In this study we evaluate the distortion of the ratio of non-performing loans (NPL) caused by rapid credit growth to show that the bias in this ratio (caused by the prolonged credit boom) may indeed be significant. Next, we discuss an adjustment to the NPL ratio based on a theoretical model of a loan portfolio. This adjustment is robust for credit booms and busts; therefore, it can be used to compare credit quality ratios across distinct portfolios and banks as well as to simulate future NPL ratio developments. Our estimates of the portfolio of housing loans in Poland show that the new adjusted index of non-performing loans is robust to different model specifications.

This research analyzes factors affecting the scientific success of
central bankers. We combine data from the RePEc and EDIRC databases,
which contain information about economic publications of authors from
182 central banks. We construct a dataset containing information about
3312 authors and almost 80,000 scientific papers published between 1965
and 2020. The results from Poisson regressions of citation impact
measure (called the h-index) on a number of research features
suggest that economists from the U.S. Federal Reserve Banks,
international financial institutions, and some eurozone central banks
are cited more frequently than economists with similar characteristics
from central banks located in emerging markets. Researchers from some
big emerging economies like Russia or Indonesia are cited particularly
infrequently by the scientific community. Beyond these outcomes, we
identify a significant positive relationship between research networking
and publication success. Moreover, economists cooperating with highly
cited scientists also obtain a high number of citations even after
controlling for the size of their research networks.

We estimated a structural vector autoregressive (SVAR) model describing the links between a banking sector and a real economy. We proposed a new method to verify robustness of impulse-response functions to the ordering of variables in an SVAR model. This method applies permutations of orderings of variables and uses the Cholesky decomposition of the error covariance matrix to identify parameters. Impulse response functions are computed and combined for all permutations. We explored the method in practice by analyzing the macro-financial linkages in the Polish economy. Our results indicate that the combined impulse response functions are more uncertain than those from a single model specification with a given ordering of variables, but some findings remain robust. It is evident that macroeconomic aggregate shocks and interest rate shocks have a significant impact on banking variables.