. The firm makes different types of furniture. Let x ( x1,..., x n. If the firm produces nothing it rents out the entire space and so has a profit of

Size: px
Start display at page:

Download ". The firm makes different types of furniture. Let x ( x1,..., x n. If the firm produces nothing it rents out the entire space and so has a profit of"

Transcription

1 Joh Riley F Maimizatio with a sigle costrait F3 The Ecoomic approach - - shadow prices Suppose that a firm has a log term retal of uits of factory space The firm ca ret additioal space at a retal rate of per uit or ret out some of the uits at this same rate If the firm produces othig it rets out the etire space ad so has a profit of The firm makes differet types of furiture Let ( 1,, ) be the quatities of each type the profit of the firm from furiture sales is f( ) The space required i the productio of is g ( ) Addig the reveue from retig out some of the space (or the cost of retig additioal space, the profit of the firm is L (, ) f ( ) ( bˆ g( )) For every shadow price 0 we ca solve the followig profit maimizatio problem Ma{ L (, ) f ( ) g( )} 0 Let ( ) be a solutio of the maimizatio problem The total demad for space is b ( ) g( ( )) Cosider furiture type If 0, the the margial profit must be zero If 0, the the margial profit caot be positive Therefore we have the followig ecessary coditios for a maimum L f g (, ) ( ) ( ) 0 with equality if 0 (11) The firm s demad for space is the mappig from the shadow price to demad b ( ) If we put b o the horizotal ais ad o the vertical price, the graph is of the demad price

2 fuctio This is the price that clears the market for ay supply of space Suppose that for all b there is some demad price () b 1 Figure 3-1: Demad price fuctio for The for ay there is a demad price firm chooses the productio vector b () ˆ ˆ Thus, with this market price, the profit maimizig ad uses the leased space b ˆ g( ( b ˆ )) Cosider ay that satisfies the space costrait g() bˆ Sice is profit maimizig, it follows that f ( ) ˆ ( bˆ g( )) f ( ˆ) ˆ ( bˆ g( ˆ)) Rearragig this iequality, f ( ) f ( ˆ) ˆ ( g( ) g( ˆ)) For ay satisfyig the space costrait, the right had side is egative Therefore f ( ) f ( ˆ ) 0 1 This will ecessarily be the case if the profit fuctio is cocave 2

3 We have therefore proved the followig result Propositio: Solutio to the costraied maimizatio problem Ma{ f ( ) bˆ g( ) 0} 0 Let ( ) solve the profit maimizatio problem whe it is possible to buy or sell the iput, ie Ma{ L (, ) f ( ) ( bˆ g( ))} 0 ad let b( ) g( ( )) be the implied demad for the iput If there is a shadow price that b( ˆ ) bˆ, the ( ˆ ) solves the costraied maimizatio problem ˆ such Remark: College courses o calculus, such as at UCLA, do ot cover maimizatio problems with o-egativity ad iequality costraits If a College level tet book cosiders a costraied maimizatio problem, the costrait holds with equality ad is ay vector of real umbers, ie the problem aalyzed is the closely related but simpler optimizatio problem Ma{ f ( ) bˆ g( ) 0} We have iterpreted L(, ) f ( ) ( bˆ g( )) as the profit of a firm To a mathematicia, this is the Lagragia of the costraied maimizatio problem The price (or shadow price), is called the Lagrage multiplier Eample 1: Divisio of a firm with a budget costrait A firm ca produce output q f () z usig the iput vector z The iput price vector is r The divisio maager is give b dollars ad istructed to maimize output, q f () z without violatig the budget costrait r z r ˆ z b 1 Eample 2: The cost fuctio of the firm A firm ca produce output q f () z usig the iput vector z The iput price vector is r The 3

4 divisio maager is asked to solve for the miimum cost of producig ay output ˆq, give that the iput price vector is r Remark: Suppose that you have already solved the maimum output problem for some productio fuctio f( ) Let z * ( b, r) be the output maimizig iputs so that q b * ( ) f ( ( b, r)) This mappig is depicted below Figure 3-2: Maimum output with a fied budget Thus for ay ˆq we ca choose so that qˆ q() bˆ Thus the miimum cost is o more tha Sice q * ( b ) is strictly icreasig, it follows that for ay bˆ bˆ, the maimum output is strictly less the ˆq Thus the miimized cost is Havig solved for the mappig qˆ q() bˆ, the miimized total cost is the iverse of this fuctio 1 C( r, qˆ) q ( qˆ) 4

5 Eample 3: Utility maimizatio with Cobb-Douglas prefereces Prefereces are represeted by the followig strictly icreasig fuctio U( ) l 1 i i The budget costrait is 1 p p I Sice utility is strictly icreasig the solutio equality The Lagragia of the problem is must satisfy the budget costrait with L (, ) l ( I p ) 1 1 FOC L (, ) p 0, 1,,, with equality if 0 (12) Sice p I, there is some k for which k 0 Therefore Therefore L k (, ) pk 0 k k L p k (, ) 0, 1,, p k k Note that U L as 0 so (, ) is strictly positive for sufficietly small Thus there is o corer solutio where oe or more compoets of the solutio is zero Hece 5

