Tion’ and a rise in volatility during the recent recession. Including

April 27, 2018

Tion’ and a rise in volatility during the recent recession. Including within-quarter monthly indicators tends to dampen the swings in volatility, more so as more ALS-008176 biological activity months of data within the quarter become available. However, even with the BMFSV nowcasting model, there continue to be sizable movements in volatility. The second main finding is that the average log-scores of the BMF and BMFSV Tulathromycin A molecular weight models improve as more data become available for the quarter (i.e. scores are higher for models with 2 months of data than 1 month of data, etc.). As a consequence, some of the nowcasting models with 2 or 3 months of data on the quarter but constant volatility score better than the ARSV model. However, these gains are rarely statistically significant. Moreover, in the precrisis sample, nowcasting models with constant volatilities have a more difficult time beating the ARSV benchmark. Third, both BMFSV models improve on the average log-score of the baseline ARSV specification. The gains increase as the nowcasting models receive more months of data. In most cases, the gains are statistically significant, even in the case of month 1 of the quarter. The large model is better for short horizons; the small for longer horizons. However, results in Carriero et al. (2013) for different subgroups of the indicators indicate that financial indicators, by themselves (as opposed to in conjunction with other indicators, as in our large model), are not very helpful for density forecasting. Finally, for density forecasting, rolling estimation of the BMF model with constant volatility sometimes (not always) improves on the accuracy of the recursively generated forecasts from the same model but falls short of the recursively estimated model with stochastic volatility. Consider forecasts from the small BMF model from month 3 of the quarter. Relative to the ARSV baseline, the recursively estimated BMFSV model has a score differential of 19.5 , compared with a score differential of 1.9 for the rolling window version of the BMF forecast and -0.7 for the recursive version of the BMF forecast. This finding suggests that, in alternative model formulations such as MIDAS, it would be necessary to incorporate stochastic volatility–which5.5.04.54.3.53.02.2.01.5 0 1995 2000 2005 2010 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 20061.A. Carriero, T. E. Clark and M. Marcellino(a)(b)5.5.4.4.3.3.2.2.1.1.0 1995 2000 2005(c)Fig. 2. Volatility (0:5 of equation (2)) (posterior medians of standard deviations) estimates from ARSV ( ) and large BMFSV ( m,t and 85th percentiles) models, last vintage of data: (a) in month 2 of quarter t; (b) in month 3 of quarter t; (c) in month 1 of quarter t C)(, 15thRealtime NowcastingTable 4. Forecast Coverage rates, nominal 70 Results for the following months and quarters: Month 1, quarter t 1985, quarter 1?011, quarter 3 AR 0.925 (0.000) ARSV 0.720 (0.653) Small BMF 0.925 (0.000) Large BMF 0.925 (0.000) Small BMF, rolling 0.822 (0.001) Large BMF, rolling 0.794 (0.016) Small BMFSV 0.748 (0.259) Large BMFSV 0.673 (0.552) 1985, quarter 1?008, quarter 2 AR 0.947 (0.000) ARSV 0.723 (0.614) Small BMF 0.947 (0.000) Large BMF 0.947 (0.000) Small BMF, rolling 0.840 (0.000) Large BMF, rolling 0.830 (0.001) Small BMFSV 0.777 (0.076) Large BMFSV 0.670 (0.541) Month 2, quarter t Month 3, quarter t Month 1, quarter t +0.944 (0.000) 0.692 (0.851) 0.935 (0.000) 0.925 (0.000) 0.841 (0.000) 0.841 (0.000) 0.729 (0.502) 0.673 (0.552) 0.957 (0.000) 0.691 (0.Tion’ and a rise in volatility during the recent recession. Including within-quarter monthly indicators tends to dampen the swings in volatility, more so as more months of data within the quarter become available. However, even with the BMFSV nowcasting model, there continue to be sizable movements in volatility. The second main finding is that the average log-scores of the BMF and BMFSV models improve as more data become available for the quarter (i.e. scores are higher for models with 2 months of data than 1 month of data, etc.). As a consequence, some of the nowcasting models with 2 or 3 months of data on the quarter but constant volatility score better than the ARSV model. However, these gains are rarely statistically significant. Moreover, in the precrisis sample, nowcasting models with constant volatilities have a more difficult time beating the ARSV benchmark. Third, both BMFSV models improve on the average log-score of the baseline ARSV specification. The gains increase as the nowcasting models receive more months of data. In most cases, the gains are statistically significant, even in the case of month 1 of the quarter. The large model is better for short horizons; the small for longer horizons. However, results in Carriero et al. (2013) for different subgroups of the indicators indicate that financial indicators, by themselves (as opposed to in conjunction with other indicators, as in our large model), are not very helpful for density forecasting. Finally, for density forecasting, rolling estimation of the BMF model with constant volatility sometimes (not always) improves on the accuracy of the recursively generated forecasts from the same model but falls short of the recursively estimated model with stochastic volatility. Consider forecasts from the small BMF model from month 3 of the quarter. Relative to the ARSV baseline, the recursively estimated BMFSV model has a score differential of 19.5 , compared with a score differential of 1.9 for the rolling window version of the BMF forecast and -0.7 for the recursive version of the BMF forecast. This finding suggests that, in alternative model formulations such as MIDAS, it would be necessary to incorporate stochastic volatility–which5.5.04.54.3.53.02.2.01.5 0 1995 2000 2005 2010 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 20061.A. Carriero, T. E. Clark and M. Marcellino(a)(b)5.5.4.4.3.3.2.2.1.1.0 1995 2000 2005(c)Fig. 2. Volatility (0:5 of equation (2)) (posterior medians of standard deviations) estimates from ARSV ( ) and large BMFSV ( m,t and 85th percentiles) models, last vintage of data: (a) in month 2 of quarter t; (b) in month 3 of quarter t; (c) in month 1 of quarter t C)(, 15thRealtime NowcastingTable 4. Forecast Coverage rates, nominal 70 Results for the following months and quarters: Month 1, quarter t 1985, quarter 1?011, quarter 3 AR 0.925 (0.000) ARSV 0.720 (0.653) Small BMF 0.925 (0.000) Large BMF 0.925 (0.000) Small BMF, rolling 0.822 (0.001) Large BMF, rolling 0.794 (0.016) Small BMFSV 0.748 (0.259) Large BMFSV 0.673 (0.552) 1985, quarter 1?008, quarter 2 AR 0.947 (0.000) ARSV 0.723 (0.614) Small BMF 0.947 (0.000) Large BMF 0.947 (0.000) Small BMF, rolling 0.840 (0.000) Large BMF, rolling 0.830 (0.001) Small BMFSV 0.777 (0.076) Large BMFSV 0.670 (0.541) Month 2, quarter t Month 3, quarter t Month 1, quarter t +0.944 (0.000) 0.692 (0.851) 0.935 (0.000) 0.925 (0.000) 0.841 (0.000) 0.841 (0.000) 0.729 (0.502) 0.673 (0.552) 0.957 (0.000) 0.691 (0.