Automobile Prices in Market Equilibrium. Berry, Pakes and Levinsohn
|
|
- Stuart Glenn
- 5 years ago
- Views:
Transcription
1 Automobie Prices in Market Equiibrium Berry, Pakes and Levinsohn Empirica Anaysis of demand and suppy in a differentiated products market: equiibrium in the U.S. automobie market. Oigopoistic Differentiated Goods Market Price is determined from the oigopoistic market equiibrium. About 00 different automobie modes per year. Each mode has different observed characteristics: size, fue efficiency, etc. And aso each mode has unobserved characteristics such as brand image, reiabiity, etc. Data avaiabiity: some product characteristics of different modes (size, fue efficiency, etc.), aggregate saes share, aggregate consumer eve data (distribution of income, etc.) are avaiabe. No individua eve consumer data avaiabe. No cost information avaiabe. Hedonic approach: An automobie is a bunde of severa inherent hedonic characteristics that are observabe (size, mieage, etc) and unobserved (brand image, etc). Reduce 00 different modes into a bunde of severa characteristics.
2 Question that can be addressed: What is the own and cross price easticity of different modes? How much do consumers vaue each vehice characteristics. What is the impact of vountary export restraint (VET) on the U.S. automobie industry? What is the expected share of a new mode that has size fue efficiency y, etc? Mode Consumers Consumer i s utiity from purchasing a mode: U ς p, x, ξ, θ ( ) i, ς i: unobserved consumer characteristics. Distribution is known (income, famiy size, etc). p : price of a mode. x : observed characteristics of mode ξ : unobserved characteristics of mode θ : parameters of the utiity function.
3 Consumer i chooses mode if and ony if ( ς p, x, ξ, θ ) U ( ς, p, x, ξ, θ ) U for a i, i r r r r = 0,,..., J, r r = 0 means the consumer did not buy a car. A : set of consumer characteristics that buys mode : utiity of mode is higher than the utiity of any other modes incuding no purchase. (2.) A = ς : U ς, p, x, ξ, θ U ς, p, x, ξ, θ ; r = 0,.., J { ( ) ( ) } Consumers whose random unobserved characteristic ς is such that she gets the highest utiity by choosing mode. Let P 0 be the distribution of consumer s unobserved characteristics (or random utiity shock) ς. Then, Market share of mode : measure of consumers who buy mode (2.2) s ( p x, ; θ ), ξ = ς P0 ( dς ) A r r r
4 probabiity (or share) of the consumers for whom the utiity of purchasing mode is highest. Integrate over unobserved characteristics. Demand: Ms ( p, ξ;θ ) M :Number of consumers. Firms: F :modes for a singe firm. Profit of firm from a its modes ( ) π = p mc Ms p, ξ; (3.2) ( ) ( θ ) r F Fat margina cost (3.) n( mcr ) = w r γ + r r r ω w r:observabe cost characteristics ω :unobservabe cost characteristics r we assume that E[ ω w] = 0 i.e. the observabe demand and cost characteristics is exogenous. r r r
5 Bertrand Nash equiibrium under the differentiated goods economy. Given the prices of modes of other firms, pk : k Fr, take derivative of the profit function w.r.t. price and set it to zero. F.O.C. (3.3) ( p, ξ; θ ) sk s ( p, ξ, θ ) + ( pk mck ) = 0 p k F r
6 Moment conditions for estimation: What is in the data? Ony aggregate information: Market share of each brand Price and other mode characteristics, no cost info. Distribution of consumer characteristics (income, famiy size, etc). Demand Side: Suppose individua i s utiity for car mode has the foowing specification: u = u x, p, θ + ξ + ε (6.) i ( ) i x : mode specific observed characteristics (trunk space, mieage, horsepower, etc.) p : price of the mode ξ :mode specific unobserved characteristics (brand image, etc): assumed to be mean zero distributed conditiona on w (observed product characteristics or cost variabes). That is, [ w] = 0 E ξ i ε : individua specific unobserved taste component: assumed to be i.i.d. extreme vaue distributed.
