nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒02‒20
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Non-linear DSGE Models and The Optimized Particle Filter By Martin M. Andreasen
  2. Bootstrap Sequential Determination of the Co-integration Rank in VAR Models By ; Anders Rahbek; A.M.Robert Taylor
  3. Cointegration Analysis with State Space Models By Wagner, Martin
  4. Short-Run Parameter Changes in a Cointegrated Vector Autoregressive Model.. By Kurita, Takamitsu; Nielsen, Bent
  5. A Random Matrix Approach to VARMA Processes By Zdzis{\l}aw Burda; Andrzej Jarosz; Maciej A. Nowak; Ma\l{}gorzata Snarska
  6. A new space-time model for volatility clustering in the financial market By Maria Boguta; Eric J\"arpe
  7. Is the Spurious Regression Problem Spurious? By Bennett T. McCallum

  1. By: Martin M. Andreasen (Bank of England and CREATES)
    Abstract: This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter.
    Keywords: Likelihood inference, Non-linear DSGE models, Non-normal shocks, Particle filtering
    JEL: C13 C15 E10 E32
    Date: 2010–01–27
  2. By: (Department of Statistical Sciences, University of Bologna); Anders Rahbek (Department of Economics, University of Copenhagen and CREATES); A.M.Robert Taylor (School of Economics and Granger Centre for Time Series Econometrics, University of Nottingham)
    Abstract: Determining the co-integrating rank of a system of variables has become a fundamental aspect of applied research in macroeconomics and finance. It is wellknown that standard asymptotic likelihood ratio tests for co-integration rank of Johansen (1996) can be unreliable in small samples with empirical rejection frequencies often very much in excess of the nominal level. As a consequence, bootstrap versions of these tests have been developed. To be useful, however, sequential procedures for determining the co-integrating rank based on these bootstrap tests need to be consistent, in the sense that the probability of selecting a rank smaller than (equal to) the true co-integrating rank will converge to zero (one minus the marginal significance level), as the sample size diverges, for general I(1) processes. No such likelihood-based procedure is currently known to be available. In this paper we fill this gap in the literature by proposing a bootstrap sequential algorithm which we demonstrate delivers consistent cointegration rank estimation for general I(1) processes. Finite sample Monte Carlo simulations show the proposed procedure performs well in practice.
    Keywords: Co-integration, trace test, sequential rank determination, i.i.d.bootstrap, wild bootstrap
    JEL: C30 C32
    Date: 2010–02–01
  3. By: Wagner, Martin (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and Frisch Centre for Economic Research, Oslo, Norway)
    Abstract: This paper presents and exemplifies results developed for cointegration analysis with state space models by Bauer and Wagner in a series of papers. Unit root processes, cointegration and polynomial cointegration are defined. Based upon these definitions the major part of the paper discusses how state space models, which are equivalent to VARMA models, can be fruitfully employed for cointegration analysis. By means of detailing the cases most relevant for empirical applications, the I(1), MFI(1) and I(2) cases, a canonical representation is developed and thereafter some available statistical results are briefly mentioned.
    Keywords: State space models, unit roots, cointegration, polynomial cointegration, pseudo maximum likelihood estimation, subspace algorithms
    JEL: C13 C32
    Date: 2010–02
  4. By: Kurita, Takamitsu; Nielsen, Bent
    Abstract: A family of cointegrated vector autoregressive models with adjusted short-run dynamics is introduced. These models can describe evolving short-run dynamics in a more flexible way than standard vector autoregressions, and yet likelihood analysis is based on reduced rank regression using conventional asymptotic tables. The family of dynamics-adjusted vector autoregressions consists of three models: a model subject to short-run parameter changes, a model with partial short-run dynamics and a model with short-run explanatory variables. An empirical illustration using US gasoline prices is presented, together with some simulation experiments.
    JEL: C51 C52 C31
    Date: 2009
  5. By: Zdzis{\l}aw Burda; Andrzej Jarosz; Maciej A. Nowak; Ma\l{}gorzata Snarska
    Abstract: We apply random matrix theory to derive spectral density of large sample covariance matrices generated by multivariate VMA(q), VAR(q) and VARMA(q1,q2) processes. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N/T is fixed. In this regime the underlying random matrices are asymptotically equivalent to Free Random Variables (FRV). We apply the FRV calculus to calculate the eigenvalue density of the sample covariance for several VARMA-type processes. We explicitly solve the VARMA(1,1) case and demonstrate a perfect agreement between the analytical result and the spectra obtained by Monte Carlo simulations. The proposed method is purely algebraic and can be easily generalized to q1>1 and q2>1.
    Date: 2010–02
  6. By: Maria Boguta; Eric J\"arpe
    Abstract: A new space-time model for interacting agents on the financial market is presented. It is a combination of the Curie-Weiss model and a space-time model introduced by J\"arpe 2005. Properties of the model are derived with focus on the critical temperature and magnetization. It turns out that the Hamiltonian is a sufficient statistic for the temperature parameter and thus statistical inference about this parameter can be performed. Thus e.g. statements about how far the current financial situation is from a financial crisis can be made, and financial trading stability be monitored for detection of malicious risk indicating signals.
    Date: 2010–02
  7. By: Bennett T. McCallum
    Abstract: So-called “spurious regression” relationships between random-walk (or strongly autoregressive) variables are generally accompanied by clear signs of severe autocorrelation in their residuals. A conscientious researcher would therefore not end an investigation with such a result, but would likely re-estimate with an autocorrelation correction. Simulations show, for several typical cases, that the test-rejection statistics for the re-estimated relationships are very close to the true values, so do not yield results of the spurious type.
    JEL: C22 C29
    Date: 2010–01

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