nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒03‒29
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models By Luis Uzeda
  2. Real-time forecasting with a MIDAS VAR By Mikosch, Heiner; Neuwirth, Stefan
  3. Root-N consistent estimations of time dummies for the dynamic fixed effects logit models: Monte Carlo illustrations By Yoshitsugu Kitazawa
  4. VAR Models with Non-Gaussian Shocks By Ching-Wai (Jeremy) Chiu; Haroon Mumtaz; Gabor Pinter
  6. Estimation of Nonlinear Panel Models with Multiple Unobserved Effects By Chen , Mingli

  1. By: Luis Uzeda
    Abstract: Implications to signal extraction that arise from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well-investigated. In contrast, an analogous statement for forecasting evaluation cannot be made. This paper attempts to fill this gap in light of the recent resurgence of studies adopting UC models for forecasting purposes. In particular, four correlation structures are entertained: orthogonal, correlated, perfectly correlated innovations as well as a novel approach which combines features from two contrasting cases, namely, orthogonal and perfectly correlated innovations. Parameter space restrictions associated with different correlation structures and their connection with forecasting are discussed within a Bayesian framework. Introducing perfectly correlated innovations, however, reduces the covariance matrix rank. To accommodate that, a Markov Chain Monte Carlo sampler which builds upon properties of Toeplitz matrices and recent advances in precision-based algorithms is developed. Our results for several measures of U.S. inflation indicate that the correlation structure between state variables has important implications for forecasting performance as well as estimates of trend inflation.
    Keywords: Bayesian, Markov Chain Monte Carlo, State Space, Unobserved ComponentsModels, ARIMA, Reduced Rank, Precision, Forecasting
    JEL: C11 C15 C51 C53
    Date: 2016–03
  2. By: Mikosch, Heiner; Neuwirth, Stefan
    Abstract: This paper presents a MIDAS type mixed frequency VAR forecasting model. First, we propose a general and compact mixed frequency VAR framework using a stacked vector approach. Second, we integrate the mixed frequency VAR with a MIDAS type Almon lag polynomial scheme which is designed to reduce the parameter space while keeping models fexible. We show how to recast the resulting non-linear MIDAS type mixed frequency VAR into a linear equation system that can be easily estimated. A pseudo out-of-sample forecasting exercise with US real-time data yields that the mixed frequency VAR substantially improves predictive accuracy upon a standard VAR for dierent VAR specications. Forecast errors for, e.g., GDP growth decrease by 30 to 60 percent for forecast horizons up to six months and by around 20 percent for a forecast horizon of one year.
    Keywords: Forecasting, mixed frequency data, MIDAS, VAR, real time
    JEL: C53 E27
    Date: 2015–04–13
  3. By: Yoshitsugu Kitazawa (Faculty of Economics, Kyushu Sangyo University)
    Abstract: This paper illustrates the feasibility of the root-N consistent estimations of time dummies for both dynamic fixed effects logit models with strictly exogenous continuous explanatory variables and with no explanatory variable by using some Monte Carlo experiments. The illustrations not only imply the direct rebuttal to the generalization of Hahn fs (2001) suggestion, but also pave the way for fathoming the time effects in dynamic binary choice panel data models in a breeze.
    Keywords: dynamic fixed effects logit models; time dummies; root-N consistent GMM estimators; Monte Carlo
    JEL: C23 C25
    Date: 2016–03
  4. By: Ching-Wai (Jeremy) Chiu (Bank of England); Haroon Mumtaz (Queen Mary University of London); Gabor Pinter (Bank of England)
    Abstract: We introduce a Bayesian VAR model with non-Gaussian disturbances that are modelled with a finite mixture of normal distributions. Importantly, we allow for regime switching among the different components of the mixture of normals. Our model is highly flexible and can capture distributions that are fat-tailed, skewed and even multimodal. We show that our model can generate large out-of-sample forecast gains relative to standard forecasting models, especially during tranquil periods. Our model forecasts are also competitive with those generated by the conventional VAR model with stochastic volatility.
    Keywords: Bayesian VAR, Non-Gaussian shocks, Density Forecasting
    JEL: C11 C32 C52
    Date: 2902
  5. By: Murat Midilic (-)
    Abstract: This study applies the Iteratively Weighted Least Squares (IWLS) algorithm to a Smooth Transition Autoregressive (STAR) model with conditional variance. Monte Carlo simulations are performed to measure the performance of the algorithm, to compare its performance with the performances of established methods in the literature, and to see the effect of initial value selection method. Simulation results show that low bias and mean squared error are received for the slope parameter estimator from the IWLS algorithm when the real value of the slope parameter is low. In an empirical illustration, STAR-GARCH model is used to forecast daily US Dollar/Australian Dollar and FTSE Small Cap index returns. 1-day ahead out-of-sample forecast results show that forecast performance of the STAR-GARCH model improves with the IWLS algorithm and the model performs better that the benchmark model.
    Keywords: STAR, GARCH, iteratively weighted least squares, Australian Dollar,FTSE
    JEL: C15 C51 C53 C58 C87 F31
    Date: 2016–01
  6. By: Chen , Mingli (Department of Economics, University of Warwick)
    Abstract: I propose a fixed effects expectation-maximization (EM) estimator that can be applied to a class of nonlinear panel data models with unobserved heterogeneity, which is modeled as individual effects and/or time effects. Of particular interest is the case of interactive effects, i.e. when the unobserved heterogeneity is modeled as a factor analytical structure. The estimator is obtained through a computationally simple, iterative two-step procedure, where the two steps have closed form solutions. I show that estimator is consistent in large panels and derive the asymptotic distribution for the case of the probit with interactive effects. I develop analytical bias corrections to deal with the incidental parameter problem. Monte Carlo experiments demonstrate that the proposed estimator has good finite-sample properties.
    Keywords: Nonlinear panel, latent variables, interactive effects, factor error structure, EM algorithm, incidental parameters, bias correction
    JEL: C13 C21 C22
    Date: 2016

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