6 p 1 (13) 1 1 p Therefore p (14) Substitutig ito the budget costrait, p I Therefore 1 I `1 Substitutig for i (14), p I `1 Remark: if you ca see how to prove the followig rule for 2, the you might like to use it Ratio Rule: Equal ratios are also equal to the ratio of sums of the umerators ad deomiators a If r, 1,, b ad b 0 the a b r From (13) ad the Ratio Rule 6

7 p 1 p p I Oe ca the solve immediately for the demad fuctios 7

EVEN NUMBERED EXERCISES IN CHAPTER 4

EVEN NUMBERED EXERCISES IN CHAPTER 4 Joh Riley 7 July EVEN NUMBERED EXERCISES IN CHAPTER 4 SECTION 4 Exercise 4-: Cost Fuctio of a Cobb-Douglas firm What is the cost fuctio of a firm with a Cobb-Douglas productio fuctio? Rather tha miimie

More information

Parametric Density Estimation: Maximum Likelihood Estimation

Parametric Density Estimation: Maximum Likelihood Estimation Parametric Desity stimatio: Maimum Likelihood stimatio C6 Today Itroductio to desity estimatio Maimum Likelihood stimatio Itroducto Bayesia Decisio Theory i previous lectures tells us how to desig a optimal

More information

Overlapping Generations

Overlapping Generations Eco. 53a all 996 C. Sims. troductio Overlappig Geeratios We wat to study how asset markets allow idividuals, motivated by the eed to provide icome for their retiremet years, to fiace capital accumulatio

More information

5. Best Unbiased Estimators

5. Best Unbiased Estimators Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai

More information

Problem Set 1a - Oligopoly

Problem Set 1a - Oligopoly Advaced Idustrial Ecoomics Sprig 2014 Joha Steek 6 may 2014 Problem Set 1a - Oligopoly 1 Table of Cotets 2 Price Competitio... 3 2.1 Courot Oligopoly with Homogeous Goods ad Differet Costs... 3 2.2 Bertrad

More information

Combining imperfect data, and an introduction to data assimilation Ross Bannister, NCEO, September 2010

Combining imperfect data, and an introduction to data assimilation Ross Bannister, NCEO, September 2010 Combiig imperfect data, ad a itroductio to data assimilatio Ross Baister, NCEO, September 00 rbaister@readigacuk The probability desity fuctio (PDF prob that x lies betwee x ad x + dx p (x restrictio o

More information

Introduction to Probability and Statistics Chapter 7

Introduction to Probability and Statistics Chapter 7 Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based

More information

Notes on Expected Revenue from Auctions

Notes on Expected Revenue from Auctions Notes o Epected Reveue from Auctios Professor Bergstrom These otes spell out some of the mathematical details about first ad secod price sealed bid auctios that were discussed i Thursday s lecture You

More information

Maximum Empirical Likelihood Estimation (MELE)

Maximum Empirical Likelihood Estimation (MELE) Maximum Empirical Likelihood Estimatio (MELE Natha Smooha Abstract Estimatio of Stadard Liear Model - Maximum Empirical Likelihood Estimator: Combiatio of the idea of imum likelihood method of momets,

More information

Solutions to Problem Sheet 1

Solutions to Problem Sheet 1 Solutios to Problem Sheet ) Use Theorem.4 to prove that p log for all real x 3. This is a versio of Theorem.4 with the iteger N replaced by the real x. Hit Give x 3 let N = [x], the largest iteger x. The,

More information

Monopoly vs. Competition in Light of Extraction Norms. Abstract

Monopoly vs. Competition in Light of Extraction Norms. Abstract Moopoly vs. Competitio i Light of Extractio Norms By Arkadi Koziashvili, Shmuel Nitza ad Yossef Tobol Abstract This ote demostrates that whether the market is competitive or moopolistic eed ot be the result

More information

Insurance and Production Function Xingze Wang, Ying Hsuan Lin, and Frederick Jao (2007)

Insurance and Production Function Xingze Wang, Ying Hsuan Lin, and Frederick Jao (2007) Isurace ad Productio Fuctio Xigze Wag, Yig Hsua Li, ad Frederick Jao (2007) 14.01 Priciples of Microecoomics, Fall 2007 Chia-Hui Che September 28, 2007 Lecture 10 Isurace ad Productio Fuctio Outlie 1.

