nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒01‒08
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Alternatives to large VAR, VARMA and multivariate stochastic volatility models By Mike G. Tsionas
  2. Alternative Bayesian compression in Vector Autoregressions and related models By Mike G. Tsionas
  3. Generalized quasi-maximum likelihood inference for periodic conditionally heteroskedastic models By Aknouche, Abdelhakim; Al-Eid, Eid; Demouche, Nacer
  4. Efficient asymptotic variance reduction when estimating volatility in high frequency data By Simon Clinet; Yoann Potiron
  5. Fractional Dynamics of Natural Growth and Memory Effect in Economics By Valentina V. Tarasova; Vasily E. Tarasov
  6. Controlling the Size of Autocorrelation Robust Tests By Pötscher, Benedikt M.; Preinerstorfer, David

  1. 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 mixture-of-normals 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 time-varying parameter vector autoregressions (TVP-VAR) 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
  2. 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 out-of-sample forecasting by treating the problem as an error-in-variables 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 computa-tional 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 out-of-sample forecasting. The new techniques are used in U.S data featuring medium-size, large and huge BVARs
    Keywords: Bayesian Vector Autoregressions; Bayesian Compressed Re-gression; Error-in-Variables; Forecasting; Multivariate Autoregressive Index model.
    JEL: C11 C13
    Date: 2016–11
  3. By: Aknouche, Abdelhakim; Al-Eid, Eid; Demouche, Nacer
    Abstract: This paper establishes consistency and asymptotic normality of the generalized quasi-maximum 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 time-varying 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 one-step framework and to PCH models with complex periodic patterns such as high frequency seasonality and non-integer seasonality.
    Keywords: Periodic conditionally heteroskedastic models, periodic asymmetric power GARCH, generalized QML estimation, consistency and asymptotic normality, prediction of powers, high frequency periodicity, non-integer periodicity.
    JEL: C13 C18 C51 C58
    Date: 2016–02–03
  4. 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 [Barndorff-Nielsen et al., 2008] and the quasi-maximum 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 Tukey-Hanning 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
  5. By: Valentina V. Tarasova; Vasily E. Tarasov
    Abstract: A generalization of the economic model of natural growth, which takes into account the power-law 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 power-law memory we used the theory of derivatives of non-integer order and fractional-order differential equation. We propose equations take into account the effects of memory with one-parameter power-law 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
  6. 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

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