
on Econometric Time Series 
By:  Mike G. Tsionas (Athens University of Economics and Business) 
Abstract:  In this paper, our proposal is to combine univariate ARMA models to produce a variant of the VARMA model that is much more easily implementable and does not involve certain complications. The original model is reduced to a series of univariate problems and a copula – like term (a mixtureofnormals densities) is introduced to handle dependence. Since the univariate problems are easy to handle by MCMC or other techniques, computations can be parallelized easily, and only univariate distribution functions are needed, which are quite often available in closed form. The results from parallel MCMC or other posterior simulators can then be taken together and use simple sampling  resampling to obtain a draw from the exact posterior which includes the copula  like term. We avoid optimization of the parameters entering the copula mixture form as its parameters are optimized only once before MCMC begins. We apply the new techniques in three types of challenging problems. Large timevarying parameter vector autoregressions (TVPVAR) with nearly 100 macroeconomic variables, multivariate ARMA models with 25 macroeconomic variables and multivariate stochastic volatility models with 100 stock returns. Finally, we perform impulse response analysis in the data of Giannone, Lenza, and Primiceri (2015) and compare, as they proposed with results from a dynamic stochastic general equilibrium model. 
Keywords:  Vector Autoregressive Moving Average models; Multivariate Stochastic Volatility models; Copula models; Bayesian analysis 
JEL:  C11 C13 
Date:  2016–12 
URL:  http://d.repec.org/n?u=RePEc:bog:wpaper:217&r=ets 
By:  Mike G. Tsionas (Athens University of Economics and Business) 
Abstract:  In this paper we reconsider large Bayesian Vector Autoregressions (BVAR) from the point of view of Bayesian Compressed Regression (BCR). First, we show that there are substantial gains in terms of outofsample forecasting by treating the problem as an errorinvariables formulation and estimating the compression matrix instead of using random draws. As computations can be e?ciently organized around a standard Gibbs sampler, timings and computational complexity are not a?ected severely. Second, we extend the Multivariate Autoregressive Index model to the BCR context and show that we have, again, gains in terms of outofsample forecasting. The new techniques are used in U.S data featuring mediumsize, large and huge BVARs 
Keywords:  Bayesian Vector Autoregressions; Bayesian Compressed Regression; ErrorinVariables; Forecasting; Multivariate Autoregressive Index model. 
JEL:  C11 C13 
Date:  2016–11 
URL:  http://d.repec.org/n?u=RePEc:bog:wpaper:216&r=ets 
By:  Aknouche, Abdelhakim; AlEid, Eid; Demouche, Nacer 
Abstract:  This paper establishes consistency and asymptotic normality of the generalized quasimaximum likelihood estimate (GQMLE) for a general class of periodic conditionally heteroskedastic time series models (PCH). In this class of models, the volatility is expressed as a measurable function of the infinite past of the observed process with periodically timevarying parameters, while the innovation of the model is an independent and periodically distributed sequence. In contrast with the aperiodic case, the proposed GQMLE is rather based on S instrumental density functions where S is the period of the model while the corresponding asymptotic variance is in a "sandwich" form. Application to the periodic GARCH and the periodic asymmetric power GARCH model is given. Moreover, we discuss how to apply the GQMLE to the prediction of power problem in a onestep framework and to PCH models with complex periodic patterns such as high frequency seasonality and noninteger seasonality. 
Keywords:  Periodic conditionally heteroskedastic models, periodic asymmetric power GARCH, generalized QML estimation, consistency and asymptotic normality, prediction of powers, high frequency periodicity, noninteger periodicity. 
JEL:  C13 C18 C51 C58 
Date:  2016–02–03 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:75770&r=ets 
By:  Simon Clinet; Yoann Potiron 
Abstract:  This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from [BarndorffNielsen et al., 2008] and the quasimaximum likelihood estimator (QMLE) studied in [Xiu, 2010]. To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK TukeyHanning 16 and 1 for the QMLE. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice. 
Date:  2017–01 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1701.01185&r=ets 
By:  Valentina V. Tarasova; Vasily E. Tarasov 
Abstract:  A generalization of the economic model of natural growth, which takes into account the powerlaw memory effect, is suggested. The memory effect means the dependence of the process not only on the current state of the process, but also on the history of changes of this process in the past. For the mathematical description of the economic process with powerlaw memory we used the theory of derivatives of noninteger order and fractionalorder differential equation. We propose equations take into account the effects of memory with oneparameter powerlaw damping. Solutions of these fractional differential equations are suggested. We proved that the growth and downturn of output depend on the memory effects. We demonstrate that the memory effect can lead to decrease of output instead of its growth, which is described by model without memory effect. Memory effect can lead to increase of output, rather than decrease, which is described by model without memory effect. 
Date:  2016–12 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1612.09060&r=ets 
By:  Pötscher, Benedikt M.; Preinerstorfer, David 
Abstract:  Autocorrelation robust tests are notorious for suffering from size distortions and power problems. We investigate under which conditions the size of autocorrelation robust tests can be controlled by an appropriate choice of critical value. 
Keywords:  Autocorrelation robust tests, size control 
JEL:  C22 
Date:  2016–11 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:75657&r=ets 