nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒02‒08
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility By Massimiliano Marcellino; Mario Porqueddu; Fabrizio Venditti
  2. Does BIC Estimate and Forecast Better Than AIC? By Carlos A. Medel; Sergio C. Salgado
  4. Estimating the State Vector of Linearized DSGE Models without the Kalman Filter By Robert Kollmann
  5. Is the Use of Autocovariances in Level the Best in Estimating the Income Processes? A Simulation Study By Chau, Tak Wai

  1. By: Massimiliano Marcellino (European University Institute and Bocconi University); Mario Porqueddu (Bank of Italy); Fabrizio Venditti (Bank of Italy)
    Abstract: In this paper we develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components. We take a Bayesian perspective and derive a Gibbs sampler to obtain the posterior density of the model parameters. This new tool is then used to investigate business cycle dynamics and to forecast GDP growth at short-term horizons in the euro area. We discuss three sets of empirical results. First, we use the model to evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows us to make a probabilistic assessment of the contribution of releases to forecast revisions. Third, we design a pseudo out-of-sample forecasting exercise and examine point and density forecast accuracy. In line with findings in literature on Bayesian Vector Autoregressions (BVAR), we find that stochastic volatility contributes to an improvement in density forecast accuracy.
    Keywords: forecasting, business cycle, mixed-frequency data, nonlinear models, nowcasting
    JEL: E32 C22 E27
    Date: 2013–01
  2. By: Carlos A. Medel; Sergio C. Salgado
    Abstract: We test two questions: (i) Is the Bayesian information criterion (BIC) more parsimonious than the Akaike information criterion (AIC)?, and (ii) Can the BIC forecast better than the AIC? By using simulated data, we provide statistical inference of both hypotheses individually and then jointly with a multiple hypotheses testing procedure to control better for type-I error. Both testing procedures deliver the same result: The BIC shows an in- and out-of-sample superiority over AIC only in a long-sample context.
    Date: 2012–11
  3. By: Gabriele Fiorentini (Università di Firenze); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We derive simple algebraic expressions for score tests of serial correlation in the levels and squares of common and idiosyncratic factors in static factor models with (semi) parametrically specified elliptical distributions even though one must generally compute the likelihood by simulation. We also robustify our Gaussian tests against non-normality. The orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices reflect their unobservability. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests, and compare them to other existing procedures. Finally, we apply our methods to monthly US stock returns.
    Keywords: ARCH, Financial returns, Kalman filter, LM tests, Non-Gaussian state space models, Predictability.
    JEL: C32 C12 C13 C14 C38 C46 C58
    Date: 2012–10
  4. By: Robert Kollmann
    Keywords: DSGE Models; Kalman Filter; smoothing
    JEL: E37 C32 C68
    Date: 2013–01
  5. By: Chau, Tak Wai
    Abstract: In this simulation study, I compare the efficiency and finite sample bias of parameter estimators for popular income dynamic models using various forms of autocovariances. The dynamic models have a random walk or a heterogeneous growth permanent component, a persistent autoregressive component and a white noise transitory component. I compare the estimators using autocovariances in level, first differences (FD), and autocovariances between level and future first differences (LD), where the last one is new in the literature of income dynamics. To maintain the same information used as in using level covariances, I also augment the FD and LD covariances with level variances in the estimation. The results show that using level covariances can give rise to larger finite sample biases and larger standard errors than using covariances in FD and LD augmented by level variance. Without augmenting the level variances, LD provides more efficient estimators than FD in estimating the non-permanent components. I also show that LD provides a convenient test between random walk and heterogeneous growth models with good power.
    Keywords: covariance structure; income dynamics; random walk; heterogeneous growth profi le; finite sample bias; efficiency
    JEL: C51 J31 C33
    Date: 2013–01–30

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