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Probability Models for Economic Decisions with CDROM (Duxbury Applied)by Roger B. Myerson
Synopses & ReviewsPublisher Comments:This book is an introduction to the use of probability models for analyzing risks and economic decisions. Throughout this book, author Roger Myerson focuses on showing students how to use probability in complex realistic situations. All the analytical work in this book is done in Microsoft Excel spreadsheets. As a result of the emphasis on spreadsheet modeling, students will also develop sophisticated spreadsheet skills. However, the main goals of the book are to make the practical power of probability analysis accessible to students and to demonstrate how to apply these concepts in the real world.
Book News Annotation:Myerson (economics, University of Chicago) takes the uncertainty out of analyzing uncertainty in this text/CDROM introduction to the use of probability models for analyzing risks and economic decisions. Focus is on using probability models with Micosoft Excel to quantify uncertainty and risk in complex, realistic situations. Chapter cases and exercises are included. The CDROM contains software developed by the author to extend Excel's capacity to Monte Carlo simulation and risk analysis spreadsheets. The text can be used at the undergraduate or MBA level, and no previous study of probability is assumed.
Annotation ©2004 Book News, Inc., Portland, OR (booknews.com) Synopsis:Learn to use probability in complex realistic situations with PROBABILITY MODELS FOR ECONOMIC DECISIONS. This introduction to the use of probability models for analyzing risks and economic decisions uses Microsoft Excel spreadsheets for the analytic work. As a result of the emphasis on spreadsheet modeling, you'll also develop sophisticated spreadsheet skills.
About the AuthorRoger B. Myerson is the W. C. Norby Professor of Economics at the University of Chicago. He previously taught at the Kellogg School of Management at Northwestern University (19762001). His teaching interests include decision analysis, probability modeling, game theory, mathematical optimization theory, social choice and formal political theory, and economics of information. Dr. Myerson has received a number of professional awards, including Guggenheim Fellow (19831984), Sloan Foundation Fellow (19841986), Fellow of the Econometric Society (elected 1983), Fellow of the American Academy of Arts and Sciences (elected 1993), and an honorary doctorate from the University of Basel (2002). His research interests include game theory, economics of information, and analysis of voting systems.
Table of Contents1. SIMULATION AND CONDITIONAL PROBABILITY. Getting Started with Simtools in Excel. How to Toss Coins in a Spreadsheet. A Simulation Model of Twenty Sales Calls. Analysis Using Excel's DataTable Command. Conditional Independence. A Continuous Random Skill Variable from a Triangular Distribution. Probability Trees and Bayes's Rule. Advanced Spreadsheet Techniques: Constructing a Table with Multiple Inputs. Using Models. Summary. Exercises. 2. DISCRETE RANDOM VARIABLES. Unknown Quantities in Decisions Under Uncertainty. Charting a Probability Distribution. Simulating Discrete Random Variables. Expected Value and Standard Deviation. Estimates from Sample Data. Accuracy of Sample Estimates. Decision Criteria. Multiple Random Variables. Summary. Exercises. 3. UTILITY THEORY WITH CONSTANT RISK TOLERANCE. Taking Account of Risk Aversion: Utility Analysis with Probabilities. Utility Analysis from Simulation Data. The More General Assumption of Linear Risk Tolerance. Advanced Technical Note on Utility Theory. Advanced Technical Note on Constant RiskTolerance. Summary. Exercises. 4. CONTINUOUS RANDOM VARIABLES. Normal Distributions. EXP and LN. Lognormal Distributions. Generalized Lognormal Distributions. Subjective Probability Assessment. A Decision Problem with Discrete and Continuous Unknowns. Certainty Equivalents of Normal Lotteries. Other Probability Distributions. Summary. Exercises. 5. CORRELATION AND MULTIVARIATE NORMAL RANDOM VARIABLES. Joint Distributions of Discrete Random Variables. Covariance and Correlation. Linear Functions of Several Random Variables. Estimating Correlations from Data. Making Multivariate Normal Random Variables with CORAND and NORMINV. Portfolio Analysis with Multivariate Normal Asset Returns. Excel Solver and Efficient Portfolio Design. Subjective Assessment of Correlations. Using CORAND with NonNormal Random Variables. More About Linear Functions of Random Variables. Summary. Exercises. 6. CONDITIONAL EXPECTATION. Dependence Among Random Variables. Estimating Conditional Expectations and Standard Deviations. The ExpectedPosterior Law in a Discrete Example. Backwards Analysis of Conditional Expectations in Tree Diagrams. Conditional Expectation Relationships and Correlation. Uncertainty About a Probability. Linear Regression Models. Regression Analysis and Least Squared Errors. Summary. Exercises. 7. OPTIMIZATION OF DECISION VARIABLES. General Techniques for Using Simulation in Decision Analysis. Strategic Use of Information. Decision Trees. A Simple Bidding Problem. The Winner's Curse. Analyzing Competitive Behavior. Summary. Exercises. 8. RISK SHARING AND FINANCE. Optimal Risk Sharing in a Partnership of Individuals with Constant Risk Tolerance. Optimality of Linear Rules in the Largest Class of Nonlinear Sharing Rules. Risk Sharing Subject to MoralHazard Incentive Constraints. PiecewiseLinear Sharing Rules with Moral Hazard. Corporate DecisionMaking and Asset Pricing in the Stock Market. Fundamental Ideas of Arbitrage Pricing Theory. Summary. Exercises. 9. DYNAMIC MODELS OF GROWTH AND ARRIVALS. Net Present Value. Forecasting Models. Forecasting Example: Goeing Case. BrownianMotion Growth Models. LogOptimal Investment Strategies. Exponential Arrival Models. Queuing Models. A Simple Inventory Model. Project Length and Critical Tasks. Summary. Exercises. APPENDIX: EXCEL ADDINS FOR USE WITH THIS BOOK.
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