MONOPOLISTIC COMPETITION MODEL

Similar documents
Analyzing Control Structures

CHAPTER 4 RADICAL EXPRESSIONS

ANSWER KEY 7 GAME THEORY, ECON 395

Mu Sequences/Series Solutions National Convention 2014

Chapter 9 Jordan Block Matrices

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005

1 Solution to Problem 6.40

(This summarizes what you basically need to know about joint distributions in this course.)

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process

Lecture 3. Sampling, sampling distributions, and parameter estimation

5 Short Proofs of Simplified Stirling s Approximation

Third handout: On the Gini Index

bg 0. 2 Cournot Oligopoly The Cournot Model q i

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

A Mean- maximum Deviation Portfolio Optimization Model

STATISTICS 13. Lecture 5 Apr 7, 2010

STK4011 and STK9011 Autumn 2016

UNIVERSITY OF EAST ANGLIA. Main Series UG Examination

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

Design maintenanceand reliability of engineering systems: a probability based approach

Arithmetic Mean and Geometric Mean

Lecture Notes Types of economic variables

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

New Trade Theory (1979)

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

Correlation and Regression Analysis

MA 524 Homework 6 Solutions

CHAPTER 2. = y ˆ β x (.1022) So we can write

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Chapter 14 Logistic Regression Models

Problems and Solutions

Spring Ammar Abu-Hudrouss Islamic University Gaza

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Ideal multigrades with trigonometric coefficients

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

n -dimensional vectors follow naturally from the one

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

Lecture 02: Bounding tail distributions of a random variable

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

Multiple Choice Test. Chapter Adequacy of Models for Regression

Tail Factor Convergence in Sherman s Inverse Power Curve Loss Development Factor Model

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

Math 10 Discrete Mathematics

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

LINEAR REGRESSION ANALYSIS

Lecture 8: Linear Regression

The Mathematical Appendix

QR Factorization and Singular Value Decomposition COS 323

Unsupervised Learning and Other Neural Networks

Chapter 1 Counting Methods

PTAS for Bin-Packing

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

9.1 Introduction to the probit and logit models

Beam Warming Second-Order Upwind Method

EECE 301 Signals & Systems

Chapter 3 Sampling For Proportions and Percentages

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010

Lecture 2: Linear Least Squares Regression

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

A tighter lower bound on the circuit size of the hardest Boolean functions

Chapter 5 Properties of a Random Sample

2. Independence and Bernoulli Trials

: At least two means differ SST

ENGI 3423 Simple Linear Regression Page 12-01

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

6.867 Machine Learning

7.0 Equality Contraints: Lagrange Multipliers

Complex Numbers Primer

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

ESS Line Fitting

Brander and Lewis (1986) Link the relationship between financial and product sides of a firm.

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

Chapter 4 Multiple Random Variables

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

The equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.

Evaluating Polynomials

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Sampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study

8.1 Hashing Algorithms

Econ 388 R. Butler 2016 rev Lecture 5 Multivariate 2 I. Partitioned Regression and Partial Regression Table 1: Projections everywhere

Transcription:

MONOPOLISTIC COMPETITION MODEL Key gredets Cosumer utlty: log (/ ) log (taste for varety of dfferetated goods) Produto of dfferetated produts: y (/ b) max[ f, ] (reasg returs/fxed osts) Assume that good, the agrultural good, s produed wth perfet ompetto ad the ostat returs to sale produto futo y, but that there are maufatured goods that are produed wth moopolst ompetto ad the reasg returs to sale produto futo spefed above The represetatve osumer solves max log (/ ) log s t p p w u/ p u/ p (MRS equals pre rato) / p / p Produer of good solves the osumer s problem to fd the dret demad futo: p (multplyg by ) p p p (summg over )

p p (smplfyg) p w (from prevous equato ad budget ostrat) w p Idret demad futo p w p = Profts of frm : py wby wf (reveue-varable osts -fxed osts) We suppose that the frm hooses ts output y to maxmze ts profts, assumg that the outputs of all other frms are ostat ad that pres wll adust to lear the markets of eah good (Ths s the Courot ompetto assumpto) To maxmze profts, the frm sets MR MC: We set y the dret demad futo (ths s the assumpto that the pre of good adusts to lear the market for good ) ad plug ths futo to expresso for profts: w y y wby wf y To maxmze profts, the frms sets the frst dervatve of ths expresso equal to, that s, MR MC : Set w as umerare w ( y ) y y y wb ( y )

Se frms are symmetr, we kow that there s a equlbrum whh y y f y : The profts of typal frm are ( y ) y y y b ( y ) ( ) b y ( ) y b y b p p y y ( ) ( ) pyby f f We assume that there s free etry/ext utl profts equal zero: f ( ) The profts of typal frm are ( ) pyby f f We assume that there s free etry/ext utl profts equal zero: f ( ) ( ) ( ) 4( )( f ) 4 f 3

