nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒05‒22
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Parameter Estimation and Inference with Spatial Lags and Cointegration By Mutl, Jan; Sögner, Leopold
  2. Orthogonal Transformation of Coordinates in Copula M-GARCH Models – Bayesian analysis for WIG20 spot and futures returns By Mateusz Pipień
  3. On the estimation of dynamic conditional correlation models. By Hafner, Christian
  4. Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model By Stelios D. Bekiros; Alessia Paccagnini
  5. Forecast Evaluations for Multiple Time Series: A Generalized Theil Decomposition By Wolfgang Polasek

  1. By: Mutl, Jan (EBS Business School, Wiesbaden, Germany); Sögner, Leopold (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)
    Abstract: We study dynamic panel data models where the long run outcome for a particular crosssection is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies several cointegrating relationships that are nonlinear in the coefficients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator and investigate its small sample distribution in a simulation study. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, firm specific and industry data. A "closeness" measure for firms is based on inputoutput matrices. Our estimates show that this particular form of spatial correlation of credit default spreads is substantial and highly significant.
    Keywords: Dynamic ordinary least squares, cointegration, credit risk, spatial autocorrelation
    JEL: C31 C32
    Date: 2013–05
  2. By: Mateusz Pipień (National Bank of Poland, Economic Institute)
    Abstract: We check the empirical importance of some generalisations of the conditional distribution in M-GARCH case. A copula M-GARCH model with coordinate free conditional distribution is considered, as a continuation of research concerning specification of the conditional distribution in multivariate volatility models, see Pipień (2007) and (2010). The main advantage of the proposed family of probability distributions is that the coordinate axes, along which heavy tails and symmetry can be modelled, are subject to statistical inference. Along a set of specified coordinates both, linear and nonlinear dependence can be expressed in a decomposed form. In the empirical part of the paper we considered a problem of modelling the dynamics of the returns on the spot and future quotations of the WIG20 index from the Warsaw Stock Exchange. On the basis of the posterior odds ratio we checked the data support of considered generalisation, comparing it with BEKK model with the conditional distribution simply constructed as a product of the univariate skewed components. Our example clearly showed the empirical importance of the proposed class of the coordinate free conditional distributions.
    Keywords: Bayes factors, multivariate GARCH models, coordinate free distributions, Householder matrices
    JEL: C11 C32 C52
    Date: 2013
  3. By: Hafner, Christian
    Abstract: The maximum likelihood estimator applied to the dynamic conditional correlation model is severely biased in high dimensions. This is, in particular, the case where the cross-section dimension is close to the sample size. It is argued that one of the reasons for the bias lies in an ill-conditioned sample covariance matrix, which is used in the so-called variance targeting technique to match sample and theoretical unconditional covariances. A reduction of the bias is proposed by using shrinkage to target methods for the sample covariance matrix. Alternatively, the identity matrix, a single factor model, and equicorrelation are used as targets. Since the shrinkage intensity decreases towards zero with increasing sample size, the estimator is asymptotically equivalent to the maximum likelihood estimator. The finite sample performance of the proposed estimator over alternative estimators is demonstrated through a Monte Carlo study. Finally, an illustrative application to financial time series compares alternative estimation methods by means of commonly used statistical and economic criteria.
    Date: 2012
  4. By: Stelios D. Bekiros (Department of Economics, European University Institute (EUI) and Rimini Centre for Economic Analysis (RCEA), Italy); Alessia Paccagnini (Department of Economics, Università degli Studi di Milano-Bicocca, Italy)
    Abstract: In this paper we employ advanced Bayesian methods in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Very recently, hybrid models have become popular for dealing with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. This study includes a comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and tested against standard, Bayesian and Factor Augmented VARs. In this study we focus on a Factor Augmented DSGE model that is estimated using Bayesian approaches. The investigated period spans 1960:Q4 to 2010:Q4 for the real GDP, the harmonized CPI and the nominal short-term interest rate. We produce their forecasts for the out-of-sample testing period 1997:Q1-2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.
    Keywords: Bayesian estimation, Forecasting, Metropolis-Hastings, Markov chain monte carlo, Marginal data density, Factor Augmented DSGE
    JEL: C11 C15 C32
    Date: 2013–04
  5. By: Wolfgang Polasek (Institute of Advanced Studies, Austria)
    Abstract: The mean square error (MSE) compares point forecasts or a location parameter of the forecasting distribution with actual observations by the quadratic loss criterion. This paper shows how the Theil decomposition of the MSE error into a bias, variance and noise component which was proposed for univariate time series can be used to evaluate and compare multiple time series forecasts. Thus, for multivariate time series the ordinary and the alternative Theil decomposition is applied to decompose the MSE matrix. As an alternative we propose the average predictive ordinate criterion (APOC) which evaluates the ordinates of the predictive distribution for comparing forecasts of volatile time series. The multivariate Theil decomposition for the MSE and APOC criterion is used to compare and evaluate 3-dimensional VAR-GARCH-M time series forecasts for stock indices and exchange rates.
    Keywords: Forecast comparisons, average predictive ordinate criterion APOC, MSE matrix and multivariate predictions, multivariate and alternative Theil decomposition
    Date: 2013–05

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