This paper uses t and f statistical tests to evaluate empirically the competitive conditions in the German banking system for the period 2003-2007. For this purpose we implement the non-structural estimation technique in logarithmic form (Hondroyiannis, Lolos, Papapetrou, 1999, p.377):
lnTrev = α1+α2 lnPL+α3 lnPK+α4 lnPF+α5 lnRISKASS+α6 lnASSET+α7 lnEMP
Table of Contents
1. Introduction
2a) Testing for autocorrelation
2b) Test for heteroskedacity
2c) Test for normality
2d) Testing for parameter stability
2e) Testing functional form of model
Objectives and Topics
This report aims to empirically evaluate the competitive conditions within the German banking system during the period 2003-2007 by implementing a non-structural estimation technique to analyze the relationships between financial variables.
- Application of statistical t and f-tests to assess regression coefficients.
- Implementation of the Hondroyiannis, Lolos, and Papapetrou (1999) non-structural model.
- Comprehensive diagnostic testing including autocorrelation, heteroskedasticity, and normality.
- Evaluation of parameter stability and functional model form.
- Analysis of bank-specific structural data using the Ordinary Least Square (OLS) method.
Excerpt from the Book
1. Introduction
This paper uses t and f statistical tests to evaluate empirically the competitive conditions in the German banking system for the period 2003-2007. For this purpose we implement the non-structural estimation technique in logarithmic form (Hondroyiannis, Lolos, Papapetrou, 1999, p.377):
lnTrev = α1+α2 lnPL+α3 lnPK+α4 lnPF+α5 lnRISKASS+α6 lnASSET+α7 lnEMP
Dependent variable Trev represents the ratio of total revenue to total assets. Inedependent variables are PL (the ratio of personal expenses to employees, in other words unit price of labor), PK (ratio of capital expenses to fixed assets or unit price of capital), PF (ratio of annual interest expenses to own funds or unit price of funds, RISKASS (ratio of provisions to total assets), ASSET (bank total assets), EMP (total number of employees). αο is constant or intercept of the equation. Total number of employees is taken as an alternative measure or bank size because of data insufficiency on the total number of bank branches (Hondroyiannis, Lolos, Papapetrou, 1999, p.383). Prior to presenting regression results, it must be mentioned that the model introduced is somewhat limited in the sense that it is originally designed to test competitiveness in the Greek banking system. Furthermore, our sample consists of mixed nature of banking institutions, each one having specific structural and capital characteristics. Bank sample was drawn completely randomly from the bankscope database. We use ordinary least square method (OLS) to estimate weighting of relationships between the endogenous and exogenous variables. Please note that tables presented throughout this paper are in short form. Detailed data output is available in the appendix
Summary of Chapters
1. Introduction: Presents the research objective, the econometric model utilized, and the variables used to analyze the German banking system.
2a) Testing for autocorrelation: Investigates the existence of serial correlation in the error terms using Durbin-Watson and Breusch-Godfrey tests.
2b) Test for heteroskedacity: Examines whether the regression errors maintain constant variance by employing White's general test.
2c) Test for normality: Analyzes the distribution of model residuals to check for normality, introducing dummy variables to account for outliers.
2d) Testing for parameter stability: Utilizes the Chow breakpoint test to verify if the regression parameters remain constant over the study period.
2e) Testing functional form of model: Conducts the Ramsey RESET test to determine if the relationship between exogenous and endogenous variables follows a linear functional form.
Keywords
German banking system, competitiveness, non-structural estimation, OLS regression, t-test, f-test, heteroskedasticity, autocorrelation, parameter stability, residual distribution, econometric modeling, bank assets, labor elasticity, financial ratios, Ramsey RESET test.
Frequently Asked Questions
What is the primary focus of this paper?
The paper evaluates the competitive conditions of the German banking system between 2003 and 2007 using a non-structural empirical model.
Which methodology is employed for the analysis?
The study implements an OLS-based regression analysis, following the non-structural technique established by Hondroyiannis, Lolos, and Papapetrou (1999).
What is the central research objective?
The objective is to estimate the weighting of relationships between various bank-specific financial variables and total revenue to assess competitiveness.
Which statistical tests are used to validate the model?
The author employs t-tests, f-tests, Wald tests, Durbin-Watson tests, White’s heteroskedasticity tests, and the Ramsey RESET test.
What are the main variables studied?
Key variables include total revenue/assets (dependent), personal expenses, capital expenses, interest expenses, risk assets, total assets, and number of employees.
How does the author handle non-normality in residuals?
The author identifies outliers (e.g., HASPA Finanzholding data) and introduces dummy variables to shift the residual distribution closer to normal.
Why are dummy variables used in the unrestricted equation?
They are used specifically to remove extreme outliers and improve the normality of residual distribution, ensuring more robust statistical findings.
What does the Chow breakpoint test indicate?
It tests for parameter stability over time; in this study, the null hypothesis of stability was rejected, indicating changes in parameters.
What was the conclusion regarding the model's functional form?
Based on the Ramsey RESET test, the model was found to be non-linear in its functional form.
How is the "size" of the bank measured in this study?
Due to insufficient data on bank branches, the total number of employees is used as a proxy measure for bank size.
- Quote paper
- Dimitar Vasilev (Author), 2010, Report on assessing competitiveness of the German banking system (2003-2008), Munich, GRIN Verlag, https://www.hausarbeiten.de/document/180771