Equlbrum A equlbrum of the moopolst ompetto model s the umber of maufaturg frm ˆ, a pre ˆp for the agrultural good, a pre p ˆ for eah maufaturg frm that operates at a postve level, a wage rate ŵ, a osumpto pla ˆ, ˆ ˆ ˆ,,, ˆ, produto plas, ŷ, ˆ for the agrultural good ad y ˆ, ˆ for eah maufaturg frm that operates at a postve level suh that Gve pˆ, pˆ, pˆ,, p ˆ ˆ, ad ŵ, the osumer hooses ˆ, ˆ, ˆ,, ˆ ˆ to solve pˆ wˆ, f yˆ ˆ max log (/ ) log s t p ˆ p ˆ wˆ Gve the dret demad futo p (,,,, ) that omes from solvg the represetatve osumer s utlty maxmzato problem, frm hooses y ˆ to solve max p ( yˆ,, y,, yˆ ) y wby ˆ wf ˆ ˆ pˆ pˆ ( yˆ,, yˆ,, yˆ ) py ˆ ˆ ˆ ˆ ˆ, f ˆ wby wf y where pˆ ˆ ( ˆ ˆ ˆ p y,, y,, y) ŷ ˆ yˆ (/ b) max[ ˆ f, ],,,, ˆ ˆ yˆ,,,,, ˆ ˆ ˆ ˆ 4

Numeral example b, f, /, 49 45 (45) 4(45)(4) 8 7 y 5, p 3333 p w, y 45 Utlty: / log 45+log(7(5) ) 7496 Homogeous of degree oe represetato of utlty (a real ome dex): / exp[(/ )(log 45+log(7(5) ))] exp[(/ )(7496)] 444 5

A tegral umber of frms? There s a problem wth our oept of equlbrum f the umber of frms, ˆ, does ot tur out to be a teger Suppose, for example, that The, whe we solve b, f, /, 49 45 (45) 4(45)(4), 8 we obta ˆ 634 How do we terpret ths soluto? There are two approahes that we ould take: We ould restrt ˆ to be a teger, ad let t be the largest umber of frms for whh profts are oegatve I ths ase, however, there a be postve profts equlbrum These profts eed to be eared by someoe If we gve them to the represetatve osumer, the the osumer s budget ostrat beomes p ˆ p ˆ ˆ ˆ w ˆ where ˆ are profts Everythg beomes a more omplated eve ths smple model wth oly oe market wth moopolst ompetto Thgs beome muh more omplated appled models wth may suh markets We ould thk of ˆ as beg a teger up utl we ompute the umber of frms, at whh we pot we smply alulate a real umber Ths s the approah that eoomsts typally use applyg ths sort of model 6

Reterpretg the model as a model of teratoal trade We a reterpret ths model as a model of teratoal trade amog outres that are detal exept for ther szes as measured by ther labor fores, Cosder the umeral example whh b, f, / ad there are two outres, oe whh 44 ad the other whh 49 (We a thk of these outres as beg the Uted States ad Caada respetvely) I the tegrated equlbrum of the world eoomy p w 45 (45) 4(45)(4) 634 8 ( ) 63449 y 9367 b 634 y b 634 p 37 y y ( ) 634 49 y 45 p To alulate osumpto of eah varety eah outry, we ust dvde the world produto of the varety y proportoally I outry, for example, 44 y 9367 743 49 We also dvde the produto ad the osumpto of the agrultural good proportoally: ˆ ˆ (44/ 49) ˆ (44/ 49)634 568 (49 / 49) ˆ (49 / 49)634 634, ad yˆ (44/ 49) yˆ (44/ 49)45 5 yˆ (49 / 49) yˆ (49 / 49)45 45 (Strtly speakg, there s othg ths model that ps dow the loato of produto of the agrultural good We are alulatg a symmetr equlbrum) 7

Trade Equlbrum ˆ ˆ p p wˆ ˆ y y outry 568 37 5 743 5 5 9367 39367 outry 634 37 45 937 45 45 9367 39367 ˆ ˆ Utlty: Real ome dex: / uˆ log 5 log 634(743) 433 / uˆ log 45 log 634(937) 989 uˆ / e 5 uˆ / e 3557 (Note that, ot surprsgly, the real ome outry s 9 tmes greater tha that outry ) Gas from Trade To alulate the gas from trade, we a ompute the autarky equlbra for both outres (We have already alulated ths equlbrum for outry Autarky Equlbrum ˆ ˆ p p wˆ ˆ y y outry 5675 363 5 93 5 5 93 393 outry 7 3333 45 5 45 45 5 35 ˆ ˆ Utlty: Real ome dex: / uˆ log 5 log 5675(93) 48 / uˆ log 45 log 7(5) 7496 uˆ / e 5748 uˆ / e 444 The smaller outry, outry, has the most to ga from trade: I outry, real ome goes up by 54 peret (5 /5748 54) I outry, real ome goes up by 94 peret (3557 / 444 3944 ) 8