nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒06‒18
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Kolmogorov Space in Time Series Data By K. Kanjamapornkul; R. Pin\v{c}\'ak
  2. On the design of data sets for forecasting with dynamic factor models By Rünstler, Gerhard
  3. Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions By YAMAMOTO, Yohei
  4. Analysis of average value of a Fourier series using z-transform: comparison with Hodrick-Prescott filter By Minskya, Ksovim
  5. A three-pole filter understanding of the average value of a Fourier series By Minskya, Ksovim
  6. Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models By Shelton Peiris; Manabu Asai; Michael McAleer

  1. By: K. Kanjamapornkul; R. Pin\v{c}\'ak
    Abstract: We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for time series data. Our concept is realized as the algorithm of empirical mode decomposition and intrinsic time scale decomposition and it is subsequently used for preliminary analysis on the real time series data.
    Date: 2016–06
  2. By: Rünstler, Gerhard
    Abstract: Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and Least Angle Regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results. JEL Classification: E37, C53, C51
    Keywords: dynamic factor models, forecasting, LARS, variable selection
    Date: 2016–04
  3. By: YAMAMOTO, Yohei
    Abstract: In this paper, we consider residual-based bootstrap methods à la GonÇalves and Perron (2014) to construct the confidence interval for structural impulse response functions in factor-augmented vector autoregressions. In particular, we compare the bootstrap with factor estimation (Procedure A) with the bootstrap without factor estimation (Procedure B). In theory, both procedures are asymptotically valid under a condition √T/N → 0, where N and T are the cross-sectional dimension and the time dimension, respectively. Even when √T/N → 0 is irrelevant, Procedure A still accounts for the effect of the factor estimation errors on the impulse response function estimate and it achieves good coverage rates in most cases. On the contrary, Procedure B is invalid in such cases and tends to undercover if N is much smaller than T. However, Procedure B is implemented more straightforwardly from the standard structural VARs and the length of the confidence interval is shorter than that of Procedure A in finite samples. Given that Procedure B still gives a satisfactory coverage rate unless N is very small, it remains in consideration of empirical use, although using Procedure A is safer as it correctly accounts for the effect of the factor estimation errors.
    Keywords: factor-augmented vector autoregression, structural identiOcation, coverage rate, impulse response function
    JEL: C14 C22
    Date: 2016–05–28
  4. By: Minskya, Ksovim
    Abstract: This paper develops a method of analyzing average value of a complex-valued function that can be represented as a Fourier series satisfying a few realistic restrictions. This method may be useful when Discrete Fourier transform is highly inefficient, and comparison with Hodrick-Prescott filter is made.
    Keywords: filtering, time series, hodrick-prescott, z-transform, average value, linear trend
    JEL: C19 C49 C59 C69 E32
    Date: 2016–06–01
  5. By: Minskya, Ksovim
    Abstract: This paper extends the idea in ``Analysis of average value of a Fourier series using z-transform'' by the author. The main difference is that a three-pole filter is used instead of a two-pole filter. This paper reaches qualitatively the same conclusion.
    Keywords: three-pole filter; z-transform; filtering; linear trend
    JEL: C49 C59 C69 E32
    Date: 2016–06–05
  6. By: Shelton Peiris (University of Sydney, Australia); Manabu Asai (Soka University, Japan); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain)
    Abstract: In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.
    Keywords: Stochastic volatility; GARCH models; Gegenbauer Polynomial; Long Memory; Spectral Likelihood; Estimation; Forecasting
    JEL: C18 C21 C58
    Date: 2016–06–06

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