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on Econometric Time Series |
| By: | Danilo Leiva-Leon (Banco de España) |
| Abstract: | This paper proposes a Markov-switching framework to endogenously identify periods where economies are more likely to (i) synchronously enter recessionary and expansionary phases, and (ii) follow independent business cycles. The reliability of the framework is validated with simulated data in Monte Carlo experiments. The framework is applied to assess the timevarying intra-country synchronization in US. The main results report substantial changes over time in the cyclical affiliation patterns of US states, and show that the more similar the economic structures of states, the higher the correlation between their business cycles. A synchronization-based network analysis discloses a change in the propagation pattern of aggregate contractionary shocks across states, suggesting that the US has become more internally synchronized since the early 1990s. |
| Keywords: | business cycles, Markov-Switching, network analysis |
| JEL: | E32 C32 C45 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1726&r=ets |
| By: | Pierre Guérin (Bank of Canada); Danilo Leiva-Leon (Banco de España) |
| Abstract: | This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In the empirical application, we forecast U.S. business cycle turning points with statelevel employment data. We find that forecasts obtained with our best combination scheme provide timely updates of U.S. recessions in that they outperform a notoriously dicult benchmark to beat (the anxious index from the Survey of Professional Forecasters) for short-term forecasts. |
| Keywords: | business cycles, forecast combination, forecasting, Markov-switching, nowcasting |
| JEL: | C53 E32 E37 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1727&r=ets |
| By: | Lovcha, Yuliya; Pérez Laborda, Alejandro |
| Abstract: | This paper shows that the frequency domain estimation of VAR models over a frequency band can be a good alternative to pre-filtering the data when a low-frequency cycle contaminates some of the variables. As stressed in the econometric literature, pre-filtering destroys the low-frequency range of the spectrum, leading to substantial bias in the responses of the variables to structural shocks. Our analysis shows that if the estimation is carried out in the frequency domain, but employing a sensible band to exclude (enough) contaminated frequencies from the likelihood, the resulting VAR estimates and the impulse responses to structural shocks do not present significant bias. This result is robust to several specifications of the external cycle and data lengths. An empirical application studying the effect of technology shocks on hours worked is provided to illustrate the results. Keywords: Impulse-response, filtering, identification, technology shocks. JEL Classification: C32, C51, E32, E37 |
| Keywords: | Previsió econòmica, Models economètrics, Cicles econòmics, 33 - Economia, |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:urv:wpaper:2072/290743&r=ets |
| By: | Massimo Franchi ("Sapienza" University of Rome) |
| Abstract: | Minimality of the state space representation of a stochastic process places restrictions on the rank of certain matrices that show up in the leading coecient of the principal part of the MA transfer functions implied by the system. When unit roots are allowed for, those restrictions and the reduced rank structure of the state process shape the integration and cointegration properties of the state and the observed processes. A characterization of cointegration is presented in the I(d) case and it is further found that the present results lead to a construction of the canonical form in Bauer and Wagner (2012) Econometric Theory, 28, 1313-49. |
| Keywords: | State Space systems, unit roots, cointegration. |
| JEL: | C32 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:sas:wpaper:20174&r=ets |
| By: | Mikio Ito; Akihiko Noda; Tatsuma Wada |
| Abstract: | A non-Bayesian, generalized least squares (GLS)-based approach is formally proposed to estimate a class of time-varying AR parameter models. This approach has partly been used by Ito et al. (2014, 2016a,b), and is proven very efficient because, unlike conventional methods, it does not require the Kalman filtering and smoothing procedures, but yields a smoothed estimate that is identical to the Kalman-smoothed estimate. Unlike the maximum likelihood estimator, the possibility of the pile-up problem is shown to be small. In addition, this approach enables us to possibly deal with stochastic volatility models and models with a time-dependent variance-covariance matrix. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1707.06837&r=ets |