nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒10‒10
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Common Feature Analysis of Economic Time Series: An Overview and Recent Developments By Marco Centoni; Gianluca Cubadda
  2. Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility By Roberto Leon-Gonzalez;
  3. STR: A Seasonal-Trend Decomposition Procedure Based on Regression By G. Forchini; Bin Jiang; Bin Peng

  1. By: Marco Centoni (LUMSA University); Gianluca Cubadda (DEF & CEIS, University of Rome "Tor Vergata")
    Abstract: In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.
    Keywords: Common features; common cycles; reduced-rank regression; canonical correlation analysis; vector autoregressive models; dynamic factor models; business cycles.
    Date: 2015–10–05
  2. By: Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies);
    Abstract: This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma Stochastic Volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse Gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and fi…nancial datasets, it is shown that the inverse Gamma and Generalized inverse Gamma processes can greatly outperform the commonly used log normal volatility processes with student-t errors.
    Date: 2015–10
  3. By: G. Forchini; Bin Jiang; Bin Peng
    Abstract: The set-up considered by Pesaran (Econometrica, 2006) is extended to allow for endogenous explanatory variables. A class of instrumental variables estimators is studied and it is shown that estimators in this class are consistent and asymptotically normally distributed as both the cross-section and time-series dimensions tend to infinity.
    Keywords: time series decomposition, seasonal data, Tikhonov regularisation, ridge regression, LASSO, STL, TBATS, X-12-ARIMA, BSM
    JEL: C33 C36
    Date: 2015

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