Senin, 09 September 2013

BOOK: Microeconomics of Banking, FREIXAS & ROCHET

Over the last thirty years, a new paradigm in banking theory has overturned economists' traditional vision of the banking sector. The asymmetric information model, extremely powerful in many areas of economic theory, has proven useful in banking theory both for explaining the role of banks in the economy and for pointing out structural weaknesses in the banking sector that may justify government intervention. In the past, banking courses in most doctoral programs in economics, business, or finance focused either on management or monetary issues and their macroeconomic consequences; a microeconomic theory of banking did not exist because the Arrow-Debreu general equilibrium model of complete contingent markets (the standard reference at the time) was unable to explain the role of banks in the economy. 
This text provides students with a guide to the microeconomic theory of banking that has emerged since then, examining the main issues and offering the necessary tools for understanding how they have been modeled. This second edition covers the recent dramatic developments in academic research on the microeconomics of banking, with a focus on four important topics: the theory of two-sided markets and its implications for the payment card industry; "non-price competition" and its effect on the competition-stability tradeoff and the entry of new banks; the transmission of monetary policy and the effect on the functioning of the credit market of capital requirements for banks; and the theoretical foundations of banking regulation, which have been clarified, although recent developments in risk modeling have not yet led to a significant parallel development of economic modeling. This book also tells us about concept of banking efficiency.
Xavier Freixas is Dean of the Undergraduate School of Economics and Business Administration and Professor at the Universitat Pompeu Fabra, Barcelona. Jean-Charles Rochet is Professor of Mathematics and Economics at the University of Toulouse School of Economics.

Selasa, 03 September 2013

BUKU: Strategic Performance Management and Measurement Using Data Envelopment Analysis

Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement.

Minggu, 01 September 2013

Pengantar Umum DEA

DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to generalize the Farrell(1957) single-input/ single-output technical efficiency measure to the multiple-input/ multiple-output case by constructing a relative efficiency score as the ratio of a single virtual output to a single virtual input. Thus DEA become a new tool in operational research for measuring technical efficiency. It originally was developed by Charnes, Cooper, Rhodes(1978) with CRS and was extended by Banker, Charnes, Cooper(1984) to include variable returns to scale. So the basic DEA models are known as CCR and BCC. 

Since 1978 over 1000 articles, books and dissertation have been published and DEA has rapidly extended to returns to scale, dummy or categorical variables, discretionary and non-discretionary variables, incorporating value judgments, longitudinal analysis, weight restrictions, stochastic DEA, non-parametric Malmquist indices, technical change in DEA and many other topics. 

Up to now the DEA measure has been used to evaluate and compare educational departments (schools, colleges and universities), health care (hospitals, clinics) prisons, agricultural production, banking, armed forces, sports, market research, transportation (highway maintenance), courts, benchmarking, index number construction and many other applications.At the moment researchers follow wide ranges of DEA and related topics.
 
Here are some topics in DEA:
Returns to scale
Dummy or categorical variables
Discretionary and non-discretionary variables
Incorporating judgment
Longitudinal analysis
Weight restriction
Stochastic DEA
Fuzzy and imprecise DEA
Non-parametric Malmquist indices
Technical change in DEA
Dynamics of Data Envelopment Analysis
Sensitivity
and many more ….