|
on Financial Development and Growth |
| By: | Zhou, Peng (Cardiff Business School) |
| Abstract: | This paper proposes a new filter technique to separate trend and cycle based on stylised economic properties of trend and cycle, rather than relying on ad hoc statistical proper-ties such as frequency. Given the theoretical separation between economic growth and business cycle literature, it is necessary to make the measures of trend and cycle match what the respective theories intend to explain. The proposed filter is applied to the long macroeconomic data collected by the Bank of England (1700-2015). |
| Keywords: | Filter, Trend, Cycle |
| JEL: | C32 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2017/1&r=fdg |
| By: | Andrea Carriero (Queen Mary University of London); Galvao, Ana Beatriz (University of Warwick); Kapetanios, George (Kings College London) |
| Abstract: | This paper contributes to the academic literature and the practice of macroeconomic forecasting. Our evaluation compares the performance of four classes of state-of-art forecasting models : Factor-Augmented Distributed Lag (FADL) Models, Mixed Data Sampling (MIDAS) Models, Bayesian Vector Autoregressive (BVAR) Models and a medium-sized Dynamic Stochastic General Equilibrium Model (DSGE). We look at these models to predict output growth and ination with datasets from the US, UK, Euro Area, Germany, France, Italy and Japan. We evaluate the accuracy of point and density forecasts, and compare models with a large set of predictors with models that employ a medium-sized dataset. Our empirical results shed light on how the predictive ability of economic indicators for output growth and ination changes with horizon, on the impact of dataset size on the calibration of density forecasts, and how the choice of the multivariate forecasting model depends on the forecasting horizon. |
| Keywords: | factor models ; BVAR models ; MIDAS models ; DSGE models ; density forecasts JEL Classification Numbers: C53 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkemf:10&r=fdg |
| By: | Schivardi, Fabiano (LUISS School of European Political Economy); Sette, Enrico (Bank of Italy); Tabellini, Guido (Bocconi University) |
| Abstract: | Do banks with low capital extend excessive credit to weak firms, and does this matter for aggregate efficiency? Using a unique data set that covers almost all bank-firm relationships in Italy in the period 2004-2013, we find that, during the Eurozone financial crisis: (i) Under-capitalized banks were less likely to cut credit to non-viable firms. (ii) Credit misallocation increased the failure rate of healthy firms and reduced the failure rate of non viable firms. (iii) Nevertheless, the adverse effects of credit misallocation on the growth rate of healthier firms were negligible, and so were the effects on TFP dispersion. This goes against previous influential findings that, we argue, face serious identification problems. Thus, while banks with low capital can be an important source of aggregate inefficiency in the long run, their contribution to the severity of the great recession via capital misallocation was modest. |
| Keywords: | Bank capitalization; zombie lending; capital misallocation |
| JEL: | D23 E24 G21 |
| Date: | 2017–03–10 |
| URL: | http://d.repec.org/n?u=RePEc:ris:sepewp:2017_003&r=fdg |