nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒07‒05
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Underidentified SVAR models: A framework for combining short and long-run restrictions with sign-restrictions By Andrew Binning
  2. Mixed frequency structural models: estimation, and policy analysis By Claudia Foroni; Massimiliano Marcellino
  3. Bayesian Inference of Asymmetric Stochastic Conditional Duration Models By Zhongxian Men; Adam W. Kolkiewicz; Tony S. Wirjanto
  4. Stochastic Conditional Duration Models with Mixture Processes By Tony S. Wirjanto; Adam W. Kolkiewicz; Zhongxian Men
  5. A Threshold Stochastic Conditional Duration Model for Financial Transaction Data By Zhongxian Men; Tony S. Wirjanto; Adam W. Kolkiewicz

  1. By: Andrew Binning (Norges Bank (Central Bank of Norway))
    Abstract: I describe a new method for imposing zero restrictions (both short and long-run) in combination with conventional sign-restrictions. In particular I extend the Rubio-Ramrez et al.(2010) algorithm for applying short and long-run restrictions for exactly identified models to models that are underidentified. In turn this can be thought of as a unifying framework for short-run, long-run and sign restrictions. I demonstrate my algorithm with two examples. In the first example I estimate a VAR model using the Smets & Wouters (2007) dataset and impose sign and zero restrictions based on the impulse responses from their DSGE model. In the second example I estimate a BVAR model using the Mountford & Uhlig (2009) data set and impose the same sign and zero restrictions they use to identify an anticipated government revenue shock.
    Keywords: SVAR, Identification, Impulse responses, Short-run restrictions, Long-run restrictions, Sign restrictions
    Date: 2013–06–10
  2. By: Claudia Foroni (Norges Bank (Central Bank of Norway)); Massimiliano Marcellino (European University Institute, Bocconi University and CEPR)
    Abstract: In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identfication problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identfication issues, and yield more reliable policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.
    Keywords: Structural VAR, DSGE models, temporal aggregation, mixed frequency data, estimation. policy analysis
    JEL: C32 C43 E32
    Date: 2013–06–11
  3. By: Zhongxian Men (Department of Statistics & Actuarial Science, University of Waterloo, Canada); Adam W. Kolkiewicz (Department of Statistics & Actuarial Science, University of Waterloo, Canada); Tony S. Wirjanto (Department of Statistics & Actuarial Science, University of Waterloo, Canada)
    Abstract: This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process. Novel algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allows comparison of our models and methods to those existing in the literature.
    Keywords: Stochastic Duration; Bayesian Inference; Markov Chain Monte Carlo; Leverage Effect; Acceptance-rejection; Slice Sampler
    Date: 2013–05
  4. By: Tony S. Wirjanto (Department of Statistics and Actuarial Science, University of Waterloo, Canada); Adam W. Kolkiewicz (Department of Statistics and Actuarial Science, University of Waterloo, Canada); Zhongxian Men (Department of Statistics and Actuarial Science, University of Waterloo, Canada)
    Abstract: This paper studies a stochastic conditional duration (SCD) model with a mixture of distribution processes for financial asset’s transaction data. Specifically it imposes a mixture of two positive distributions on the innovations of the observed duration process, where the mixture component distributions could be either Exponential, Gamma or Weibull. The model also allows for correlation between the observed durations and the logarithm of the latent conditionally expected durations in order to capture a leverage effect known to exist in the equity market. In addition the proposed mixture SCD model is shown to be able to accommodate possibly heavy tails of the marginal distribution of durations. Novel Markov Chain Monte Carlo (MCMC) algorithms are developed for Bayesian inference of parameters and duration forecasting of these models. Simulation studies and empirical applications to two stock duration data sets are provided to assess the performance of the proposed mixture SCD models and the accompanying MCMC algorithms.
    Keywords: Stochastic conditional duration; Mixture of distributions; Bayesian inference; Markov Chain Monte Carlo; Leverage effect; Slice sampler
    Date: 2013–05
  5. By: Zhongxian Men (Department of Statistics & Actuarial Science, University of Waterloo, Canada); Tony S. Wirjanto (Department of Statistics & Actuarial Science, University of Waterloo, Canada; School of Accounting and Finance, University of Waterloo, Canada); Adam W. Kolkiewicz (Department of Statistics & Actuarial Science, University of Waterloo, Canada)
    Abstract: This paper proposes a threshold stochastic conditional duration (TSCD) model to capture the asymmetric property of financial transactions. The innovation of the observable duration equation is assumed to follow a threshold distribution with two component distributions switching between two regimes. The distributions in different regimes are assumed to be Exponential, Gamma or Weibull. To account for uncertainty in the unobserved threshold level, the observed durations are treated as self-exciting threshold variables. Adopting a Bayesian approach, we develop novel Markov Chain Monte Carlo algorithms to estimate all of the unknown parameters and latent states. To forecast the one-step ahead durations, we employ an auxiliary particle filter where the filter and prediction distributions of the latent states are approximated. The proposed model and the developed MCMC algorithms are illustrated by using both simulated and actual financial transaction data. For model selection, a Bayesian deviance information criterion is calculated to compare our model with other competing models in the literature. Overall, we find that the threshold SCD model performs better than the SCD model when a single positive distribution is assumed for the innovation of the duration equation.
    Keywords: Stochastic conditional duration; Threshold; Markov Chain Monte Carlo; Auxiliary particle filter; Deviance information criterion
    Date: 2013–05

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