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on Econometric Time Series |
| By: | Christopher Baum (Boston College; DIW Berlin); Jesús Otero (Universidad del Rosario, Colombia) |
| Abstract: | We present response surface coefficients for a large range of quantiles of the Elliott, Rothenberg and Stock (Econometrica, 1996) DF-GLS unit root tests, for different combinations of the number of observations and the lag order in the test regressions, where the latter can be either specified by the user or endogenously determined. The critical values depend on the method used to select the number of lags. The Stata command ersur is presented, and its use illustrated with an empirical example that tests the validity of the expectations hypothesis of the term structure of interest rates. |
| Date: | 2017–08–10 |
| URL: | http://d.repec.org/n?u=RePEc:boc:scon17:7&r=ets |
| By: | Luca Barbaglia; Christophe Croux; Ines Wilms |
| Abstract: | Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with t-distributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.02073&r=ets |
| By: | Ruben Crevits; Christophe Croux |
| Abstract: | We provide a framework for robust exponential smoothing. For a class of exponential smoothing variants, we present a robust alternative. The class includes models with a damped trend and/or seasonal components. We provide robust forecasting equations, robust starting values, robust smoothing parameter estimation and a robust information criterion. The method is implemented in the R package robets, allowing for automatic forecasting. We compare the standard non-robust version with the robust alternative in a simulation study. Finally, the methodology is tested on data. |
| Keywords: | Automatic Forecasting, Outliers, R package, Time series |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:ete:kbiper:588812&r=ets |
| By: | Ruben Crevits; Christophe Croux |
| Abstract: | The model parameters of linear state space models are typically estimated with maximum likelihood estimation, where the likelihood is computed analytically with the Kalman filter. Outliers can deteriorate the estimation. Therefore we propose an alternative estimation method. The Kalman filter is replaced by a robust version and the maximum likelihood estimator is robustified as well. The performance of the robust estimator is investigated in a simulation study. Robust estimation of time varying parameter regression models is considered as a special case. Finally, the methodology is applied to real data. |
| Keywords: | Kalman Filter, Forecasting, Outliers, Time varying parameters |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:ete:kbiper:588734&r=ets |