nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒04‒14
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Automatic spectral density estimation for Random fileds on a lattice via bootstrap By Jose M. Vidal-Sanz
  2. Test of Unbiasedness of the Integrated Covariance Estimation in the Presence of Noise By Masato Ubukata; Kosuke Oya
  3. Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation By Jennifer L. Castle; David F. Hendry

  1. By: Jose M. Vidal-Sanz
    Abstract: This paper considers the nonparametric estimation of spectral densities for second order stationary random fields on a d-dimensional lattice. I discuss some drawbacks of standard methods, and propose modified estimator classes with improved bias convergence rate, emphasizing the use of kernel methods and the choice of an optimal smoothing number. I prove uniform consistency and study the uniform asymptotic distribution, when the optimal smoothing number is estimated from the sampled data.
    Date: 2007–03
  2. By: Masato Ubukata (Graduate School of Economics, Osaka University); Kosuke Oya (Graduate School of Economics, Osaka University)
    Abstract: The cumulative covariance estimator in Hayashi and Yoshida (2005) which suits for non-synchronous observations possibly has a bias in the presence of the observational noise. We propose the test statistic to detect whether the observational noise causes a measurable bias in the estimator of Hayashi and Yoshida (2005). The test statistic proposed in this paper is asymptotically distributed as standard normal under null hypothesis. The finite sample performance of the test statistic is investigated through Monte Carlo simulation.
    Keywords: test statistic; integrated covariance; non-synchronous observation; observational noise; market microstructure noise
    JEL: C12 D49
    Date: 2007–04
  3. By: Jennifer L. Castle; David F. Hendry
    Abstract: Structural models` inflation forecasts are often inferior to those of naive devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, so no gain results, helping interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.
    Keywords: Inflation Forecasting, Structural Breaks, Robust Forecasts, Time-disaggregation, Forecast-error Taxonomies
    JEL: C32 C53
    Date: 2007

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