nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒01‒23
five papers chosen by
Yong Yin
SUNY at Buffalo

  2. Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions By Strikholm, Birgit; Teräsvirta, Timo
  3. Testing for causality in variance in the presence of breaks By D van Dijk; D R Osborn; M Sensier
  4. GMM Estimation for Long Memory Latent Variable Volatility and Duration Models By Willa Chen; Rohit Deo
  5. Meese-Rogoff Redux: Micro-Based Exchange Rate Forecasting By Martin D.D. Evans; Richard K. Lyons

  1. By: Somnath Chatterjee
    Abstract: The purpose of this paper is to see how the term structure of interest rates has evolved in the sterling and euro treasury bond markets over the period 1999-2003. German bonds have been used as a proxy for euro-denominated bonds. A state-space representation for the single-factor Cox, Ingersoll and Ross (1985) model is employed to analyse the intertemporal dynamics of the term structure. Quasi-maximum likelihood estimates of the model parameters are obtained by using the Kalman filter to calculate the likelihood function. Results of the empirical analysis show that while the unobserved instantaneous interest rate exhibits mean reverting behaviour in both the UK and Germany, the mean reversion of the interest rate process has been relatively slower in the UK. The volatility component, which shocks the process at each step in time is also higher in the UK as compared to Germany.
  2. By: Strikholm, Birgit (Dept. of Economic Statistics, Stockholm School of Economics); Teräsvirta, Timo (Dept. of Economic Statistics, Stockholm School of Economics)
    Abstract: <p> In this paper we propose a method for determining the number of regimes in threshold autoregressive models using smooth transition autoregression as a tool. As the smooth transition model is just an approximation to the threshold autoregressive one, no asymptotic properties are claimed for the proposed method. Tests available for testing the adequacy of a smooth transition autoregressive model are applied sequentially to determine the number of regimes. A simulation study is performed in order to find out the finite-sample properties of the procedure and to compare it with two other procedures available in the literature. We find that our method works reasonably well for both single and multiple threshold models.
    Keywords: Model specification; model selection criterion; nonlinear modelling; sequential testing; switching regression
    JEL: C22 C51
    Date: 2005–01–11
  3. By: D van Dijk; D R Osborn; M Sensier
    Abstract: We examine the size properties of tests for causality in variance in the presence of structural breaks in volatility. Extensive Monte Carlo simulations demonstrate that these tests suffer from severe size distortions when such breaks are not taken into account. Pre-testing the series for structural changes in volatility is shown to largely remedy the problem.
    Date: 2004
  4. By: Willa Chen (Texas A&M University); Rohit Deo (New york University)
    Abstract: We study the rate of convergence of moment conditions that have been commonly used in the literature for Generalised Method of Moments (GMM) estimation of short memory latent variable volatility models. We show that when the latent variable possesses long memory, these moment conditions have an n^{1/2-d} rate of convergence where 0<d<0.5 is the memory parameter. The resulting GMM estimators will thus not be ãn consistent. We then provide an alternative set of moment conditions that are ãn consistent and asymptotically normal under long memory in the latent variable, thus allowing for ãn consistent GMM estimation.
    Keywords: GMM, long memory, stochastic volatility and durations
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–01–14
  5. By: Martin D.D. Evans; Richard K. Lyons
    Abstract: This paper compares the true, ex-ante forecasting performance of a micro-based model against both a standard macro model and a random walk. In contrast to existing literature, which is focused on longer horizon forecasting, we examine forecasting over horizons from one day to one month (the one-month horizon being where micro and macro analysis begin to overlap). Over our 3-year forecasting sample, we find that the micro-based model consistently out-performs both the random walk and the macro model. Micro-based forecasts account for almost 16 per cent of the sample variance in monthly spot rate changes. These results provide a level of empirical validation as yet unattained by other models. Our result that the micro-based model out-performs the macro model does not imply that macro fundamentals will never explain exchange rates. Quite the contrary, our findings are in fact consistent with the view that the principal driver of exchange rates is standard macro fundamentals. In Evans and Lyons (2004b)we report firm evidence that the non-public information that we exploit here for forecasting exchange rates is also useful for forecasting macro fundamentals themselves.
    JEL: F3 F4 G1
    Date: 2005–01

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