Search results

1 – 10 of over 4000
Book part
Publication date: 13 October 2009

Andreas Kleine and Regina Schlindwein

DEA is a favored method to investigate the efficiency of institutions that provide educational services. We measure the efficiency of German universities especially from the…

Abstract

DEA is a favored method to investigate the efficiency of institutions that provide educational services. We measure the efficiency of German universities especially from the students’ perspective. Since 1998, the Centrum für Hochschulentwicklung (CHE) evaluates German universities annually. The CHE ranking consists of three ranking groups for different indicators, but they do not create a hierarchy of the universities. Thus, a differentiation of the universities ranked in the same group is not possible. Based on the CHE data set, especially the surveys among students, we evaluate teaching performance from the students’ point of view using data envelopment analysis (DEA). DEA enables us to identify departments that – in the students’ perspective – are efficient in the sense that they provide high quality of education. As a method for performance evaluation, we apply a DEA bootstrap approach. By the use of this approach, we incorporate stochastic influences in the data and derive confidence intervals for the efficiency. Based on data generated by the bootstrap procedure, we are able to identify stochastic efficient departments. These universities serve as a benchmark to improve teaching performance.

Details

Financial Modeling Applications and Data Envelopment Applications
Type: Book
ISBN: 978-1-84855-878-6

Book part
Publication date: 19 October 2020

Tiziano Arduini, Eleonora Patacchini and Edoardo Rainone

The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification…

Abstract

The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification conditions of peer effects and consider a two-stage least squares estimation approach. Large sample properties of the proposed estimators are derived. Their performance in finite samples is investigated using Monte Carlo simulations.

Book part
Publication date: 15 August 2006

N.K. Kwak, Yong Soo Chun and Seongho Kim

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations. This…

Abstract

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations. This paper presents the theoretical measures of the railway systems, along with the bootstrap DEA analysis. A DEA model is applied to evaluate the relative efficiency of railway operations of 29 UIC (Union Internationale des Chemins de fer) countries, based on the data obtained from the International UIC publications. The bootstrap DEA analysis provides information (bias estimates) on the sensitivity of the DEA efficiency index to the sampling variations. The model results are analyzed and evaluated in terms of their relative operational performance efficiency. The model results facilitate an organization's decision-making by providing valuable information.

Details

Applications of Management Science: In Productivity, Finance, and Operations
Type: Book
ISBN: 978-0-85724-999-9

Book part
Publication date: 29 March 2016

Lasse Mertins and Lourdes Ferreira White

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision outcomes…

Abstract

Purpose

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision outcomes: performance ratings, perceived informativeness, and decision efficiency.

Methodology/approach

Using an original case developed by the researchers, a total of 135 individuals participated in the experiment and rated the performance of carwash managers in two different scenarios: one manager excelled financially but failed to meet targets for all other three BSC perspectives and the other manager had the opposite results.

Findings

The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC. Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives, contrary to the usual predictions based on the financial measure bias. The evaluators reported that informativeness of the BSC was highest in the table or graph without summary measure formats, and, surprisingly, adding a summary measure to the graph format significantly reduced perceived informativeness compared to the table format. Decision efficiency was better for the graph formats (with or without summary measure) than for the table format.

Originality/value

Ours is the first study to compare tables, graphs with and without a summary measure in the context of managerial performance evaluations and to examine their impact on ratings, informativeness, and efficiency. We developed an original case to test the boundaries of the financial measure bias.

Details

Advances in Management Accounting
Type: Book
ISBN: 978-1-78441-652-2

Keywords

Book part
Publication date: 24 April 2023

Florens Odendahl, Barbara Rossi and Tatevik Sekhposyan

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations…

Abstract

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.

Details

Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications
Type: Book
ISBN: 978-1-83753-212-4

Keywords

Book part
Publication date: 23 June 2016

Yulia Kotlyarova, Marcia M. A. Schafgans and Victoria Zinde-Walsh

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the…

Abstract

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the leading term in the expansion of the bias may provide a poor approximation. We explore the relation between smoothness and bias and provide estimators for the degree of the smoothness and the bias. We demonstrate the existence of a linear combination of estimators whose trace of the asymptotic mean-squared error is reduced relative to the individual estimator at the optimal bandwidth. We examine the finite-sample performance of a combined estimator that minimizes the trace of the MSE of a linear combination of individual kernel estimators for a multimodal density. The combined estimator provides a robust alternative to individual estimators that protects against uncertainty about the degree of smoothness.

Details

Essays in Honor of Aman Ullah
Type: Book
ISBN: 978-1-78560-786-8

Keywords

Book part
Publication date: 20 October 2017

Eleftherios Aggelopoulos

Purpose: The present study investigates how the performance of Greek bank branching varies when the external environment causes dramatic changes that are reflected in recession…

Abstract

Purpose: The present study investigates how the performance of Greek bank branching varies when the external environment causes dramatic changes that are reflected in recession and capital control effects.

Design/Methodology: A unique dataset of accounting Profit and Loss statements of retail branches of a systemic Greek commercial bank, closely supervised by the European Central Bank (ECB), is utilized. A profit bootstrap Data Envelopment Analysis (DEA) model is selected to measure the bank branch efficiency. The derived efficiency estimates are analyzed through a second-stage panel data regression analysis against a set of efficiency drivers related to branch profitability, diversification of income, branch size, and branch activity.

Findings: The results indicate that recession negatively affects branch efficiency in the short and long run. The occurrence of recession significantly intensifies the efficiency premium of branch profitability, reduces the efficiency premium of diversification of income (i.e., a negative efficiency effect is recorded during the early recession period), while mitigating the generally negative efficiency effect of branch size. The analysis of efficiency effects from the deep recession period that encompasses capital controls reveals the importance of diversification of income for the improvement of profit efficiency at bank branch level.

Originality/Value: This is the first branch banking study that explores branch efficiency alteration and the dynamic of branch efficiency drivers when the economy suddenly enters recession and afterwards when conditions are becoming extremely difficult and consequently capital controls are imposed on the economy.

Book part
Publication date: 1 December 2016

Jaepil Han, Deockhyun Ryu and Robin Sickles

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial…

Abstract

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

Details

Spatial Econometrics: Qualitative and Limited Dependent Variables
Type: Book
ISBN: 978-1-78560-986-2

Keywords

Book part
Publication date: 18 January 2022

Luca Nocciola

The author shows that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlights under which…

Abstract

The author shows that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlights under which conditions. In doing so, the author generalizes the Pesaran and Timmermann (2005)’s forecast error decomposition and shows that it depends on four terms: (1) a period ahead risk; (2) a bias due to a conditional mean shift; (3) a bias due to a variance mismatch; (4) a gap term valid only conditionally. The author also derives new expressions for the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, the author introduces new simulation-based estimators for the finite sample forecast properties which are based on the derived decomposition. The author’s finding points out that, in some cases, parameter instability can be neglected by extending the window backward and forecasters can be insured against higher forecast risk under this model class as well, generalizing Pesaran and Timmermann (2005)’s result. The author’s result gives renewed importance to break tests, in order to distinguish cases when break-neglection is (not) appropriate.

Details

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

Keywords

Abstract

Details

The Efficiency of Mutual Fund Families
Type: Book
ISBN: 978-1-78743-799-9

1 – 10 of over 4000