7 The utiity of not buying a car is normaized to be 0. Then, the set of consumers who choose mode :Mode gives the consumer highest utiity. { ε i + ξ + εi (2.) A = : u( p, x ; θ ) ( p, x ; θ ) + ξ + ε, r = 0,.., J r } u, r r r Assume the error term ε is i.i.d. extremey vaue distributed. Share: the probabiity of consumers who coose mode : ir s p, ξ ; θ df( ε) (2.2) ( ) ε A [ u( p, x ; θ ) + ξ ] exp = J + exp + = [ u( p, x ; θ ) ξ ] The share of not buying a car: s ( p, ξ ; θ ) 0 df( ε ) ε A0 = + J [ u(, ; θ ) ξ ] exp p x + = for =,..., J
8 Then, [ s ( )] p, ξ ; θ n[ s ( p,, ξ; θ )] n 0 x ( p, x ) ξ = u ;θ + Now, we know that [ w] = 0 E ξ E. Hence, [ ξ ( w) ] = E{ E[ ξ w] ( w) } = 0 If we want our mode parameter to be cose to the true parameter vaue, then the parameter shoud satisfy the foowing sampe anaog to the above equation. J J = ( x, w ) ξ = 0 That is, the parameters shoud be chosen that the foowing equation is as cose to zero as possibe. J [ n s n s u( p, x ; θ )] ( x w ) J = 0,
9 Steps to construct demand side moments: Step : From the data, derive the share of mode s = No.of mode sod Tota sampe no. From the data, derive the no purchase share s = 0 No.of no purchase Tota sampe no. Step 2: Given p, x from the data, and given parameters θ, derive the sampe moment J [ n s n s u( p, x ; θ )] ( x w ) J = 0, Given the parameters, you can get back ξ by using ( p, x ) ξ = s n s u ;θ n 0
10 Suppy Side: margina cost equation n( mc ) = γ + ω (3.) w w :observabe cost characteristics: size, engine power, etc. ω :unobservabe cost characteristics: error term of the regression γ : parameters to be estimated. The probem about estimating the above regression equation is that there is no data on margina cost mc. If margina cost were observabe, estimating the above equation woud be simpe OLS or 2SLS. We recover the margina cost from the mode, i.e., from the First order condition of the firm s profit maximizing probem. (3.3) ( p, ξ; θ ) sk s + ( pk mck ) = 0 p k Fr s : market share of mode : can be cacuated from the data on automobie purchase. p k : price of mode k : get from the data. k
11 Aso, from the consumer s probem, given p,, and the parameter θ, we know that s ( p, ξ ; θ ) [ u( p, x ; θ ) + ξ ] exp = J + exp + = [ u( p, x ; θ ) ξ ] By taking derivative of the above equation with respect to Together, we get mc = p Δ p, we can derive s s k ( p, ξ; θ ) p x Δ k s = p k if mode k and Δ k = 0 otherwise. are produced by the same firm. Then, we get the foowing inear regression equation. [ ] ξ s = γ + ω (3.6) p Δ( p,, θ ) w We know that E[ ω w] = 0 k. Hence,
12 E [ ω ( w) ] = E{ E[ ω w] ( w) } = 0 If we want our mode parameter to be cose to the true parameter vaue, then the parameter shoud satisfy the foowing sampe anaog to the above equation. J J = ω ( x, w ) = 0 θ, γ shoud be chosen so That is, the parameters that the foowing equation is as cose to zero as possibe. J {[ ( ) ] p Δ p, ξ, θ s w γ } ( x, w ) J = In sum, parameters θ, γ shoud be chosen so that the foowing two sets of moments are made as cose to zero as possibe.
13 Generaized Method of Moments (GMM) estimation. Moments from consumer choice probem m ( p,,θ,γ ) w J [ n s n s u( p, x ; θ )] ( x w ) J = 0, Moments from firms profit maximization m ( p,,θ,γ ) = 2 w J {[ ( ) ] p Δ p, ξ, θ s w γ } ( x, w ) J = θ, γ to make those moments for We choose consumer choice probem and profit maximization probem as cose to zero as possibe.