More information

EC426 Class 5, Question 3: Is there a case for eliminating commodity taxation? Bianca Mulaney November 3, 2016

EC426 Class 5, Question 3: Is there a case for eliminating commodity taxation? Bianca Mulaney November 3, 2016 EC426 Class 5, Questio 3: Is there a case for elimiatig commodity taxatio? Biaca Mulaey November 3, 2016 Aswer: YES Why? Atkiso & Stiglitz: differetial commodity taxatio is ot optimal i the presece of

More information

14.30 Introduction to Statistical Methods in Economics Spring 2009

14.30 Introduction to Statistical Methods in Economics Spring 2009 MIT OpeCourseWare http://ocwmitedu 430 Itroductio to Statistical Methods i Ecoomics Sprig 009 For iformatio about citig these materials or our Terms of Use, visit: http://ocwmitedu/terms 430 Itroductio

More information

43. A 000 par value 5-year bod with 8.0% semiaual coupos was bought to yield 7.5% covertible semiaually. Determie the amout of premium amortized i the 6 th coupo paymet. (A).00 (B).08 (C).5 (D).5 (E).34

More information

FINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices?

FINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices? FINM6900 Fiace Theory How Is Asymmetric Iformatio Reflected i Asset Prices? February 3, 2012 Referece S. Grossma, O the Efficiecy of Competitive Stock Markets where Traders Have Diverse iformatio, Joural

More information

Math 312, Intro. to Real Analysis: Homework #4 Solutions

Math 312, Intro. to Real Analysis: Homework #4 Solutions Math 3, Itro. to Real Aalysis: Homework #4 Solutios Stephe G. Simpso Moday, March, 009 The assigmet cosists of Exercises 0.6, 0.8, 0.0,.,.3,.6,.0,.,. i the Ross textbook. Each problem couts 0 poits. 0.6.

More information

Sequences and Series

Sequences and Series Sequeces ad Series Matt Rosezweig Cotets Sequeces ad Series. Sequeces.................................................. Series....................................................3 Rudi Chapter 3 Exercises........................................

More information

- competitive economy with n consumption goods, and a single form of labor which is only input

- competitive economy with n consumption goods, and a single form of labor which is only input APPLIED WELFARE ECONOMICS AND POLICY ANALYSIS Commodity Taxatio Basic problem i commodity taxatio: if a social welfare fuctio is assumed, whas the choice of commodity tax rates that will maximize social

More information

Foreign Price Risk and Homogeneous Commodity Imports:

Foreign Price Risk and Homogeneous Commodity Imports: Foreig Price Risk ad Homogeeous Commodity Imports: A Focus o Soybea Demad i Chia Adrew Muhammad, Ph.D. Seior Research Ecoomist Ecoomic Research, USDA Iteratioal Agricultural Trade Research Cosortium December

More information

1 ECON4415: International Economics Problem Set 4 - Solutions

1 ECON4415: International Economics Problem Set 4 - Solutions ECON445: Iteratioal Ecoomics Problem Set 4 - Solutios. I Moopolistic competitio. Moopolistic competitio is a market form where May rms producig di eret varieties. Each rm has moopoly power over its ow

More information

2.6 Rational Functions and Their Graphs

2.6 Rational Functions and Their Graphs .6 Ratioal Fuctios ad Their Graphs Sectio.6 Notes Page Ratioal Fuctio: a fuctio with a variable i the deoiator. To fid the y-itercept for a ratioal fuctio, put i a zero for. To fid the -itercept for a

More information

NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE)

NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) NPTEL DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING IIT KANPUR QUANTITATIVE FINANCE END-TERM EXAMINATION (2015 JULY-AUG ONLINE COURSE) READ THE INSTRUCTIONS VERY CAREFULLY 1) Time duratio is 2 hours

More information

The Limit of a Sequence (Brief Summary) 1

The Limit of a Sequence (Brief Summary) 1 The Limit of a Sequece (Brief Summary). Defiitio. A real umber L is a it of a sequece of real umbers if every ope iterval cotaiig L cotais all but a fiite umber of terms of the sequece. 2. Claim. A sequece

More information

Exam 1 Spring 2015 Statistics for Applications 3/5/2015

Exam 1 Spring 2015 Statistics for Applications 3/5/2015 8.443 Exam Sprig 05 Statistics for Applicatios 3/5/05. Log Normal Distributio: A radom variable X follows a Logormal(θ, σ ) distributio if l(x) follows a Normal(θ, σ ) distributio. For the ormal radom

More information

1 Basic Growth Models

1 Basic Growth Models UCLA Aderso MGMT37B: Fudametals i Fiace Fall 015) Week #1 rofessor Eduardo Schwartz November 9, 015 Hadout writte by Sheje Hshieh 1 Basic Growth Models 1.1 Cotiuous Compoudig roof: lim 1 + i m = expi)

More information

1 The Power of Compounding

1 The Power of Compounding 1 The Power of Compoudig 1.1 Simple vs Compoud Iterest You deposit $1,000 i a bak that pays 5% iterest each year. At the ed of the year you will have eared $50. The bak seds you a check for $50 dollars.

More information

EXERCISE - BINOMIAL THEOREM

EXERCISE - BINOMIAL THEOREM BINOMIAL THOEREM / EXERCISE - BINOMIAL THEOREM LEVEL I SUBJECTIVE QUESTIONS. Expad the followig expressios ad fid the umber of term i the expasio of the expressios. (a) (x + y) 99 (b) ( + a) 9 + ( a) 9

More information

A random variable is a variable whose value is a numerical outcome of a random phenomenon.

