nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒08‒23
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Portfolio return distributions: Sample statistics with non-stationary correlations By Desislava Chetalova; Thilo A. Schmitt; Rudi Sch\"afer; Thomas Guhr
  2. Panel vector autoregressive models: a survey By Canova, Fabio; Ciccarelli, Matteo
  3. Predictive likelihood comparisons with DSGE and DSGE-VAR models By Warne, Anders; Coenen, Günter; Christoffel, Kai
  4. Forecasting fiscal time series using mixed frequency data By Asimakopoulos, Stylianos; Paredes, Joan; Warmedinger, Thomas
  5. Regime-switching global vector autoregressive models By Binder, Michael; Gross, Marco
  6. Measuring contagion potential among sovereigns and banks using a mixed-cross-section GVAR By Gross, Marco; Kok Sørensen, Christoffer

  1. By: Desislava Chetalova; Thilo A. Schmitt; Rudi Sch\"afer; Thomas Guhr
    Abstract: We consider random vectors drawn from a multivariate normal distribution and compute the sample statistics in the presence of non-stationary correlations. For this purpose, we construct an ensemble of random correlation matrices and average the normal distribution over this ensemble. The resulting distribution contains a modified Bessel function of the second kind whose behavior differs significantly from the multivariate normal distribution, in the central part as well as in the tails. This result is then applied to asset returns. We compare with empirical return distributions using daily data from the Nasdaq Composite Index in the period from 1992 to 2012. The comparison reveals good agreement, the average portfolio return distribution describes the data well especially in the central part of the distribution. This in turn confirms our ansatz to model the non-stationarity by an ensemble average.
    Date: 2013–08
  2. By: Canova, Fabio; Ciccarelli, Matteo
    Abstract: This paper provides an overview of the panel VAR models used in macroeconomics and finance. It discusses what are their distinctive features, what they are used for, and how they can be derived from economic theory. It also describes how they are estimated and how shock identification is performed, and compares panel VARs to other approaches used in the literature to deal with dynamic models involving heterogeneous units. Finally, it shows how structural time variation can be dealt with and illustrates the challanges that they present to researchers interested in studying cross-unit dynamics interdependences in heterogeneous setups. JEL Classification: C11, C30, C53
    Keywords: estimation, Identification, inference, Panel VAR
    Date: 2013–01
  3. By: Warne, Anders; Coenen, Günter; Christoffel, Kai
    Abstract: This paper shows how to compute the h-step-ahead predictive likelihood for any subset of the observed variables in parametric discrete time series models estimated with Bayesian methods. The subset of variables may vary across forecast horizons and the problem thereby covers marginal and joint predictive likelihoods for a fixed subset as special cases. The basic idea is to utilize well-known techniques for handling missing data when computing the likelihood function, such as a missing observations consistent Kalman filter for linear Gaussian models, but it also extends to nonlinear, nonnormal state-space models. The predictive likelihood can thereafter be calculated via Monte Carlo integration using draws from the posterior distribution. As an empirical illustration, we use euro area data and compare the forecasting performance of the New Area-Wide Model, a small-open-economy DSGE model, to DSGEVARs, and to reduced-form linear Gaussian models. JEL Classification: C11, C32, C52, C53, E37
    Keywords: Bayesian inference, forecasting, Kalman filter, Missing data, Monte Carlo integration
    Date: 2013–04
  4. By: Asimakopoulos, Stylianos; Paredes, Joan; Warmedinger, Thomas
    Abstract: Given the increased importance of …fiscal monitoring, this study amends the existing literature in the …field of intra-annual fi…scal data in two main dimensions. First, we use quarterly fi…scal data to forecast a very disaggregated set of …fiscal series at annual frequency. This makes the analysis useful in the typical forecasting environment of large institutions, which employ a "bottom-up" or disaggregated framework. Aside from this practical type of consideration, we fi…nd that forecasts for total revenues and expenditures via their subcomponents can actually result more accurate than a direct forecast of the aggregate. Second, we employ a Mixed Data Sampling (MiDaS) approach to analyze mixed frequency …fiscal data, which is a methodological novelty. It is shown that MiDaS is the best approach for the analysis of mixed frequency fi…scal data compared to two alternative approaches. The results regarding the information content of quarterly …fiscal data con…rm previous work that such data should be taken into account as it becomes available throughout the year for improving the end-year forecast. For instance, once data for the third quarter is incorporated, the annual forecast becomes very accurate (very close to actual data). We also benchmark against the European Commission’s forecast and fi…nd the results fare favorably, particularly when considering that they stem from a simple univariate framework. JEL Classification: C22, C53, E62, H68
    Keywords: aggregated vs disaggregated forecast, Fiscal Policy, Mixed frequency data, short-term forecasting
    Date: 2013–05
  5. By: Binder, Michael; Gross, Marco
    Abstract: The purpose of the paper is to develop a Regime-Switching Global Vector Autoregressive (RS-GVAR) model. The RS-GVAR model allows for recurring or non-recurring structural changes in all or a subset of countries. It can be used to generate regime-dependent impulse response functions which are conditional upon a regime-constellation across countries. Coupling the RS and the GVAR methodology improves out-of-sample forecast accuracy significantly in an application to real GDP, price inflation, and stock prices. JEL Classification: C32, E17, G20
    Keywords: forecasting and simulation, Global macroeconometric modeling, nonlinear modeling, Regime switching
    Date: 2013–08
  6. By: Gross, Marco; Kok Sørensen, Christoffer
    Abstract: This paper aims to illustrate how a Mixed-Cross-Section Global Vector Autoregressive (MCS-GVAR) model can be set up and solved for the purpose of forecasting and scenario simulation. The application involves two cross-sections: sovereigns and banks for which we model their credit default swap spreads. Our MCS-GVAR comprises 23 sovereigns and 41 international banks from Europe, the US and Japan. The model is used to conduct systematic shock simulations and thereby compute a measure of spill-over potential for within and across the group of sovereigns and banks. The results point to a number of salient facts: i) Spill-over potential in the CDS market was particularly pronounced in 2008 and more recently in 2011-12; ii) while in 2008 contagion primarily went from banks to sovereigns, the direction reversed in 2011-12 in the course of the sovereign debt crisis; iii) the index of spill-over potential suggests that the system of banks and sovereigns has become more densely connected over time. Should large shocks of size similar to those experienced in the early phase of the crisis hit the system in 2011/2012, considerably more pronounced and more synchronized adverse responses across banks and sovereigns would have to be expected. JEL Classification: C33, C53, C61, E17
    Keywords: Contagion, forecasting and simulation, Global macroeconometric modeling, macro-financial linkages, models with panel data, network analysis, spill-overs
    Date: 2013–08

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