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E-grāmata: Probability Models for Economic Decisions, second edition

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(California Polytechnic State University), (University of Chicago)
  • Formāts: EPUB+DRM
  • Sērija : The MIT Press
  • Izdošanas datums: 17-Dec-2019
  • Izdevniecība: MIT Press
  • Valoda: eng
  • ISBN-13: 9780262355605
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Probability Models for Economic Decisions, second editionPalielināt
  • Formāts: EPUB+DRM
  • Sērija : The MIT Press
  • Izdošanas datums: 17-Dec-2019
  • Izdevniecība: MIT Press
  • Valoda: eng
  • ISBN-13: 9780262355605
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An introduction to the use of probability models for analyzing risk and economic decisions, using spreadsheets to represent and simulate uncertainty.

This textbook offers an introduction to the use of probability models for analyzing risks and economic decisions. It takes a learn-by-doing approach, teaching the student to use spreadsheets to represent and simulate uncertainty and to analyze the effect of such uncertainty on an economic decision. Students in applied business and economics can more easily grasp difficult analytical methods with Excel spreadsheets.

The book covers the basic ideas of probability, how to simulate random variables, and how to compute conditional probabilities via Monte Carlo simulation. The first four chapters use a large collection of probability distributions to simulate a range of problems involving worker efficiency, market entry, oil exploration, repeated investment, and subjective belief elicitation. The book then covers correlation and multivariate normal random variables; conditional expectation; optimization of decision variables, with discussions of the strategic value of information, decision trees, game theory, and adverse selection; risk sharing and finance; dynamic models of growth; dynamic models of arrivals; and model risk.

New material in this second edition includes two new chapters on additional dynamic models and model risk; new sections in every chapter; many new end-of-chapter exercises; and coverage of such topics as simulation model workflow, models of probabilistic electoral forecasting, and real options. The book comes equipped with Simtools, an open-source, free software used througout the book, which allows students to conduct Monte Carlo simulations seamlessly in Excel.