A random variable is a variable whose value is a numerical outcome of a random phenomenon. The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss

More information

Standard Deviations for Normal Sampling Distributions are: For proportions For means _

Standard Deviations for Normal Sampling Distributions are: For proportions For means _ Sectio 9.2 Cofidece Itervals for Proportios We will lear to use a sample to say somethig about the world at large. This process (statistical iferece) is based o our uderstadig of samplig models, ad will

More information

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i The iformatio required by the mea-variace approach is substatial whe the umber of assets is large; there are mea values, variaces, ad )/2 covariaces - a total of 2 + )/2 parameters. Sigle-factor model:

More information

MA Lesson 11 Section 1.3. Solving Applied Problems with Linear Equations of one Variable

MA Lesson 11 Section 1.3. Solving Applied Problems with Linear Equations of one Variable MA 15200 Lesso 11 Sectio 1. I Solvig Applied Problems with Liear Equatios of oe Variable 1. After readig the problem, let a variable represet the ukow (or oe of the ukows). Represet ay other ukow usig

More information

Sampling Distributions and Estimation

Sampling Distributions and Estimation Cotets 40 Samplig Distributios ad Estimatio 40.1 Samplig Distributios 40. Iterval Estimatio for the Variace 13 Learig outcomes You will lear about the distributios which are created whe a populatio is

More information

10.The Zero Lower Bound in a two period economy

10.The Zero Lower Bound in a two period economy .The Zero Lower Boud i a two period ecoomy Idex:. The Zero Lower Boud i a two period ecoomy.... Itroductio.... A two period closed ecoomy with moey.....osumptio.....the IS curve...3..3the Fisher equatio...3..4the

More information

11.7 (TAYLOR SERIES) NAME: SOLUTIONS 31 July 2018

11.7 (TAYLOR SERIES) NAME: SOLUTIONS 31 July 2018 .7 (TAYLOR SERIES NAME: SOLUTIONS 3 July 08 TAYLOR SERIES ( The power series T(x f ( (c (x c is called the Taylor Series for f(x cetered at x c. If c 0, this is called a Maclauri series. ( The N-th partial

More information

Yoav Wachsman University of Hawaii

Yoav Wachsman University of Hawaii A Model of Fishig Coflicts i Foreig Fisheries Yoav Wachsma Uiversity of Hawaii A Brief History of Fisheries Exploitatio Util the later half of this cetury most marie fisheries were accessible to DWFNs

More information

x. The saver is John Riley 7 December 2016 Econ 401a Final Examination Sketch of answers 1. Choice over time Then Adding,

x. The saver is John Riley 7 December 2016 Econ 401a Final Examination Sketch of answers 1. Choice over time Then Adding, John Riley 7 December 06 Econ 40a Final Eamination Sketch of answers Choice over time (a) y s, Adding, y ( r) s y s r r y y r r (b) The slope of the life-time budget line is r When r The initial optimum

More information

Statistics for Economics & Business

Statistics for Economics & Business Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie

More information

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory Dr Maddah ENMG 64 Fiacial Eg g I 03//06 Chapter 6 Mea-Variace Portfolio Theory Sigle Period Ivestmets Typically, i a ivestmet the iitial outlay of capital is kow but the retur is ucertai A sigle-period

More information

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1 Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for

More information

Summary. Recap. Last Lecture. .1 If you know MLE of θ, can you also know MLE of τ(θ) for any function τ?

Summary. Recap. Last Lecture. .1 If you know MLE of θ, can you also know MLE of τ(θ) for any function τ? Last Lecture Biostatistics 60 - Statistical Iferece Lecture Cramer-Rao Theorem Hyu Mi Kag February 9th, 03 If you kow MLE of, ca you also kow MLE of τ() for ay fuctio τ? What are plausible ways to compare

More information

ECON 5350 Class Notes Maximum Likelihood Estimation

ECON 5350 Class Notes Maximum Likelihood Estimation ECON 5350 Class Notes Maximum Likelihood Estimatio 1 Maximum Likelihood Estimatio Example #1. Cosider the radom sample {X 1 = 0.5, X 2 = 2.0, X 3 = 10.0, X 4 = 1.5, X 5 = 7.0} geerated from a expoetial

More information

Lecture 9: The law of large numbers and central limit theorem

Lecture 9: The law of large numbers and central limit theorem Lecture 9: The law of large umbers ad cetral limit theorem Theorem.4 Let X,X 2,... be idepedet radom variables with fiite expectatios. (i) (The SLLN). If there is a costat p [,2] such that E X i p i i=

More information

Competing Auctions with Endogenous Quantities

Competing Auctions with Endogenous Quantities Competig Auctios with Edogeous Quatities Bey Moldovau, Aer Sela, Xiawe Shi December 6, 006 Abstract We study models where two sellers simultaeously decide o their discrete supply of a homogeous good. There

