Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Enclosed is a copy of the Board of Governors’ Semiannual Agenda of Regulations for the period August 1, 1980 through February 1, 1981. The Semiannual Agenda provides you with information on those regulatory matters that the Board now has under consideration or anticipates considering over the next six months, and is divided into three parts: (1) regulatory matters that the Board had considered during the previous six months on which final action has been taken; (2) regulatory matters that have been proposed for public comment and that require further Board consideration; and (3) regulatory matters that the Board may consider over the next six months.

Federal Reserve Bank of New Yorkの制定せる財務諸表様式について

This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration, the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.

/pdf/unit-roots-and-cointegration-in-panels-55d843t3ur.pdf

Unit Roots and Cointegration in Panels

This paper studies the determinants of income smoothing by management of loan-loss provisions in banks around the world. Using a panel database of 3221 bank-year observations from 40 countries and controlling for unobservable bank effects and for the endogeneity of explanatory variables, we find that bank income smoothing depends on investor protection, disclosure, regulation and supervision, financial structure, and financial development. Results suggest there is less bank income smoothing not only with the strength of investor protection, but also with the extent of accounting disclosure, restrictions on bank activities, and official and private supervision, while there is more income smoothing with market orientation and development of a country's financial system.

Cross-country determinants of bank income smoothing by managing loan loss provisions

Forecasting Bankruptcy More Accurately: A Simple Hazard Model

This study examines the relationship between funding liquidity and bank risk taking. Using quarterly data for U.S. bank holding companies from 1986 to 2014, we find evidence that banks having lower funding liquidity risk as proxied by higher deposit ratios, take more risk. A reduction in banks’ funding liquidity risk increases bank risk as evidenced by higher risk-weighted assets, greater liquidity creation and lower Z-scores. However, our results show that bank size and capital buffers usually limit banks from taking more risk when they have lower funding liquidity risk. Moreover, during the Global Financial Crisis banks with lower funding liquidity risk took less risk. The findings of this study have implications for bank regulators advocating greater liquidity and capital requirements for banks under Basel III.

Funding liquidity and bank risk taking

The main challenge of forecasting credit default risk in loan portfolios is forecasting the default probabilities and the default correlations. We derive a Merton-style threshold-value model for the default probability which treats the asset value of a firm as unknown and uses a factor model instead. In addition, we demonstrate how default correlations can be easily modeled. The empirical analysis is based on a large data set of German firms provided by Deutsche Bundesbank. We find that the inclusion of variables which are correlated with the business cycle improves the forecasts of default probabilities. Asset and default correlations depend on the factors used to model default probabilities. The better the point-in-time calibration of the estimated default probabilities, the smaller the estimated correlations. Thus, correlations and default probabilities should always be estimated simultaneously.

Forecasting Credit Portfolio Risk

https://www.econstor.eu/bitstream/10419/19728/1/200401dkp_b.pdf

Credit risk analytics : measurement techniques, applications, and examples in SAS

https://opus.lib.uts.edu.au/bitstream/10453/123879/4/KruegerRoeschScheule2018.pdf

Harald Scheule

Papers

Funding liquidity and bank risk taking

Forecasting Credit Portfolio Risk

Forecasting Credit Portfolio Risk

Credit risk analytics : measurement techniques, applications, and examples in SAS

The impact of loan loss provisioning on bank capital requirements