nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒07‒26
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Panel Unit Root Tests with Structural Breaks By Pengyu Chen; Yiannis Karavias; Elias Tzavalis
  2. Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions By Florian Huber; Gary Koop
  3. Time series models with infinite-order partial copula dependence By Martin Bladt; Alexander J. McNeil
  4. Mixed semimartingales: Volatility estimation in the presence of fractional noise By Carsten Chong; Thomas Delerue; Guoying Li
  5. A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters By Giacomo Bormetti; Fulvio Corsi

  1. By: Pengyu Chen (University of Birmingham); Yiannis Karavias (University of Birmingham); Elias Tzavalis (Athens University of Economics and Business)
    Abstract: This article introduces the xtbunitroot command in Stata, which implements the panel data unit root tests developed by Karavias and Tzavalis (2014). These tests allow for one or two structural breaks in deterministic components of the series and can be seen as panel data counterparts of the tests by Zivot and Andrews (1992) and Lumsdaine and Papell (1997). The dates of the breaks can be known or unknown. The tests allow for intercepts and linear trends, nonnormal errors and cross-section heteroskedasticity and dependence. They have power against homogeneous and heterogeneous alternatives, and can be applied to panels with small or large time series dimensions.
    Keywords: Panel data, Unit root, Structural break, Banking, COVID–19, xtbunitroot
    Date: 2021–07
  2. By: Florian Huber; Gary Koop
    Abstract: Macroeconomists using large datasets often face the choice of working with either a large Vector Autoregression (VAR) or a factor model. In this paper, we develop methods for combining the two using a subspace shrinkage prior. Subspace priors shrink towards a class of functions rather than directly forcing the parameters of a model towards some pre-specified location. We develop a conjugate VAR prior which shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage as well as the number of factors. After establishing the theoretical properties of our proposed prior, we carry out simulations and apply it to US macroeconomic data. Using simulations we show that our framework successfully detects the number of factors. In a forecasting exercise involving a large macroeconomic data set we find that combining VARs with factor models using our prior can lead to forecast improvements.
    Date: 2021–07
  3. By: Martin Bladt; Alexander J. McNeil
    Abstract: Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious method for parameterizing s-vine processes using the Kendall partial autocorrelation function is developed. The potential of the resulting models to give improved statistical fits in many applications is indicated with an example using macroeconomic data.
    Date: 2021–07
  4. By: Carsten Chong; Thomas Delerue; Guoying Li
    Abstract: We consider the problem of estimating volatility for high-frequency data when the observed process is the sum of a continuous It\^o semimartingale and a noise process that locally behaves like fractional Brownian motion with Hurst parameter H. The resulting class of processes, which we call mixed semimartingales, generalizes the mixed fractional Brownian motion introduced by Cheridito [Bernoulli 7 (2001) 913-934] to time-dependent and stochastic volatility. Based on central limit theorems for variation functionals, we derive consistent estimators and asymptotic confidence intervals for H and the integrated volatilities of both the semimartingale and the noise part, in all cases where these quantities are identifiable. When applied to recent stock price data, we find strong empirical evidence for the presence of fractional noise, with Hurst parameters H that vary considerably over time and between assets.
    Date: 2021–06
  5. By: Giacomo Bormetti; Fulvio Corsi
    Abstract: We propose an observation-driven time-varying SVAR model where, in agreement with the Lucas Critique, structural shocks drive both the evolution of the macro variables and the dynamics of the VAR parameters. Contrary to existing approaches where parameters follow a stochastic process with random and exogenous shocks, our observation-driven specification allows the evolution of the parameters to be driven by realized past structural shocks, thus opening the possibility to gauge the impact of observed shocks and hypothetical policy interventions on the future evolution of the economic system.
    Date: 2021–07

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