More information

Price Discrimination through Multi-Level Loyalty Programs

Price Discrimination through Multi-Level Loyalty Programs Price Discrimiatio through Multi-Level Loyalty Programs Serdar Sayma Murat Usma December 03 This research was supported by TÜBİTAK (The Scietific ad Techological Research Coucil of Turkey Project No: 07K069

More information

III. RESEARCH METHODS. Riau Province becomes the main area in this research on the role of pulp

III. RESEARCH METHODS. Riau Province becomes the main area in this research on the role of pulp III. RESEARCH METHODS 3.1 Research Locatio Riau Provice becomes the mai area i this research o the role of pulp ad paper idustry. The decisio o Riau Provice was supported by several facts: 1. The largest

More information

SOCIETY OF ACTUARIES FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS

SOCIETY OF ACTUARIES FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS SOCIETY OF ACTUARIES EXAM FM FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS This set of sample questios icludes those published o the iterest theory topic for use with previous versios of this examiatio.

More information

ACTUARIAL RESEARCH CLEARING HOUSE 1990 VOL. 2 INTEREST, AMORTIZATION AND SIMPLICITY. by Thomas M. Zavist, A.S.A.

ACTUARIAL RESEARCH CLEARING HOUSE 1990 VOL. 2 INTEREST, AMORTIZATION AND SIMPLICITY. by Thomas M. Zavist, A.S.A. ACTUARIAL RESEARCH CLEARING HOUSE 1990 VOL. INTEREST, AMORTIZATION AND SIMPLICITY by Thomas M. Zavist, A.S.A. 37 Iterest m Amortizatio ad Simplicity Cosider simple iterest for a momet. Suppose you have

More information

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013 18.S096 Problem Set 5 Fall 2013 Volatility Modelig Due Date: 10/29/2013 1. Sample Estimators of Diffusio Process Volatility ad Drift Let {X t } be the price of a fiacial security that follows a geometric

More information

Consumer Tracking and E cient Matching in Online Advertising Markets

Consumer Tracking and E cient Matching in Online Advertising Markets Cosumer Trackig ad E ciet Matchig i Olie Advertisig Markets Susa C. Athey, Emilio Calvao ad Joshua S. Gas December 3, 0 Oe of the advaces i our uderstadig of two-sided markets or platforms is the otio

More information

Managing Rentals with Usage-Based Loss

Managing Rentals with Usage-Based Loss Corell Uiversity School of Hotel Admiistratio The Scholarly Commos Articles ad Chapters School of Hotel Admiistratio Collectio 1-7-2015 Maagig Retals with Usage-Based Loss Vicet W. Slaugh Corell Uiversity

More information

CHAPTER 8 Estimating with Confidence

CHAPTER 8 Estimating with Confidence CHAPTER 8 Estimatig with Cofidece 8.2 Estimatig a Populatio Proportio The Practice of Statistics, 5th Editio Stares, Tabor, Yates, Moore Bedford Freema Worth Publishers Estimatig a Populatio Proportio

More information

CHAPTER 2 PRICING OF BONDS

CHAPTER 2 PRICING OF BONDS CHAPTER 2 PRICING OF BONDS CHAPTER SUARY This chapter will focus o the time value of moey ad how to calculate the price of a bod. Whe pricig a bod it is ecessary to estimate the expected cash flows ad

More information

Competing Auctions with Endogenous Quantities 1

Competing Auctions with Endogenous Quantities 1 Competig Auctios with Edogeous Quatities 1 Bey Moldovau, Aer Sela 3, Xiawe Shi 4 July 11, 007 1 We wish to tha Stefa Behriger, Drew Fudeberg, Phil Haile, two aoymous referees ad a associate editor for

More information

Chapter 10 Counterexamples to Commonly Held Assumptions on Unit Commitment and Market Power Assessment

Chapter 10 Counterexamples to Commonly Held Assumptions on Unit Commitment and Market Power Assessment Chapter 10 Couterexamples to Commoly Held Assumptios o Uit Commitmet ad Market Power Assessmet Wolfgag Gatterbauer ad Marija Ilić 10.1 Cetralized Versus Decetralized Uit Commitmet (UC) This first subsectio

More information

Cost centres and cost behaviour

Cost centres and cost behaviour 2 Cost cetres ad cost behaviour this chapter covers... I this chapter we look i more detail at how the basic priciples of costig that we explaied i the last chapter are used i a costig system. We will

More information

B = A x z

B = A x z 114 Block 3 Erdeky == Begi 6.3 ============================================================== 1 / 8 / 2008 1 Correspodig Areas uder a ormal curve ad the stadard ormal curve are equal. Below: Area B = Area

More information

Further Pure 1 Revision Topic 5: Sums of Series

Further Pure 1 Revision Topic 5: Sums of Series The OCR syllabus says that cadidates should: Further Pure Revisio Topic 5: Sums of Series Cadidates should be able to: (a) use the stadard results for Σr, Σr, Σr to fid related sums; (b) use the method