An introduction to the use of probability models for analyzing risk and economic decisions, using spreadsheets to represent and simulate uncertainty.
Preface xi
1 Simulation and Conditional Probability
1(58)
1.0 Getting Started with Simtools in Excel
2(1)
1.1 How to Toss Coins in a Spreadsheet
3(4)
1.2 A Simulation Model of 20 Sales Calls
7(9)
1.3 Analysis Using Excel's Data-Table Command
16(4)
1.4 Conditional Independence
20(1)
1.5 A Continuous Random Skill Variable from a Triangular Distribution
20(7)
1.6 Probability Trees and Bayes's Rule
27(11)
1.7 Advanced Spreadsheet Techniques: Constructing a Table with Multiple Inputs
38(3)
1.8 Using Models
41(2)
1.9 The Modeling Process
43(5)
1.10 Summary
48(11)
Further Readings
50(1)
Exercises
50(9)
2 Discrete Random Variables
59(38)
2.1 Unknown Quantities in Decisions under Uncertainty
59(3)
2.2 Charting a Probability Distribution
62(3)
2.3 Simulating Discrete Random Variables
65(5)
2.4 Expected Value and Standard Deviation
70(5)
2.5 Estimates from Sample Data
75(3)
2.6 Accuracy of Sample Estimates
78(6)
2.7 Decision Criteria
84(5)
2.8 Multiple Random Variables
89(2)
2.9 Summary
91(6)
Further Readings
92(1)
Exercises
93(4)
3 Utility Theory with Constant Risk Tolerance
97(32)
3.1 Taking Account of Risk Aversion: Utility Analysis with Probabilities
98(11)
3.2 Utility Analysis from Simulation Data
109(2)
3.3 The More General Assumption of Linear Risk Tolerance
111(2)
3.4 Advanced Technical Note on Expected Utility Theory
113(5)
3.5 Advanced Technical Note on Constant Risk Tolerance
118(5)
3.6 Limitations of Expected Utility Theory
123(2)
3.7 Summary
125(4)
Further Readings
126(1)
Exercises
126(3)
4 Continuous Random Variables
129(52)
4.1 Normal Distributions
130(5)
4.2 EXP and LN
135(2)
4.3 Lognormal Distributions
137(7)
4.4 Application: The Time Diversification Fallacy
144(5)
4.5 Generalized Lognormal Distributions
149(3)
4.6 Subjective Probability Assessment
152(5)
4.7 A Decision Problem with Discrete and Continuous Unknowns
157(6)
4.8 Certainty Equivalents of Normal Lotteries
163(1)
4.9 Other Probability Distributions
164(9)
4.10 Summary
173(8)
Further Readings
175(1)
Exercises
176(5)
5 Correlation and Multivariate Normal Random Variables
181(58)
5.1 Joint Distributions of Discrete Random Variables
182(4)
5.2 Covariance and Correlation
186(2)
5.3 Linear Functions of Several Random Variables
188(4)
5.4 Estimating Correlations from Data
192(5)
5.5 Making Multivariate Normal Random Variables with CORAND and NORM.INV
197(5)
5.6 Portfolio Analysis with Multivariate Normal Asset Returns
202(5)
5.7 Excel Solver and Efficient Portfolio Design
207(7)
5.8 Political Forecasting
214(6)
5.9 Subjective Assessment of Correlations
220(5)
5.10 Using CORAND with Non-Normal Random Variables
225(4)
5.11 More about Linear Functions of Random Variables
229(4)
5.12 Summary
233(6)
Further Readings
234(1)
Exercises
234(5)
6 Conditional Expectation
239(40)
6.1 Dependence among Random Variables
239(4)
6.2 Estimating Conditional Expectations and Standard Deviations
243(4)
6.3 The Expected-Posterior Law in a Discrete Example
247(5)
6.4 Backwards Analysis of Conditional Expectations in Tree Diagrams
252(3)
6.5 Conditional Expectation Relationships and Correlation
255(2)
6.6 Uncertainty about a Probability
257(4)
6.7 Linear Regression Models
261(4)
6.8 Confidence Intervals and Prediction Intervals
265(6)
6.9 Regression Analysis and Least Squared Errors
271(3)
6.10 Summary
274(5)
Further Readings
275(1)
Exercises
276(3)
7 Optimization of Decision Variables
279(62)
7.1 General Techniques for Using Simulation in Decision Analysis
280(10)
7.2 Strategic Use of Information
290(5)
7.3 Decision Trees
295(6)
7.4 Revenue Management
301(5)
7.5 A Simple Bidding Problem
306(3)
7.6 The Winner's Curse
309(9)
7.7 Analyzing Competitive Behavior
318(9)
7.8 Summary
327(14)
Further Readings
329(1)
Exercises
330(11)
8 Risk Sharing and Finance
341(56)
8.1 Optimal Risk Sharing in a Partnership of Individuals with Constant Risk Tolerance
342(9)
8.2 Optimality of Linear Rules in the Larger Class of Nonlinear Sharing Rules
351(4)
8.3 Risk Sharing Subject to Moral-Hazard Incentive Constraints
355(7)
8.4 Piecewise-Linear Sharing Rules with Moral Hazard
362(4)
8.5 Corporate Decision Making and Asset Pricing in the Stock Market
366(12)
8.6 Fundamental Ideas of Arbitrage Pricing Theory
378(5)
8.7 Borrowing and Lending Decisions in Credit Markets with Adverse Selection
383(6)
8.8 Summary
389(8)
Further Readings
390(1)
Exercises
391(6)
9 Dynamic Models of Growth
397(50)
9.1 Net Present Value
397(3)
9.2 Forecasting Models
400(5)
9.3 Forecasting Example: The Goeing Case
405(8)
9.4 Brownian-Motion Growth Models
413(5)
9.5 The Value of Flexibility
418(5)
9.6 Log-Optimal Investment Strategies
423(7)
9.7 Some Mathematics of Gambling
430(4)
9.8 Risk Aversion on Growth Rates
434(4)
9.9 Summary
438(9)
Further Readings
439(1)
Exercises
439(8)
10 Dynamic Models of Arrivals
447(36)
10.1 Exponential Arrival Models
447(7)
10.2 Queueing Models
454(7)
10.3 A Simple Inventory Model
461(4)
10.4 The Transmission of Disease: Fixed Population
465(5)
10.5 The Transmission of Disease: Variable Population
470(4)
10.6 Project Length and Critical Tasks
474(4)
10.7 Summary
478(5)
Further Readings
479(1)
Exercises
479(4)
11 Model Risk
483(42)
11.1 Implementation and Data Errors
483(2)
11.2 Interpretation Errors
485(1)
11.3 Model Specification Errors
485(1)
11.4 Functional Form Mis-specification
486(7)
11.5 Correlation Mis-specification
493(3)
11.6 Mis-specification due to Incomplete Information
496(2)
11.7 Volatility Mis-specification
498(12)
11.8 Mitigating Model Risk: Estimation, Validation, and Testing
510(7)
11.9 Mitigating Model Risk: The Precautionary Principle
517(2)
11.10 Summary
519(6)
Further Readings
521(1)
Exercises
521(4)
Appendix: Excel Add-Ins for Use with This Book 525(8)
References 533(2)
Index 535
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