nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒07‒18
seven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Parametric Estimation of Long Memory in Factor Models By Yunus Emre Ergemen
  2. Choosing between persistent and stationary volatility By Chronopoulos, Ilias; Giraitis, Liudas; Kapetanios, George
  3. Nonlinear Forecasts and Impulse Responses for Causal-Noncausal (S)VAR Models By Christian Gourieroux; Joann Jasiak
  4. Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! By Luis Gruber; Gregor Kastner
  5. Tensor Factor Model Estimation by Iterative Projection By Yuefeng Han; Rong Chen; Dan Yang; Cun-Hui Zhang
  6. Forgetting Approaches to Improve Forecasting By Paulo M.M. Rodrigues; Robert Hill
  7. A Structural Dynamic Factor Model for Daily Global Stock Market Returns By Linton, O. B.; Tang, H.; Wu, J.;

  1. By: Yunus Emre Ergemen (Aarhus University, Department of Economics and Business Economics, and CREATES)
    Abstract: A dynamic factor model is proposed in that factor dynamics are driven by stochastic time trends describing arbitrary persistence levels. The proposed model is essentially a long memory factor model, which nests standard I(0) and I(1) behavior smoothly in common factors. In the estimation, principal components analysis (PCA) and conditional sum of squares (CSS) estimations are employed. For the dynamic model parameters, centered normal asymptotics are established at the usual parametric rates, and their small-sample properties are explored via Monte-Carlo experiments. The method is then applied to a panel of U.S. industry realized volatilities. JEL classifcation: C12, C13, C33 Key words: Factor models, long memory, conditional sum of squares, principal components analysis, realized volatility
    Date: 2022–06–24
  2. By: Chronopoulos, Ilias; Giraitis, Liudas; Kapetanios, George
    Abstract: This paper suggests a multiplicative volatility model where volatility is decomposed into a stationary and a non-stationary persistent part. We provide a testing procedure to determine which type of volatility is prevalent in the data. The persistent part of volatility is associated with a nonstationary persistent process satisfying some smoothness and moment conditions. The stationary part is related to stationary conditional heteroskedasticity. We outline theory and conditions that allow the extraction of the persistent part from the data and enable standard conditional heteroskedasticity tests to detect stationary volatility after persistent volatility is taken into account. Monte Carlo results support the testing strategy in small samples. The empirical application of the theory supports the persistent volatility paradigm, suggesting that stationary conditional heteroskedasticity is considerably less pronounced than previously thought.
    Date: 2022–06–21
  3. By: Christian Gourieroux; Joann Jasiak
    Abstract: We introduce the closed-form formulas of nonlinear forecasts and nonlinear impulse response functions (IRF) for the mixed causal-noncausal (Structural) Vector Autoregressive (S)VAR models. We also discuss the identification of nonlinear causal innovations of the model to which the shocks are applied. Our approach is illustrated by a simulation study and an application to a bivariate process of Bitcoin/USD and Ethereum/USD exchange rates.
    Date: 2022–05
  4. By: Luis Gruber; Gregor Kastner
    Abstract: Vectorautogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods, more concretely shrinking priors, have shown to be successful in improving prediction performance. In the present paper we introduce the recently developed $R^2$-induced Dirichlet-decomposition prior to the VAR framework and compare it to refinements of well-known priors in the VAR literature. We demonstrate the virtues of the proposed prior in an extensive simulation study and in an empirical application forecasting data of the US economy. Further, we shed more light on the ongoing Illusion of Sparsity debate. We find that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames; dynamic model averaging, however, can combine the merits of both worlds. All priors are implemented using the reduced-form VAR and all models feature stochastic volatility in the variance-covariance matrix.
    Date: 2022–06
  5. By: Yuefeng Han; Rong Chen; Dan Yang; Cun-Hui Zhang
    Abstract: Tensor time series, which is a time series consisting of tensorial observations, has become ubiquitous. It typically exhibits high dimensionality. One approach for dimension reduction is to use a factor model structure, in a form similar to Tucker tensor decomposition, except that the time dimension is treated as a dynamic process with a time dependent structure. In this paper we introduce two approaches to estimate such a tensor factor model by using iterative orthogonal projections of the original tensor time series. The approaches extend the existing estimation procedures and our theoretical investigation shows that they improve the estimation accuracy and convergence rate significantly. The developed approaches are similar to higher order orthogonal projection methods for tensor decomposition, but with significant differences and theoretical properties. Simulation study is conducted to further illustrate the statistical properties of these estimators.
    Date: 2020–06
  6. By: Paulo M.M. Rodrigues; Robert Hill
    Abstract: There is widespread evidence of parameter instability in the literature. One way to account for this feature is through the use of time-varying parameter (TVP) models that discount older data in favour of more recent data. This practise is often known as forgetting and can be applied in several different ways. This paper introduces and examines the performance of different (flexible) forgetting methodologies in the context of the Kalman filter. We review and develop the theoretical background and investigate the performance of each methodology in simulations as well as in two empirical forecast exercises using dynamic model averaging (DMA). Specifically, out-of-sample DMA forecasts of CPI inflation and S&P500 returns obtained using different forgetting approaches are compared. Results show that basing the amount of forgetting on the forecast error does not perform as well as avoiding instability by placing bounds on the parameter covariance matrix.
    JEL: C22 C51 C53
    Date: 2022
  7. By: Linton, O. B.; Tang, H.; Wu, J.;
    Abstract: Most stock markets are open for 6-8 hours per trading day. The Asian, European and American stock markets are separated in time by time-zone differences. We propose a statistical dynamic factor model for a large number of daily returns across multiple time zones. Our model has a common global factor as well as continental factors. Under a mild fixed-signs assumption, our model is identified and has a structural interpretation. We propose several estimators of the model: the maximum likelihood estimator-one day (MLE-one day), the quasi-maximum likelihood estimator (QMLE), an improved estimator from QMLE (QMLE-md), the QMLEres (similar to MLE-one day), and a Bayesian estimator (Gibbs sampling). We establish consistency, the rates of convergence and the asymptotic distributions of the QMLE and the QMLE-md. We next provide a heuristic procedure for conducting inference for the MLE-one day and the QMLE-res. Monte Carlo simulations reveal that the MLE-one day, the QMLE-res and the QMLE-md work well. We then apply our model to two real data sets: (1) equity portfolio returns from Japan, Europe and the US; (2) MSCI equity indices of 41 developed and emerging markets. Some new insights about linkages among different markets are drawn.
    Keywords: Daily Global Stock Market Returns, Expectation Maximization Algorithm, Minimum Distance, Quasi Maximum Likelihood, Structural Dynamic Factor Model, Time-Zone Differences
    JEL: C55 C58 G15
    Date: 2022–06–15

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