More information

Procurement, Cost Reduction, and Vertical Integration

Procurement, Cost Reduction, and Vertical Integration Procuremet, Cost Reductio, ad Vertical Itegratio Simo Loertscher Uiversity of Melboure Michael Riorda Columbia Uiversity December 9, 20 Abstract We study a two-stage model of vertical itegratio that sheds

More information

SUPPLEMENTAL MATERIAL

SUPPLEMENTAL MATERIAL A SULEMENTAL MATERIAL Theorem (Expert pseudo-regret upper boud. Let us cosider a istace of the I-SG problem ad apply the FL algorithm, where each possible profile A is a expert ad receives, at roud, a

More information

Estimating Proportions with Confidence

Estimating Proportions with Confidence Aoucemets: Discussio today is review for midterm, o credit. You may atted more tha oe discussio sectio. Brig sheets of otes ad calculator to midterm. We will provide Scatro form. Homework: (Due Wed Chapter

More information

Models of Asset Pricing

Models of Asset Pricing APPENDIX 1 TO CHAPTER 4 Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see

More information

Models of Asset Pricing

Models of Asset Pricing APPENDIX 1 TO CHAPTER4 Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see

More information

ENGINEERING ECONOMICS

ENGINEERING ECONOMICS ENGINEERING ECONOMICS Ref. Grat, Ireso & Leaveworth, "Priciples of Egieerig Ecoomy'','- Roald Press, 6th ed., New York, 1976. INTRODUCTION Choice Amogst Alteratives 1) Why do it at all? 2) Why do it ow?

More information

A New Approach to Obtain an Optimal Solution for the Assignment Problem

A New Approach to Obtain an Optimal Solution for the Assignment Problem Iteratioal Joural of Sciece ad Research (IJSR) ISSN (Olie): 231-7064 Idex Copericus Value (2013): 6.14 Impact Factor (2015): 6.31 A New Approach to Obtai a Optimal Solutio for the Assigmet Problem A. Seethalakshmy

More information

Estimating Forward Looking Distribution with the Ross Recovery Theorem

Estimating Forward Looking Distribution with the Ross Recovery Theorem roceedigs of the Asia acific Idustrial Egieerig & Maagemet Systems Coferece 5 Estimatig Forward Lookig Distributio with the Ross Recovery Theorem Takuya Kiriu Graduate School of Sciece ad Techology Keio

More information

CD Appendix AC Index Numbers

CD Appendix AC Index Numbers CD Appedix AC Idex Numbers I Chapter 20, we preseted a variety of techiques for aalyzig ad forecastig time series. This appedix is devoted to the simpler task of developig descriptive measuremets of the

More information

Course FM/2 Practice Exam 1 Solutions

Course FM/2 Practice Exam 1 Solutions Course FM/2 Practice Exam 1 Solutios Solutio 1 D Sikig fud loa The aual service paymet to the leder is the aual effective iterest rate times the loa balace: SP X 0.075 To determie the aual sikig fud paymet,

More information

SOLVING OF PORTFOLIO OPTIMIZATION PROBLEMS WITH MATHEMATICA

SOLVING OF PORTFOLIO OPTIMIZATION PROBLEMS WITH MATHEMATICA SOLVING OF PORTFOLIO OPTIMIZATION PROBLEMS WITH MATHEMATICA Iria Bolshaova BolshIV@bsu.by Belarusia State Uiversity. ABSTRACT: Optimizatio models play a icreasigly role i fiacial decisios. Portfolio imizatio

More information

STRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans

STRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans CMM Subject Support Strad: FINANCE Uit 3 Loas ad Mortgages: Text m e p STRAND: FINANCE Uit 3 Loas ad Mortgages TEXT Cotets Sectio 3.1 Aual Percetage Rate (APR) 3.2 APR for Repaymet of Loas 3.3 Credit Purchases

More information

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India July 2012

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India July 2012 Game Theory Lecture Notes By Y. Narahari Departmet of Computer Sciece ad Automatio Idia Istitute of Sciece Bagalore, Idia July 01 Chapter 4: Domiat Strategy Equilibria Note: This is a oly a draft versio,

More information

We characterize the trade-offs among firms compliance strategies in a market-based program where

We characterize the trade-offs among firms compliance strategies in a market-based program where PRODUCTION AND OPERATIONS MANAGEMENT Vol. 16, No. 6, November December 2007, pp. 763 779 iss 1059-1478 07 1606 763$1.25 POMS doi 10.3401/poms. 2007 Productio ad Operatios Maagemet Society Compliace Strategies

More information

MATH 205 HOMEWORK #1 OFFICIAL SOLUTION

MATH 205 HOMEWORK #1 OFFICIAL SOLUTION MATH 205 HOMEWORK #1 OFFICIAL SOLUTION Problem 2: Show that if there exists a ijective field homomorhism F F the char F = char F. Solutio: Let ϕ be the homomorhism, suose that char F =. Note that ϕ(1 =

