...theory and econometrics of corporate finance beyond what is covered in previous courses in corporate finance (esp BUSN92 Empirical Corporate Finance). It is not necessary to have completed BUSN92 Empirical Corporate Finance (corporate finance students), nor BUSN80 Financial Econometrics and BUSN81 Theory of Corporate Finance (MSc finance students), but you are expected to hold equivalent knowledge of the theory and econometrics of corporate finance. The course emphasizes three perspectives: behavioral corporate finance, corporate governance, and microeconometrics. Behavioral corporate finance integrates psychology into the study of corporate financial decisions, while corporate governance focuses on implicit and explicit contracting, supervision, and control for ensuring accountability and reconciliation of conflicting interests. Microeconometrics, finally, refers to econometric tools for analysis of individual-level data on the economic behavior of individuals or firms. Assessment and grading The intention with the assessment is for you to give account for your knowledge and demonstrate your capacity to undertake the abilities you are expected to learn in the course. All assessment tasks must be carried out in English. In the grading, we make use of scoring system where you collect points on different assessment tasks. The maximum score on the course is 100 points and you need at least 50% to pass the course. Your overall grade is determined by adding your individual scores on...
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...M Finance Vrije Universiteit Amsterdam - Fac. der Economische Wet. en Bedrijfsk. - M Finance - 2012-2013 Vrije Universiteit Amsterdam - Fac. der Economische Wet. en Bedrijfsk. - M Finance - 2012-2013 I Inhoudsopgave Vak: Institutional Investments and ALM Vak: Valuation and Corporate Governance Vak: Thesis Vak: Asset Pricing Vak: Derivatives and Asset Management Vak: Empirical Finance Vak: Research Project Finance Vak: Financial Markets and Institutions Vak: Private Equity and Behavioral Corporate Finance for Finance Vak: Financial Risk Management (Quantitative Finance) Vak: Real Estate Management Vak: Adv Corporate Finance 4.1 Vak: Valuation and Corporate Governance for Finance Vak: Institutional Investments and ALM for Finance 1 2 3 3 4 6 7 9 10 11 12 13 14 14 Vrije Universiteit Amsterdam - Fac. der Economische Wet. en Bedrijfsk. - M Finance - 2012-2013 II Institutional Investments and ALM Course code Credits Language of tuition Faculty Coordinator Teaching staff Teaching method(s) E_FIN_IIALM () 6.0 English Fac. der Economische Wet. en Bedrijfsk. prof. dr. C.G.E. Boender prof. dr. C.G.E. Boender, prof. dr. T.B.M. Steenkamp Lecture Course objective Achieve advanced knowledge of the investment process of institutional investors, like pension funds and insurers. The main objective is to fully understand the most important theoretical concepts in the institutional investment process and the way these concepts are used in practice. After following the...
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...Mostly Harmless Econometrics: An Empiricist’ Companion s Joshua D. Angrist Massachusetts Institute of Technology Jörn-Ste¤en Pischke The London School of Economics March 2008 ii Contents Preface Acknowledgments Organization of this Book xi xiii xv I Introduction 1 3 9 10 12 16 1 Questions about Questions 2 The Experimental Ideal 2.1 2.2 2.3 The Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Random Assignment Solves the Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . Regression Analysis of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II The Core 19 21 22 23 26 30 36 38 38 44 47 51 51 3 Making Regression Make Sense 3.1 Regression Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 3.1.2 3.1.3 3.1.4 3.2 Economic Relationships and the Conditional Expectation Function . . . . . . . . . . . Linear Regression and the CEF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic OLS Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saturated Models, Main E¤ects, and Other Regression Talk . . . . . . . . . . . . . . . Regression and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 3.2.2 3.2.3 The Conditional Independence Assumption . . . . . . . . . . . . . . . . . . . . . . . . The Omitted Variables Bias Formula . ....
