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on Econometric Time Series |
| By: | Joshua C.C. Chan |
| Abstract: | We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in precision-based algorithms for estimating these new models. In an empirical application involving U.S. inflation we find that these moving average stochastic volatility models provide better insample fitness and out-of-sample forecast performance than the standard variants with only stochastic volatility. |
| Keywords: | state space, unobserved components model, precision, sparse, density forecast. |
| JEL: | C11 C51 C53 |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2013-31&r=ets |
| By: | De Graeve, Ferre (Research Department, Central Bank of Sweden); Westermark, Andreas (Research Department, Central Bank of Sweden) |
| Abstract: | Macroeconomic research often relies on structural vector autoregressions to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to DSGE-models. Empirically, short lag-length is deemed necessary as increased parametrization induces excessive uncertainty. The paper shows that this argument is incomplete. Longer lag-length simultaneously reduces misspecification, which in turn reduces variance. For data generated by frontier DSGE-models long-lag VARs are feasible, reduce bias and variance, and have better coverage. Thus, contrary to conventional wisdom, the trivial solution to the critique actually works. |
| Keywords: | VAR; SVAR; Lag-length; Truncation |
| JEL: | C18 E37 |
| Date: | 2013–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:rbnkwp:0271&r=ets |
| By: | Qian, Hang |
| Abstract: | Three new approaches are proposed to handle mixed frequency Vector Autoregression. The first is an explicit solution to the likelihood and posterior distribution. The second is a parsimonious, time-invariant and invertible state space form. The third is a parallel Gibbs sampler without forward filtering and backward sampling. The three methods are unified since all of them explore the fact that the mixed frequency observations impose linear constraints on the distribution of high frequency latent variables. By a simulation study, different approaches are compared and the parallel Gibbs sampler outperforms others. A financial application on the yield curve forecast is conducted using mixed frequency macro-finance data. |
| Keywords: | VAR, Temporal aggregation, State space, Parallel Gibbs sampler |
| JEL: | C11 C32 C82 |
| Date: | 2013–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:47856&r=ets |