes; cycles; reciprocal polynomials; palindromic polynomials
    JEL: C32
    Date: 2005–04
  3. By: Zisimos Koustas (Department of Economics, Brock University); Jean-Francois Lamarche (Department of Economics, Brock University)
    Abstract: This paper utilizes tests for a unit root that have power against nonlinear alternatives to provide empirical evidence on the time series properties of the ex-post real interest rate in the G7 countries. We find that the unit-root hypothesis can be rejected in the presence of a nonlinear alternative motivated by theoretical literature on optimal monetary policy rules. This represents a reversal of the results obtained using standard linear unit-root and cointegration tests. Tests for linearity reject this hypothesis for Canada, France, Germany, Italy, and the US. For these countries we estimate nonlinear models to capture the dynamics of the ex-post real interest rate.
    Keywords: Fisher Effect; Unit Roots; Self-Exciting Threshold Autoregression
    JEL: E40 E50 C32
    Date: 2005–07
  4. By: Theodore Panagiotidis (Dept of Economics, Loughborough University); David Chappell (University of Sheffield)
    Abstract: The standardised residuals from GARCH models fitted to three stock indices of the Athens Stock Exchange are examined for evidence of chaotic behaviour. In each case the correlation dimension is calculated for a range of embedding dimensions. The results do not support the hypothesis of chaotic behaviour; it appears that each set of residuals is iid.
    Keywords: Non-linear Dynamics, Stock Indices, Chaos, Correlation Dimension.
    JEL: C22 C53 G10
    Date: 2004–11
  5. By: EVA RAQUEL PORRAS (Instituto de Empresa)
    Abstract: In the financial field much of the data available is in panel form. The objective of this paper is to analyze the long run equilibrium relationship between prices and fundamentals while proposing a very simple method of extending time series models to panel data. This method has several characteristics that make it appealing. First, it is simple to implement. Second, it is general in scope. Third, it takes into account arbitrary correlations. Fourth, it does not require making unrealistic assumptions. Our results are supportive of Han´s (1996) in that we do not find cointegration between fundamentals and prices.
    Keywords: Panel data, Time series, Fundamentals, Prices
    Date: 2004–06

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