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...forms of employee involvement. Module Content: • History of the HR function, theories and models of HRM; • The roles and responsibilities of stakeholders in HRM; • The changing nature of work, managing diversity, technology and flexibility; • Human resourcing: recruitment and selection, human resource planning; • Reward and performance management; • Employee relations, employment legislation, the legal framework for unionism; • Human resource development; managing learning, knowledge and change; • The integration of HR and corporate strategy. Teaching Format: One 2-hour lecture per week; Three 1-hour tutorials. Assessment: • Group coursework assignment (40%); • Individual written coursework assignment (60%). Text(s): Beardwell, J. and...
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...become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like ‘what to do if there is choice-based sampling?’ or ‘when to measure effects?’ can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes. Keywords. Propensity score matching; Treatment effects; Evaluation; Sensitivity analysis; Implementation 1. Introduction Matching has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies (see e.g., Heckman et al., 1997a; Dehejia...
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...ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA: NEW INSIGHTS INTO THE CAUSALITY RELATIONSHIP AN ECONOMETRICS ASSIGNMENT SUBMITTED BY ------------------------------------------------- OLUWAFEMI JOSHUA IBRAHIM MATRIC NUMBER: 121937 LEVEL: 700 LECTURER IN-CHARGE: PROFESSOR E.O. OGUNKOLA November, 2010 1) STATEMENT OF THE PROBLEM Electricity plays a very important role in the socio-economic and technological development of every nation. It is widely accepted that there is a strong correlation between socio-economic development and the availability of electricity (Sambo, 2008). It is generally recognized that energy, including electricity, plays a significant role in economic development, not only because it enhances the productivity of capital , labour and other factors of production, but also that increased consumption, particularly commercial energy like electricity, signifies high economic status of a country(Aklas & Yilmaz, 2008). The relationship that exists between electricity consumption and economic growth has been of great interest to many researchers. The study of this relationship arises from the need to understand the complex links between these variables. Such understanding is basic to regulators and investors in deregulated electricity markets, in order to design a system that is reliable, efficient and growth-efficient. The empirical argument has been centered on whether economic growth responds to increase in electricity consumption, or whether...
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...ANALYSIS OF MARKET COMPETITION, SWITCHING COSTS AND ITS CONSEQUENCES IN TELECOMMUNICATIONS IN NEPAL NAME: SAROJ POUDEL DEGREE: MASTER OF INFORMATION SYSTEMS/MASTER OF INFORMATION TECHNOLOGY COURSE: 7112ICT RESEARCH METHODS IN INFORMATION TECHNOLOGY INTRODUCTION The economics of switching costs and network effects have achieved a significant amount of popular, as well as professional attention in the last few decades. It is presently defined as the core factor for new Information Technology economy. Switching costs originates, if a consumer demands a product, or its related accessories(hardware or software), of his own purchases to be compatible with each other this creates economies of scope among his purchases from a single supplier. Whereas network effects arise when a user wants his system to be compatible so that s/he can interact or trade with other users, or switch to the same compatible system, which leads to the creation of economies of scope between different incompatible products. Thus these economies of scope impacts the consumer’s buying and switching behavior between various products. The state of lock-in arises when the switching cost is sufficiently high so that the consumer proceeds using the same product rather than switching to the different product. Lock in is the state where the cost of switching exceeds the benefits of switching. Economics of switching costs is the summation of various types of switching costs including: compatibility...
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...MANAGERIAL ECONOMICS SEMESTER 2, 2012 ASSIGNMENT Assessment This assignment contributes to 20% of the final assessment Word Limit This assignment should be no longer than 2000 words (excluding tables, footnotes and appendix). Please keep within the word limit as marks may be deducted if the assignment is too long. Cover sheet Make sure you put a cover sheet on your assignment identifying student names and ID numbers, your tutor and tutorial times. Also put the word count for the assignment on the cover sheet. Due date 8 October 2012, 4.00 pm. Instructions for Assignment The assignment will involve group work. Students will be required to form a group of three students to prepare the assignment. Students are encouraged to form these groups as soon as possible. Membership of groups can be across lecture streams and tutorials. Each group should nominate a group leader to manage the group process. The group leader should ensure the group members are entered into the ‘Assignment Tool’ and that an electronic copy of the assignment is submitted through the ‘Assignment Tool’ on the subject homepage before the due date. The assignment should be the group's own work and should not have been submitted previously for assessment in another course. It is expected that each member of a group contribute equally to the preparation of the assignment. All students should keep a copy of the assignment. The assignment mark will be posted on the subject homepage after...
