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In a survey of more than 600 economists, most respondents prefer their central bank to have an explicit inflation target. Roughly half want the central bank to keep its current target. Two thirds of the rest want to raise the target, with a median preferred change of one percentage point. In a hypothetical scenario in which the central bank has no prior history of inflation targeting, an additional 12% of the respondents would prefer a different (typically higher) target than the current one. This result suggests that the costs of changing the current target hold some respondents back from wanting an actual target change. Respondents who are worried about the central bank credibility are less likely to support a target raise. Conversely, preference for a target raise is more likely to come from those who are concerned about the zero lower bound on the nominal interest rate. The average estimate of the equilibrium real interest rate in the sample is 0.6%. However, personal views about the equilibrium real interest rate do not predict a preference for a target raise.

We show that financial crises are preceded by changes in specific types of narrative information contained in newspaper article titles. Our novel international dataset and the resulting empirical evidence are gathered by integrating information from a large panel of economic news articles in global newspapers between the years 1870 and 2016 with conventional macroeconomic and financial indicators. We find that the predictive information of newspaper article titles that signals coming crisis episodes is substantial ver and above the macroeconomic and financial indicators. The new indicators capture common features that have often been discussed as potential causes of specific crises but which have not been incorporated into empirical models.

The recent financial crises have brought into focus questions regarding the quality of banks' assets. We study the patterns in banks reserving for and reporting of loan losses in the EU before and after implementation of the Single Supervisory Mechanism (SSM). We find that banks that 1) have less tier 1 capital, 2) are smaller, 3) are less liquid and 4) have smaller net interest margins either report relatively smaller loan loss reserves or less loan losses, even after including various controls. This supports the hypothesis that financially weaker banks may have a larger incentive to engage in balance sheet window dressing. We further find that the SSM has reduced but not eliminated the under-reserving and under-reporting bias. In addition, there has been a separate positive effect on the overall proportion of nonperforming loans (NPLs) that are realised as losses among the banks that have been under direct supervision by the SSM since implementation of the SSM.

CDS spreads are often used as market's view of credit risk. There is no popular alternative to it; perhaps only the distance-to-default measure based on Merton (1974) comes close to it. In this paper we investigate the relationship between these two measures for large European banks in post subprime crises era. The analysis makes use of conventional Granger causality test statistics for individual banks and for the whole panel data. As for the results, we find that the lead-lag relationship between these variables varies over time and over different banks and economic regimes. The lead of distance-to-default is stronger for banks in problem countries (PIGS), during European debt crises, for relatively small banks and when there are large changes in CDS spread. These results suggest that we may have predictive power by not only using the CDS spread, but also other measures such as the distance-to-default.

The early warning system literature on banking crises has often relied on linear classifiers such as the logit model, which are usually estimated with large datasets of multiple regions of countries. We construct an EWS based on an artificial neural network model with monthly data from the Scandinavian countries to tackle the poor generalization ability of the usual models that might be due to regional heterogeneity of the countries and a nonlinear decision boundary of the classification problem. We show that the Finnish and Swedish banking crises in 1991 were quite predictable with an artificial neural network model when information from earlier crises in Denmark and Norway was used. We also use cross validation in the model selection process to get the optimal amount of complexity to the models. Finally the area under the ROC-curve is used as the model assessment criteria and in this framework we show that the artificial neural network outperforms the logit regression in banking crises prediction.