nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒10‒07
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference By Nikolaus Hautsch; Yangguoyi Ou
  2. DSGE model-based forecasting of non-modelled variables By Frank Schorfheide; Keith Sill; Maxym Kryshko
  3. Structural vector autoregressions: theory of identification and algorithms for inference By Juan F. Rubio-Ramírez; Daniel F.Waggoner; Tao Zha
  4. GARCH-based identification and estimation of triangular systems By Todd Prono
  5. Direct and iterated multistep AR methods for difference stationary processes By Proietti, Tommaso
  6. Fitting vast dimensional time-varying covariance models By Robert Engle; Neil Shephard; Kevin Shepphard
  7. Real-time measurement of business conditions By S. Boragan Aruoba; Francis X. Diebold; Chiara Scotti

  1. By: Nikolaus Hautsch; Yangguoyi Ou
    Abstract: In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.
    Keywords: Stochastic Volatility, Markov Chain Monte Carlo, Metropolis-Hastings al- Jump Processes
    JEL: C15 C22 G12
    Date: 2008–09
  2. By: Frank Schorfheide; Keith Sill; Maxym Kryshko
    Abstract: This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). The authors use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model- generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, the authors apply their approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, and the unemployment rate along with predictions for the seven variables that have been used to estimate the DSGE model.
    Date: 2008
  3. By: Juan F. Rubio-Ramírez; Daniel F.Waggoner; Tao Zha
    Abstract: Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic general equilibrium models. Yet there have been no workable rank conditions to ascertain whether an SVAR is globally identified. When identifying restrictions such as long-run restrictions are imposed on impulse responses, there have been no efficient algorithms for small-sample estimation and inference. To fill these important gaps in the literature, this paper makes four contributions. First, we establish general rank conditions for global identification of both overidentified and exactly identified models. Second, we show that these conditions can be checked as a simple matrix-filling exercise and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we establish a very simple rank condition for exactly identified models that amounts to a straightforward counting exercise. Fourth, we develop a number of efficient algorithms for small-sample estimation and inference.
    Date: 2008
  4. By: Todd Prono
    Abstract: Diagonal GARCH is shown to support identification of the triangular system and is argued as a higher moment analog to traditional exclusion restrictions used for determining suitable instruments. The estimator for this result is ML in the case where a distribution for the GARCH process is known and GMM otherwise. For the GMM estimator, an alternative weighting matrix is proposed.
    Keywords: Time-series analysis
    Date: 2008
  5. By: Proietti, Tommaso
    Abstract: The paper focuses on the comparison of the direct and iterated AR predictors when Xt is a difference stationary process. In particular, it provides some useful results for comparing the efficiency of the two predictors and for extracting the trend from macroeconomic time series using the two methods. The main results are based on an encompassing representation for the two predictors which enables to derive their properties quite easily under a maintained model. The paper provides an analytic expression for the mean square forecast error of the two predictors and derives useful recursive formulae for computing the direct and iterated coefficients. From the empirical standpoint, we propose estimators of the AR coefficients based on the tapered Yule-Walker estimates; we also provide a test of equal forecast accuracy which is very simple to implement and whose critical values can be obtained with the bootstrap method. Since multistep prediction is tightly bound up with the estimation of the long run component in a time series, we turn to the role of the direct method for trend estimation and derive the corresponding multistep Beveridge-Nelson decomposition.
    Keywords: Beveridge-Nelson decomposition; Multistep estimation; Tapered Yule-Walker estimates; Forecast combination.
    JEL: C51 E32 C53 E31 C22
    Date: 2008–10–01
  6. By: Robert Engle; Neil Shephard; Kevin Shepphard
    Abstract: Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared.
    Keywords: ARCH models; composite likelihood; dynamic conditional correlations; incidental parameters; quasi-likelihood; time-varying covariances.
    JEL: C01 C14 C32
    Date: 2008
  7. By: S. Boragan Aruoba; Francis X. Diebold; Chiara Scotti
    Abstract: We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the framework in a prototype empirical example and a simulation study calibrated to the example.
    Keywords: Business conditions
    Date: 2008

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