More information

Models of Asset Pricing

Models of Asset Pricing 4 Appedix 1 to Chapter Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see

More information

APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES

APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES Example: Brado s Problem Brado, who is ow sixtee, would like to be a poker champio some day. At the age of twety-oe, he would

More information

Asymptotics: Consistency and Delta Method

Asymptotics: Consistency and Delta Method ad Delta Method MIT 18.655 Dr. Kempthore Sprig 2016 1 MIT 18.655 ad Delta Method Outlie Asymptotics 1 Asymptotics 2 MIT 18.655 ad Delta Method Cosistecy Asymptotics Statistical Estimatio Problem X 1,...,

More information

CAPITAL PROJECT SCREENING AND SELECTION

CAPITAL PROJECT SCREENING AND SELECTION CAPITAL PROJECT SCREEIG AD SELECTIO Before studyig the three measures of ivestmet attractiveess, we will review a simple method that is commoly used to scree capital ivestmets. Oe of the primary cocers

More information

The Balassa-Samuelson Effect and Pricing-to-Market: The Role of Strategic Complementarity

The Balassa-Samuelson Effect and Pricing-to-Market: The Role of Strategic Complementarity The Balassa-Samuelso Effect ad Pricig-to-Market: The Role of Strategic Complemetarity Eddy Bekkers Uiversity of Ber Ia Simoovska Uiversity of Califoria, Davis ad NBER We propose a ovel determiat of prices

More information

x satisfying all regularity conditions. Then

x satisfying all regularity conditions. Then AMS570.01 Practice Midterm Exam Sprig, 018 Name: ID: Sigature: Istructio: This is a close book exam. You are allowed oe-page 8x11 formula sheet (-sided). No cellphoe or calculator or computer is allowed.

More information

Mixed and Implicit Schemes Implicit Schemes. Exercise: Verify that ρ is unimodular: ρ = 1.

Mixed and Implicit Schemes Implicit Schemes. Exercise: Verify that ρ is unimodular: ρ = 1. Mixed ad Implicit Schemes 3..4 The leapfrog scheme is stable for the oscillatio equatio ad ustable for the frictio equatio. The Euler forward scheme is stable for the frictio equatio but ustable for the

More information

Course FM Practice Exam 1 Solutions

Course FM Practice Exam 1 Solutions Course FM Practice Exam 1 Solutios Solutio 1 D Sikig fud loa The aual service paymet to the leder is the aual effective iterest rate times the loa balace: SP X 0.075 To determie the aual sikig fud paymet,

More information

Exercise 1: Robinson Crusoe who is marooned on an island in the South Pacific. He can grow bananas and coconuts. If he uses

Exercise 1: Robinson Crusoe who is marooned on an island in the South Pacific. He can grow bananas and coconuts. If he uses Jon Riley F Maimization wit a single constraint F5 Eercises Eercise : Roinson Crusoe wo is marooned on an isl in te Sout Pacific He can grow ananas coconuts If e uses z acres to produce ananas z acres

More information

Unbiased estimators Estimators

Unbiased estimators Estimators 19 Ubiased estimators I Chapter 17 we saw that a dataset ca be modeled as a realizatio of a radom sample from a probability distributio ad that quatities of iterest correspod to features of the model distributio.

More information

Calculation of the Annual Equivalent Rate (AER)

Calculation of the Annual Equivalent Rate (AER) Appedix to Code of Coduct for the Advertisig of Iterest Bearig Accouts. (31/1/0) Calculatio of the Aual Equivalet Rate (AER) a) The most geeral case of the calculatio is the rate of iterest which, if applied

More information

Solution to Tutorial 6

Solution to Tutorial 6 Solutio to Tutorial 6 2012/2013 Semester I MA4264 Game Theory Tutor: Xiag Su October 12, 2012 1 Review Static game of icomplete iformatio The ormal-form represetatio of a -player static Bayesia game: {A

More information

Marking Estimation of Petri Nets based on Partial Observation

Marking Estimation of Petri Nets based on Partial Observation Markig Estimatio of Petri Nets based o Partial Observatio Alessadro Giua ( ), Jorge Júlvez ( ) 1, Carla Seatzu ( ) ( ) Dip. di Igegeria Elettrica ed Elettroica, Uiversità di Cagliari, Italy {giua,seatzu}@diee.uica.it

More information

Appendix 1 to Chapter 5

Appendix 1 to Chapter 5 Appedix 1 to Chapter 5 Models of Asset Pricig I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy a asset, we are

More information

between 1 and 100. The teacher expected this task to take Guass several minutes to an hour to keep him busy but

between 1 and 100. The teacher expected this task to take Guass several minutes to an hour to keep him busy but Sec 5.8 Sec 6.2 Mathematical Modelig (Arithmetic & Geometric Series) Name: Carl Friedrich Gauss is probably oe of the most oted complete mathematicias i history. As the story goes, he was potetially recogiized