14 Further Issues on Estimation. ) Notice that the price is not in the set of instruments. Instruments are ony ( x, w) : observed consumer and producer characteristics. The reason is that the prices are endogenous. ( p, ξ; θ ) sk s ( p, ξ, θ ) + ( pk mck ) = 0 p where mc = w + ω k Fr That is, modes that have high margina cost due to high ω is more ikey to set a higher price. Hence, the error term ω and the price p is ikey to be positivey correated. This is caed the simutaneity probem in estimating equiibrium modes. Other instruments that are used for product is : firm s average, competitor s, etc. w w 2) Some specification issues u = u x, p, θ + ξ + ε (6.) i ( ) i assume that u ( x, p, θ ) = x β p α Then, the cross price easticity is
15 s ( p, θ ) p s p = s p u ( x, p ; θ ) p = s p α That is, the cross price easticity of the price of mode to a mode ony depends on the mode price and mode s share. Yugo and Benz have the same market share. Increase in BMW price has the same effect on the market share of Yugo and Benz. Same increase in market share for Benz and Yugo, which is unreasonabe. Consumers who choose Benz have a preference over a arger car (because of arger famiy size, higher income, etc.). They woud be more interested in BMW. Fina utiity specification: (6.4a) u it = α n y p + x β + ξ σ x v + ε (6. ( it t ) t t + k kt ik it u 0 = α n y + ξ + σ v + ε 0 4b) it ( it ) 0t 0 i0 i t x t,,.., J Suppose = is the size of a mode. Then, for consumers who have high v i, size of a car is very important for her utiity. She is more k
16 ikey to purchase cars with arger size (such as Benz) than the others. For her, Benz and BMW are in a simiar group and more attractive than Yugo. For peope ike her, increase in BMW price wi increase Benz saes more than Yugo saes. Suppose that x 2 t is the mieage per gaon for mode. Consumers who buy Benz: ikey to have high v i (ove for size). Likey to buy more Benz when BMW price increases. Likey to not react to Fiat price increase. v i2 Consumers who buy Yugo. Likey to have high (care for fue efficiency). Likey to buy more Yugo when Fiat price increases. Likey to not react to BMW price increase. Cross easticities are high for simiar car modes and ow for different car modes, which is more reaistic.
17 Simuators for market share Given the parameters and the quaity ξ t the market share can be simuated in the foowing steps. Step. Draw from income distribution y mt Draw v mk, k = 0,..., K from standard norma distribution. Cacuate the utiity component without the error term ε u + σ x (6.4a) mt ( mt mt ) t t k k u kt = α n y p + x β + ξ v mk = α y + ξ + σ v (6.4b) mt0 n( mt ) 0t 0 m0 and the shares for simuation sampe m: s m ( p, ξ ; θ ) [ u + ξ ] exp mt = J + exp + = 0 [ u ξ ] mt
18 Step 2: Repeat step average. m =,..., M times and derive the s M ( p, ξ, θ, P) = s ( p, ξ, θ ) M m= If the simuation size M is arge enough, this simuated share shoud be very cose to the actua share. How do I get the quaity parameter ξ t for each mode? Choose them so that the computed share s ( p, ξ, θ, P) is equa (in practice, cose to) the actua share m s = No.of mode sod Tota sampe no. The probem is that now, it is not so easy to recover the quaity measure ξ from the observed og shares any more. This is because now, the shares are a compex noninear functions of ξ. But it turns out the agorithm of deriving the ξ s given the shares is a contraction mapping. So, given the observed shares and the parmeters, the
19 quaity measure for each firm is unique up to a constant, and can be obtained reativey easiy. Data: Product characteristics: Automobie News Market Data Book: no. of cyinders, no. of doors, weight, engine dispacement, horsepower, ength, width, EPA MPG rating, front whee drive?, automatic transimission, power steering, air conditioning. Price: ist retai price for the base mode (983$) Additiona data: Gasoine price March Current Popuation Survey: income distribution of consumers. Consumer reports reiabiity ratings. Trends from 97 to 990 No. of modes: increases from 72 (974) to 50 (988). Saes per mode: decrease Price: fat unti 979, rises 50% during 80 s More fue efficient. Increase in Japanese cars market share, European shares constant.