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...An Introduction to Matlab for Econometrics John C. Frain TEP Working Paper No. 0110 February 2010 Trinity Economics Papers Department of Economics Trinity College Dublin An Introduction to MATLAB for Econometrics John C. Frain. February 2010 ∗ Abstract This paper is an introduction to MATLAB for econometrics. It describes the MATLAB Desktop, contains a sample MATLAB session showing elementary MATLAB operations, gives details of data input/output, decision and loop structures, elementary plots, describes the LeSage econometrics toolbox and maximum likelihood using the LeSage toolbox. Various worked examples of the use of MATLAB in econometrics are also given. After reading this document the reader should be able to make better use of the MATLAB on-line help and manuals. Contents 1 Introduction 1.1 1.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The MATLAB Desktop . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.2.6 1.2.7 1.2.8 1.2.9 ∗ Comments 4 4 6 6 7 8 8 9 9 9 The Command Window . . . . . . . . . . . . . . . . . . . . . . . . The Command History Window . . . . . . . . . . . . . . . . . . . The Start Button . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Edit Debug window . . . . . . . . . . . . . . . . . . . . . . . . The Figure Windows . . . . . . . . . . . . . . . . . . . . . . . . . . The Workspace Browser . . . . . . . . . . . . . . . . . . . . . . . . The...
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...RESEARCH METHODS MODULE STUDY GUIDE Module Title: | Research Methods | | Module Leader email: | Sharif.Sheriff@uwl.ac.uk | | Module Code: | BA70020E | Level: | 7 (Masters) | Credits: | Academic Year: | 2012/ 2013 | | School: | West London School of Business | Field: | Post Graduate International Business Management | © UWL 2013 Contents Section A - Overview and Content Page 4 Module Leader and Team details Welcome Office hours / contact details Administrative and Technical support Timetable Venue / rooms Module information Content of the module Aims of the module Learning outcomes Learning resources Pre-requisites Section B – Module programme Page 8 Section C – Assessment and Feedback Page 19 Assessment schedule (including deadlines for submission) Formative assessment opportunities and feedback Plagiarism regulations Evaluation of the module Frequently asked questions Appendix 1 Research presentation: marking criteria for MAHRM & Top up students, MBA, Msc, MIBM. Appendix 2 Research Proposal: marking criteria for MAHRM & ‘Top up’ students, MBA, Msc, MIBM. Details of Module leader Name | Sharif Sheriff | Field & School | Postgraduate IBM Field West London School of Business | Email | sharif.sheriff@uwl.ac.uk | Phone | 0208 231 2243 | ...
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...Assignment no: 509 Answer Managerial economics refers to the application of economic theory and the tools of analysis of decision science to examine how an organization can achieve it aims or objectives most efficiently. Importance of managerial economics Managerial Decision Problems Economic theory Microeconomics Macroeconomics Decision Sciences Mathematical Economics Econometrics MANAGERIAL ECONOMICS Application of economic theory and decision science tools to solve managerial decision problems OPTIMAL SOLUTIONS TO MANAGERIAL DECISION PROBLEMS Managerial Decision Problems Economic theory Microeconomics Macroeconomics Decision Sciences Mathematical Economics Econometrics MANAGERIAL ECONOMICS Application of economic theory and decision science tools to solve managerial decision problems OPTIMAL SOLUTIONS TO MANAGERIAL DECISION PROBLEMS Managerial enables the use of economic logic and principles to aid management decision-making. Managers are decision-makers and economics should be relevant to give practical guidance in arriving at right decisions. Every manager has to take important decisions about using his limited resources like land, capital, labour, finance etc. to get the maximum returns, therefore, managerial economics, concentrates on those practical aspects of micro-economics which help in decision-making. Managerial economics focuses on the most profitable use of scarce resources rather than on the achievement of equilibrium prices...
