
on Econometric Time Series 
By:  Martin M. Andreasen (Bank of England and CREATES) 
Abstract:  This paper improves the accuracy and speed of particle filtering for nonlinear DSGE models with potentially nonnormal 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, Nonlinear DSGE models, Nonnormal shocks, Particle filtering 
JEL:  C13 C15 E10 E32 
Date:  2010–01–27 
URL:  http://d.repec.org/n?u=RePEc:aah:create:201005&r=ets 
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 cointegrating 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 cointegration 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 cointegrating 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 cointegrating rank will converge to zero (one minus the marginal significance level), as the sample size diverges, for general I(1) processes. No such likelihoodbased 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:  Cointegration, trace test, sequential rank determination, i.i.d.bootstrap, wild bootstrap 
JEL:  C30 C32 
Date:  2010–02–01 
URL:  http://d.repec.org/n?u=RePEc:aah:create:201007&r=ets 
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 
URL:  http://d.repec.org/n?u=RePEc:ihs:ihsesp:248&r=ets 
By:  Kurita, Takamitsu; Nielsen, Bent 
Abstract:  A family of cointegrated vector autoregressive models with adjusted shortrun dynamics is introduced. These models can describe evolving shortrun 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 dynamicsadjusted vector autoregressions consists of three models: a model subject to shortrun parameter changes, a model with partial shortrun dynamics and a model with shortrun explanatory variables. An empirical illustration using US gasoline prices is presented, together with some simulation experiments. 
JEL:  C51 C52 C31 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:ner:oxford:http://economics.ouls.ox.ac.uk/14475/&r=ets 
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 VARMAtype 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 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1002.0934&r=ets 
By:  Maria Boguta; Eric J\"arpe 
Abstract:  A new spacetime model for interacting agents on the financial market is presented. It is a combination of the CurieWeiss model and a spacetime 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 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1002.0609&r=ets 
By:  Bennett T. McCallum 
Abstract:  Socalled “spurious regression” relationships between randomwalk (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 reestimate with an autocorrelation correction. Simulations show, for several typical cases, that the testrejection statistics for the reestimated relationships are very close to the true values, so do not yield results of the spurious type. 
JEL:  C22 C29 
Date:  2010–01 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:15690&r=ets 