
on Econometric Time Series 
By:  Albert FerreiroCastilla; Kees van Schaik 
Abstract:  In this note we apply the recently established WienerHopf Monte Carlo (WHMC) simulation technique for Levy processes from Kuznetsov et al. [17] to path functionals, in particular first passage times, overshoots, undershoots and the last maximum before the passage time. Such functionals have many applications, for instance in finance (the pricing of exotic options in a Levy model) and insurance (ruin time, debt at ruin and related quantities for a Levy insurance risk process). The technique works for any Levy process whose running infimum and supremum evaluated at an independent exponential time allows sampling from. This includes classic examples such as stable processes, subclasses of spectrally one sided Levy processes and large new families such as meromorphic Levy processes. Finally we present some examples. A particular aspect that is illustrated is that the WHMC simulation technique performs much better at approximating first passage times than a `plain' Monte Carlo simulation technique based on sampling increments of the Levy process. 
Date:  2013–06 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1306.3923&r=ets 
By:  Wei Chen 
Abstract:  Gframework is presented by Peng [41] for measure risk under uncertainty. In this paper, we define fractional GBrownian motion (fGBm). Fractional GBrownian motion is a centered GGaussian process with zero mean and stationary increments in the sense of sublinearity with Hurst index $H\in (0,1)$. This process has stationary increments, selfsimilarity, and long rang dependence properties in the sense of sublinearity. These properties make the fractional GBrownian motion a suitable driven process in mathematical finance. We construct wavelet decomposition of the fGBm by wavelet with compactly support. We develop fractional Gwhite noise theory, define GIt\^oWick stochastic integral, establish the fractional GIt\^o formula and the fractional GClarkOcone formula, and derive the GGirsanov's Theorem. For application the Gwhite noise theory, we consider the financial market modelled by GWickIt\^o type of SDE driven by fGBm. The financial asset price modelled by fGBm has volatility uncertainty, using GGirsanov's Theorem and GClarkOcone Theorem, we derive that sublinear expectation of the discounted European contingent claim is the bidask price of the claim. 
Date:  2013–06 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1306.4070&r=ets 
By:  Raoul Golan; Austin Gerig 
Abstract:  Financial time series exhibit a number of interesting properties that are difficult to explain with simple models. These properties include fattails in the distribution of price fluctuations (or returns) that are slowly removed at longer timescales, strong autocorrelations in absolute returns but zero autocorrelation in returns themselves, and multifractal scaling. Although the underlying cause of these features is unknown, there is growing evidence they originate in the behavior of volatility, i.e., in the behavior of the magnitude of price fluctuations. In this paper, we posit a feedback mechanism for volatility that reproduces many of the nontrivial properties of empirical prices. The model is parsimonious, requires only two parameters to fit a specific financial time series, and can be grounded in a straightforward framework where volatility fluctuations are driven by the estimation error of an exogenous Poisson rate. 
Date:  2013–06 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1306.4975&r=ets 
By:  Enrique MoralBenito (Banco de España); Luis Serven (The World Bank) 
Abstract:  For reasons of empirical tractability, analysis of cointegrated economic time series is often developed in a partial setting, in which a subset of variables is explictly modeled conditional on the rest. This approach yields valid inference only if the conditioning variables are weakly exogenous for the parameters of interest. This paper proposes a new test of weak exogeneity in panel cointegration models. The test has a limiting Gumbel distribution that is obtained by first letting T → ∞ and then letting N → ∞. We evaluate the accuracy of the asymptotic approximation in finite samples via simulation experiments. Finally, as an empirical illustration, we test weak exogeneity of disposable income and wealth in aggregate consumption 
Keywords:  panel data, cointegration, weak exogeneity, Monte Carlo methods 
JEL:  C23 C32 
Date:  2013–05 
URL:  http://d.repec.org/n?u=RePEc:bde:wpaper:1307&r=ets 
By:  Ryan GreenawayMcGrevy (Bureau of Economic Analysis) 
Date:  2013–04 
URL:  http://d.repec.org/n?u=RePEc:bea:wpaper:0100&r=ets 
By:  Tomás del Barrio Castro (Department of Applied Economics, University of the Balearic Islands); Paulo M.M. Rodrigues (Banco de Portugal, NOVA School of Business and Economics, Universidade Nova de Lisboa, CEFAGE); A.M. Robert Taylor (Granger Centre for Time Series Econometrics, University of Nottingham) 
Abstract:  Is In this paper we provide a detailed analysis of the impact of persistent cycles on the wellknown semiparametric unit root tests of Phillips and Perron (1988, Biometrika 75, 335.346). It is shown analytically and through Monte Carlo simulations that the presence of complex (near) unit roots can severely bias the size properties of these unit root test procedures. 
Keywords:  PhillipsPerron unit root test; Nonstationarity; Serial correlation; Cyclicality; Busi ness cycles. 
JEL:  C12 C22 
Date:  2013 
URL:  http://d.repec.org/n?u=RePEc:cfe:wpcefa:2013_11&r=ets 
By:  Kociecki, Andrzej 
Abstract:  The aim of the paper is to study the nature of normalization in Structural VAR models. Noting that normalization is the integral part of identification of a model, we provide a general characterization of the normalization. In consequence some the easy–to–check conditions for a Structural VAR to be normalized are worked out. Extensive comparison between our approach and that of Waggoner and Zha (2003a) is made. Lastly we illustrate our approach with the help of five variables monetary Structural VAR model. 
Keywords:  Normalization, Identification, Impulse Response Function 
JEL:  C30 C32 C51 
Date:  2013–06–17 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:47645&r=ets 
By:  James Morley (School of Economics, The University of New South Wales); Irina B. Panovska (Washington University in St. Louis); Tara M. Sinclair (the George Washington University) 
Abstract:  Unobserved components (UC) models are widely used to estimate stochastic trends in macroeconomic time series, with the existence of a stochastic trend typically motivated by a stationarity test. However, given the small sample sizes available for most macroeconomic variables, standard Lagrange multiplier tests of stationarity will perform poorly when the data are highly persistent. To address this problem, we propose the alternative use of a likelihood ratio test of stationarity based on a UC model and demonstrate that a bootstrap version of this test has far better smallsample properties for empiricallyrelevant data generating processes than bootstrap versions of the standard tests. An application to U.S. real GDP produces stronger support for the presence of large permanent shocks when using the likelihood ratio test as compared to standard tests. 
Keywords:  media and democracy; corruption; defamation; chilling effect. 
JEL:  C12 C15 C22 E23 
Date:  2013–05 
URL:  http://d.repec.org/n?u=RePEc:swe:wpaper:201241a&r=ets 
By:  Dalu Zhang (University of East Anglia); Peter Moffatt (University of East Anglia) 
Abstract:  This paper examines the existence of time series nonlinearity in the real output growth / recessionterm spread relationship. Vector Autoregression (VAR), Threshold VAR (TVAR), Structural break VAR (SBVAR), Structural break threshold VAR (SBTVAR) are applied in the analysis. The insample results indicate there are nonlinear components in this relationship. And this nonlinearity tend to be caused by structural breaks. The best insample model also shows its robustness on arrival of new information in the outofsample tests. We find evidence the model with only structural break nonlinearity outperform linear models in 1quarter, 3quarter and 4quarter ahead forecasting. 
Date:  2013–06 
URL:  http://d.repec.org/n?u=RePEc:uea:aepppr:2012_47&r=ets 