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on Econometric Time Series |
| By: | Liebermann, Joelle (Central Bank of Ireland) |
| Abstract: | This paper performs a fully real-time nowcasting (forecasting) exercise of US real gross domestic product (GDP) growth using Giannone, Reichlin and Small (2008) factor model framework which enables one to handle unbalanced datasets as available in real-time. To this end, we have constructed a novel real-time database of vintages from October 2000 to June 2010 for a panel of US variables, and can hence reproduce, for any given day in that range, the exact information that was available to a real-time forecaster. We track the daily evolution throughout the current and next quarter of the model nowcasting performance. Similarly to Giannone et al. pseudo realtime results, we find that the precision of the nowcasts increases with information releases. Moreover, the Survey of Professional Forecasters (SPF) does not carry additional information with respect to the model best specification, suggesting that the often cited superiority of the SPF, attributable to judgment, is weak over our sample. Then, as one moves forward along the real-time data flow, the continuous updating of the model provides a more precise estimate of current quarter GDP growth and the SPF becomes stale compared to all the model specifications. These results are robust to the recent recession period. |
| Keywords: | Real-time data, Nowcasting, Forecasting, Factor model. |
| JEL: | E52 C53 C33 |
| Date: | 2011–03 |
| URL: | http://d.repec.org/n?u=RePEc:cbi:wpaper:3/rt/11&r=ets |
| By: | Jouchi Nakajima (Institute for Monetary and Economic Studies, Bank of Japan (Currently in the Personnel and Corporate Affairs Department < studying at Duke University>, E-mail: jouchi.nakajima@stat.duke.edu)) |
| Abstract: | This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In that respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility to the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo (MCMC) method is employed for the estimation of the TVP-VAR models with stochastic volatility. As an example of empirical application, the TVP-VAR model with stochastic volatility is estimated using the Japanese data with significant structural changes in dynamic relationship between the macroeconomic variables. |
| Keywords: | Bayesian inference, Markov chain Monte Carlo, Monetary policy, State space model, Structural vector autoregression, Stochastic volatility, Time-varying parameter |
| JEL: | C11 C15 E52 |
| Date: | 2011–03 |
| URL: | http://d.repec.org/n?u=RePEc:ime:imedps:11-e-09&r=ets |
| By: | Massimiliano Caporin (Università di Padova); Michael McAleer (Erasmus University Rotterdam) |
| Abstract: | In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC, Exponentially Weighted Moving Average, and covariance shrinking, using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem. |
| Keywords: | Covariance forecasting, model confidence set, model ranking, MGARCH, model comparison. |
| JEL: | C32 C53 C52 |
| Date: | 2010–12 |
| URL: | http://d.repec.org/n?u=RePEc:pad:wpaper:0124&r=ets |
| By: | Siem Jan Koopman (VU University Amsterdam); Andre Lucas (VU University Amsterdam); Marcel Scharth (VU University Amsterdam) |
| Abstract: | We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. By combining existing numerical and Monte Carlo integration methods, we obtain a general and efficient likelihood evaluation method for this class of models. Our approach is based on the idea that only a small part of the likelihood evaluation problem requires simulation, even in high dimensional settings. We refer to this method as Numerically Accelerated Importance Sampling. Computational gains of our efficient importance sampler are obtained by relying on Kalman filter and smoothing methods associated with an approximated linear Gaussian state space model. Our approach also leads to the removal of the bias-variance tradeoff in the efficient importance sampling estimator of the likelihood function. We illustrate our new methods by an elaborate simulation study which reveals high computational and numerical efficiency gains for a range of well-known models. |
| Keywords: | State space models; importance sampling; simulated maximum likelihood; stochastic volatility; stochastic copula; stochastic conditional duration |
| JEL: | C15 C22 |
| Date: | 2011–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20110057&r=ets |
| By: | Troy Matheson |
| Abstract: | We develop monthly indicators for tracking growth in 32 advanced and emerging-market economies. We test the historical performance of our indicators and find that they do a good job at describing the business cycle. In a recursive out-of-sample forecasting exercise, we find that the indicators generally produce good GDP growth forecasts relative to a range of time series models. |
| Keywords: | Business cycles , Developed countries , Economic growth , Economic indicators , Emerging markets , Forecasting models , Time series , |
| Date: | 2011–02–24 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:11/43&r=ets |