nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒04‒27
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modeling dynamic diurnal patterns in high frequency financial data By Ito, Ryoko
  2. The Seasonal KPSS Test When Neglecting Seasonal Dummies: A Monte Carlo analysis By Ghassen El Montasser; Talel Boufateh; Fakhri Issaoui
  3. Forecasting with Mixed Frequency Samples: The Case of Common Trends By Peter Fuleky; Carl S. Bonham
  4. Comparison of Methods for Constructing Joint Confidence Bands for Impulse Response Functions By Helmut Lütkepohl; Anna Staszewska-Bystrova; Peter Winker
  5. Comparative study of static and dynamic neural network models for nonlinear time series forecasting By Abounoori, Abbas Ali; Mohammadali, Hanieh; Gandali Alikhani, Nadiya; Naderi, Esmaeil
  6. Further Results on Identification of Structural VAR Models By Kociecki, Andrzej

  1. By: Ito, Ryoko
    Abstract: A spline-DCS model is developed to forecast the conditional distribution of high-frequency financial data with periodic behavior. The dynamic cubic spline of Harvey and Koopman (1993) is applied to allow diurnal patterns to evolve stochastically over time. An empirical application illustrates the practicality and impressive predictive performance of the model.
    Keywords: outlier; robustness, score, calendar effect, spline, trade volume.
    JEL: C22
    Date: 2013–04–19
  2. By: Ghassen El Montasser; Talel Boufateh; Fakhri Issaoui
    Abstract: This paper shows through a Monte Carlo analysis the effect of neglecting seasonal deterministics on the seasonal KPSS test. We found that the test is most of the time heavily oversized and not convergent in this case. In addition, Bartlett-type non-parametric correction of error variances did not signally change the test's rejection frequencies.
    Keywords: Deterministic seasonality, Seasonal KPSS Test, Monte Carlo Simulations.
    JEL: C32
    Date: 2013–04–07
  3. By: Peter Fuleky (UHERO and Department of Economics, University of Hawaii at Manoa); Carl S. Bonham (Department of Economics, University of Hawaii at Manoa)
    Abstract: We analyze the forecasting performance of small mixed frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high fre- quency information is passed to low frequency series through the common factors. Dierencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the trans- fer of information among indicators. We nd that allowing for common trends improves forecasting performance over a stationary factor model based on dierenced data. The \common-trends factor model" outperforms the stationary factor model at all analyzed forecast horizons. Our results demonstrate that when mixed frequency variables are cointegrated, modeling common stochastic trends improves forecasts.
    Keywords: Dynamic Factor Model, Mixed Frequency Samples, Common Trends, Forecasting
    JEL: E37 C32 C53 L83
    Date: 2013–04
  4. By: Helmut Lütkepohl (DIW, FU Berlin); Anna Staszewska-Bystrova (University of Lodz); Peter Winker (University of Lodz)
    Abstract: In vector autoregressive analysis confidence intervals for individual impulse responses are typically reported to indicate the sampling uncertainty in the estimation results. A range of methods are reviewed and a new proposal is made for constructing joint confidence bands, given a pre-specified coverage level, for the impulse responses at all horizons considered simultaneously. The methods are compared in a simulation experiment and recommendations for empirical work are provided.
    Keywords: Vector autoregressive process, impulse responses, bootstrap, confidence band
    JEL: C32
    Date: 2013
  5. By: Abounoori, Abbas Ali; Mohammadali, Hanieh; Gandali Alikhani, Nadiya; Naderi, Esmaeil
    Abstract: During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis different types of these models have been used in forecasting. Now, there is this question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series (as a nonlinear financial time series). The data were collected daily from 25/3/2009 to 22/10/2011. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems or ANFIS and Multi-layer Feed-forward Neural Network or MFNN) and a dynamic model (nonlinear neural network autoregressive model or NNAR). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.
    Keywords: Forecasting, Stock Market, dynamic Neural Network, Static Neural Network.
    JEL: C22 C45 C60 G14 G17
    Date: 2012–10–12
  6. By: Kociecki, Andrzej
    Abstract: We provide some generalization and clarification of the identification conditions for Structural VAR (SVAR) models given in Rubio–Ramírez et al (2010). In particular we show that their basic sufficient condition is also necessary. In addition we give necessary and sufficient conditions for identification almost everywhere in SVAR under homogenous restrictions irrespective of whether the model is exactly identified or over–identified. The modification of the order condition is also suggested.
    Keywords: SVAR, identification
    JEL: C10 C32 E52
    Date: 2013–04–25

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