
on Econometric Time Series 
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 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1606.03901&r=ets 
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 shortterm 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 
URL:  http://d.repec.org/n?u=RePEc:ecb:ecbwps:20161893&r=ets 
By:  YAMAMOTO, Yohei 
Abstract:  In this paper, we consider residualbased bootstrap methods à la GonÇalves and Perron (2014) to construct the confidence interval for structural impulse response functions in factoraugmented 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 crosssectional 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:  factoraugmented vector autoregression, structural identiOcation, coverage rate, impulse response function 
JEL:  C14 C22 
Date:  2016–05–28 
URL:  http://d.repec.org/n?u=RePEc:hit:hiasdp:hiase26&r=ets 
By:  Minskya, Ksovim 
Abstract:  This paper develops a method of analyzing average value of a complexvalued 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 HodrickPrescott filter is made. 
Keywords:  filtering, time series, hodrickprescott, ztransform, average value, linear trend 
JEL:  C19 C49 C59 C69 E32 
Date:  2016–06–01 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:71745&r=ets 
By:  Minskya, Ksovim 
Abstract:  This paper extends the idea in ``Analysis of average value of a Fourier series using ztransform'' by the author. The main difference is that a threepole filter is used instead of a twopole filter. This paper reaches qualitatively the same conclusion. 
Keywords:  threepole filter; ztransform; filtering; linear trend 
JEL:  C49 C59 C69 E32 
Date:  2016–06–05 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:71765&r=ets 
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 outofsample 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 
URL:  http://d.repec.org/n?u=RePEc:tin:wpaper:20160044&r=ets 