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...Econometrics = Science & art of using economic theory & statistical techniques to analyze economic data 1. Causal Effect & the Logic of Randomized Experiments Causal Relationships Decision depends on understanding relationships among variables Empirical research seek to reveal causal relationships: cause/treatment effect Treatment or costs = variables which are subject to intervention (change) Direction & magnitude of effects? Causal Question 1. Hormone Therapy does HRT risk of coronary events? Causal Question 2.Class size does redcing class size improce outcomes of elementary school? A. pupils get more attention, less class disruptions = better grades Smaller classes = expensive, only possible if they produce better outcomes Potential outcomes & treatment effects of binary treatments Outcome (yi) without treatment: Di = 0 Outcome (yi) with treatment: Di = 1 TE (treatment effect)= difference between potential outcomes: Counterfactuals: Fundamental problem of casual inference (Holland) Not able to observe both potential outcomes (y1i & y0i) (would need parallel world) Outcome that is not observable = counterfactual outcome Average treatment effects (ATE) Estimate average effect in target population (probability that y occurs when D has already happened) straightforward: simple comparison of means estimation of ATE 1. collect data from target population 2. identify individuals with/without treatment 3....
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...hypotheses, or developing models for forecasting. Regression models are able to incorporate complex mathematical functions and operands (the variables that are manipulated) to best describe the associations between sets of variables. Unlike many other statistical techniques, regression allows for the inclusion of variables that may control for confounding phenomena or risk factors. For robust analyses to be conducted, however, the assumptions of regression must be understood and researchers must be aware of diagnostic tests and the appropriate procedures that may be used to correct for violations in model assumptions. CONCLUSION: Despite the complexities and intricacies that can exist in re gre s s i o n , this statistical technique may be applied to a wide range of studies in managed care settings. Given the increased availability of data in administrative databases, the application of these procedures to pharmacoeconomics and outc o m e s assessments may result in more varied and useful scientific investigationsand provide a more solid foundation for health care decision making. KEYWORDS: Claims database analysis, Pharmacoeconomics, Outcomes assessment, Regression analysis J Manag Care Pharm. 2005;11(3):240-51 R esearchers from a wide range of disciplines routinely use regression analyses to understand the mathematical relationships between...
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...the literature, the evidence to support such a relationship is difficult to interpret. Much of the problem resides in the fact that a wide range of variables are lumped together under the rubric “affect.” An attempt is made to ameliorate this situation by defining affective variables in terms of traditional psychological theory and classifying them as a subset of those variables intrinsic to the learner. The conflicting evidence dealing with one important affective variable, anxiety, is then examined, and it is shown that ambiguous experimental results can be resolved if the distinction between facilitating and debilitating anxiety is drawn. Further classificatory distinctions are discussed from the abundant experimentation undertaken by applied psychologists, and an attempt is made to consider the implications of some of this research for adult language learning-for some of the new methodologies in EFL as well as for future research opportunities. Affective Variables One does not have to delve deeply into the literature on the relationship between affective variables and second language learning to discover that “affect” is a cover term under which is swept a wide range of disparate constructs and behaviors. Included under the rubric of affective variables are such various categories as: “cognitive style” (Brown 1973), “ego boundaries” (Taylor 1974), “reserved vs. outgoing personality” (Chastain 1975), and “adventuresome” (Tucker et al. 1976). Perhaps the most peculiar...
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...EFB201 Financial Markets Learning Guide EFB201 Learning Guide 1 Workload Expectations The unit has a two-‐hour lecture with a one-‐hour workshop/tutorial each week. QUT Guidelines are that “Eight to 10 hours per unit per week should be spent outside the classroom reading and working on assignments and tutorial tasks.” This unit covers a large amount of material commensurate with the workload expectations described above. The lectures are an integral part of the course materials and will contain spoken or written material that is additional to that in the textbook and set readings. Conversely, not all the set textbook or other readings will be covered in the lectures. In addition, you will be expected to do your own research in respect of particular topics, and this also forms part of the unit materials. All unit material is assessable; in other words, it is not possible to identify...
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