More information

Pushing and Pulling Environmental Innovation: R&D Subsidies and Carbon Taxes

Pushing and Pulling Environmental Innovation: R&D Subsidies and Carbon Taxes Pushig ad Pullig Evirometal Iovatio: R&D Subsidies ad Carbo Taxes Matthew S. Clacy ad GiaCarlo Moschii * Abstract We use a ovel modelig framework that icorporates free etry ito the R&D sector ad ucertaity

More information

of Asset Pricing R e = expected return

of Asset Pricing R e = expected return Appedix 1 to Chapter 5 Models of Asset Pricig EXPECTED RETURN I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy

More information

An Incentive Effect of Multiple Sourcing *

An Incentive Effect of Multiple Sourcing * cetive Effect of Multiple Sourcig * Staley aima Serguei Netessie 1 The Wharto School Uiversity of Pesylvaia Philadelphia, P 19104 July 004 bstract: We cosider a supply chai with oe maufacturer who assembles

More information

INTERVAL GAMES. and player 2 selects 1, then player 2 would give player 1 a payoff of, 1) = 0.

INTERVAL GAMES. and player 2 selects 1, then player 2 would give player 1 a payoff of, 1) = 0. INTERVAL GAMES ANTHONY MENDES Let I ad I 2 be itervals of real umbers. A iterval game is played i this way: player secretly selects x I ad player 2 secretly ad idepedetly selects y I 2. After x ad y are

More information

CAUCHY'S FORMULA AND EIGENVAULES (PRINCIPAL STRESSES) IN 3-D

CAUCHY'S FORMULA AND EIGENVAULES (PRINCIPAL STRESSES) IN 3-D GG303 Lecture 19 11/5/0 1 CAUCHY'S FRMULA AN EIGENVAULES (PRINCIPAL STRESSES) IN 3- I II Mai Topics A Cauchy s formula Pricipal stresses (eigevectors ad eigevalues) Cauchy's formula A Relates tractio vector

More information

Profit Taxation, Monopolistic Competition and International Relocation of Firms

Profit Taxation, Monopolistic Competition and International Relocation of Firms 1 Profit Taxatio, Moopolistic Competitio ad Iteratioal Relocatio of Firms Wataru Johdo This paper presets a two-coutry moopolistic competitio trade model to aalyze how the profit taxatio determies the

More information

Chapter Four Learning Objectives Valuing Monetary Payments Now and in the Future

Chapter Four Learning Objectives Valuing Monetary Payments Now and in the Future Chapter Four Future Value, Preset Value, ad Iterest Rates Chapter 4 Learig Objectives Develop a uderstadig of 1. Time ad the value of paymets 2. Preset value versus future value 3. Nomial versus real iterest

More information

Chapter Six. Bond Prices 1/15/2018. Chapter 4, Part 2 Bonds, Bond Prices, Interest Rates and Holding Period Return.

Chapter Six. Bond Prices 1/15/2018. Chapter 4, Part 2 Bonds, Bond Prices, Interest Rates and Holding Period Return. Chapter Six Chapter 4, Part Bods, Bod Prices, Iterest Rates ad Holdig Period Retur Bod Prices 1. Zero-coupo or discout bod Promise a sigle paymet o a future date Example: Treasury bill. Coupo bod periodic

More information

Topic-7. Large Sample Estimation

Topic-7. Large Sample Estimation Topic-7 Large Sample Estimatio TYPES OF INFERENCE Ò Estimatio: É Estimatig or predictig the value of the parameter É What is (are) the most likely values of m or p? Ò Hypothesis Testig: É Decidig about

More information

RESOURCE ALLOCATION AND A FITTED PRODUCTION FUNCTION

RESOURCE ALLOCATION AND A FITTED PRODUCTION FUNCTION RESOURCE ALLOCATION AND A FITTED PRODUCTION FUNCTION J. H. DULOY* I. Itroductio Over a cosiderable period of time, ecoometricias have bee attemptig to estimate the margial productivities of resources by

More information

Neighboring Optimal Solution for Fuzzy Travelling Salesman Problem

Neighboring Optimal Solution for Fuzzy Travelling Salesman Problem Iteratioal Joural of Egieerig Research ad Geeral Sciece Volume 2, Issue 4, Jue-July, 2014 Neighborig Optimal Solutio for Fuzzy Travellig Salesma Problem D. Stephe Digar 1, K. Thiripura Sudari 2 1 Research

More information

1 Itroductio South Tyrol i Norther Italy is the most importat apple growig regio i Europe. Apple growers have the possibility to sell their products t

1 Itroductio South Tyrol i Norther Italy is the most importat apple growig regio i Europe. Apple growers have the possibility to sell their products t Quality eforcemet as a public good (prelimiary ad icomplete) Rudolf Kerschbamer, Muriel Niederle ad Josef Perktold Uiversity of Viea, Harvard Uiversity, Uiversity of Chicago December 1999 Abstract A competitive

More information