by Roger Myerson, Curt Hinrichs and Tom Ziolkowski
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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.
1. 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 Data-Table 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 Risk-Tolerance. 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 Non-Normal 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 Expected-Posterior 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 Moral-Hazard Incentive Constraints. Piecewise-Linear Sharing Rules with Moral Hazard. Corporate Decision-Making 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. Brownian-Motion Growth Models. Log-Optimal Investment Strategies. Exponential Arrival Models. Queuing Models. A Simple Inventory Model. Project Length and Critical Tasks. Summary. Exercises.
APPENDIX: EXCEL ADD-INS FOR USE WITH THIS BOOK.
Roger Myerson, Curt Hinrichs and Tom Ziolkowski
ISBN13: 978-0534423810This 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.
Table of Contents
1. 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 Data-Table 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 Risk-Tolerance. 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 Non-Normal 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 Expected-Posterior 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 Moral-Hazard Incentive Constraints. Piecewise-Linear Sharing Rules with Moral Hazard. Corporate Decision-Making 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. Brownian-Motion Growth Models. Log-Optimal Investment Strategies. Exponential Arrival Models. Queuing Models. A Simple Inventory Model. Project Length and Critical Tasks. Summary. Exercises.
APPENDIX: EXCEL ADD-INS FOR USE WITH THIS BOOK.