Analysis and Prediction of the UK economy

Using the business cycle accounting (BCA) framework pioneered by Chari, Kehoe and McGratten (2007, Econometrica) we examine the causes of the 2008-09 recession in the UK. There has been much commentary on the finnancial causes of this recession, which we might expect to bring about variation in the intertemporal rate of substitution in consumption. However, the recession appears to have been mostly driven by shocks to the efficiency wedge in total production, rather than the intertemporal (asset price) consumption, labour or spending wedge. From an expenditure perspective this result is consistent with the observed large falls in both consumption and investment during the

recession. To assess this result we also simulate artifcial data from a DSGE model in which asset price shocks dominate and and no strong role for the intertemporal consumption wedge using the BCA method. This result does not imply that .nancial frictions did not matter for the recent recession but that such frictions do not necessarily impact only on the intertemporal rate of substitution in consumption.

We investigate the ability of three standard nowcasting methodologies, bridge equations, unrestricted Mixed Data Sampling regressions and mixed frequency VARs, to nowcast the UK GDP. All three methodologies may have advantages over the other, bridge equations are the simplest to construct and are the most transparent. The direct forecasting approach of MIDAS may reduce errors in the face of model misspecification while remaining relatively simple to estimate and forecast with. The mixed frequency VAR allows for dynamics between the variables which may help to reduce the forecast error. We evaluate these methods using a final dataset which mimics the data availability at each period in time for 5 monthly indicators. We find that the VAR on average across all forecast horizons is the most consistent, while MIDAS has the best predictive power at the 1 step ahead horizon. The bridge equations do not appear useful until the final month of the quarter. Throughout the evaluation period the predictive accuracy of the methods varies, the MFVAR performs best during the 'Great Recession' period while MIDAS is better during normal growth periods.

In this paper, we apply the factor-augmented VAR of Bernanke, Boivin and Eliasz (2005) in the context of mixed frequencies for a US and a UK dataset. For the US we further extend the model to allow for regime switching dynamics, we compare the short-term predictive ability of the two models against the standard Mixed Frequency VAR of Murasawa and Mariano (2004, 2010). We find that in general, the MFVAR with factors performs slightly worse than the standard MFVAR for the US dataset, marginally so for forecast horizons greater than one and significantly worse at the single period ahead forecast. This result was broadly consistent for the UK dataset, except at the FAMFVAR performed slightly better at the single period ahead horizon. The Markov switching extension was the worst performing of all of the models. Studying the filtered probabilities for the recessionary regime indicated that

only the deeper of the recessions were captured. Further work on dealing with the label switching problem may be required for better performance for the Bayesian treatment of MFVARs with regime switches.