20
21 Estimation Resuts Logit Estimation Dependent variabe: n( s ) n( s ) Variabe OLS Logit IV Logit Const (0.253) (0.493) HP/Weight -0.2 (0.277).965 (0.909) Air Cond (0.073).289 (0.248) MP$ (0.043) (0.086) Size 2.34 (0.25) (0.247) Price (0.004) (0.23) No. Ineastic Demands R sq n.a. Price easticity: ( s ) p 494 modes have easticity ess than. Not consistent with profit maximization. Possibe correation between price and unobserved product attributes. IV: size, No. of own and riva firm s products, etc. Ony 22 products that are ineastic. α 0
22 Singe easticity parameter is unreaistic because it impies that a modes have about the same markup. Aso, higher share modes have higher markup and ower share modes have ower markup, which is unreaistic.
23 Resuts from the fu mode Demand side parameters Mean β ' s Constant HP/weight Air MP$ Size Std. dev. Constant σ β Cost side parameters HP/Weight Air MP$ Size n( y p) Constant Ln(HP/Weight) Air Ln(MPG) Ln(size) Trend Negative MPG coefficient: popuar cars were the ones with ow fue efficiency.
24 The easticity of demand w.r.t. MP$ decines monotonicay with the car s MP$ rating. Individuas who purchase BMW, Lexus are not concerned with fue efficiency. Consumers who purchase the smaest cars vaue more on increased acceeration. A 227 modes in the sampe have easticity higher than. The most easticay demanded products are those that are in the most crowded market segments, the compact and subcompacts. Cross price easticities are arge for cars with simiar price characteristics. Markup: rises monotonicay with price. owest markup cars: Mazda, Sentra and Escort highest markup: Lexus and BMW
25
26
27
Demand in Leisure Markets
Demand in Leisure Markets An Empirica Anaysis of Time Aocation Shomi Pariat Ph.D Candidate Eitan Bergas Schoo of Economics Te Aviv University Motivation Leisure activities matter 35% of waking time 9.7%
More information(1 ) = 1 for some 2 (0; 1); (1 + ) = 0 for some > 0:
Answers, na. Economics 4 Fa, 2009. Christiano.. The typica househod can engage in two types of activities producing current output and studying at home. Athough time spent on studying at home sacrices
More informationBayesian Learning. You hear a which which could equally be Thanks or Tanks, which would you go with?
Bayesian Learning A powerfu and growing approach in machine earning We use it in our own decision making a the time You hear a which which coud equay be Thanks or Tanks, which woud you go with? Combine
More informationLecture 10 Demand for Autos (BLP) Bronwyn H. Hall Economics 220C, UC Berkeley Spring 2005
Lecture 10 Demand for Autos (BLP) Bronwyn H. Hall Economics 220C, UC Berkeley Spring 2005 Outline BLP Spring 2005 Economics 220C 2 Why autos? Important industry studies of price indices and new goods (Court,
More informationFRST Multivariate Statistics. Multivariate Discriminant Analysis (MDA)
1 FRST 531 -- Mutivariate Statistics Mutivariate Discriminant Anaysis (MDA) Purpose: 1. To predict which group (Y) an observation beongs to based on the characteristics of p predictor (X) variabes, using
More informationCS229 Lecture notes. Andrew Ng
CS229 Lecture notes Andrew Ng Part IX The EM agorithm In the previous set of notes, we taked about the EM agorithm as appied to fitting a mixture of Gaussians. In this set of notes, we give a broader view
More informationA. Distribution of the test statistic
A. Distribution of the test statistic In the sequentia test, we first compute the test statistic from a mini-batch of size m. If a decision cannot be made with this statistic, we keep increasing the mini-batch
More information(This is a sample cover image for this issue. The actual cover is not yet available at this time.)
(This is a sampe cover image for this issue The actua cover is not yet avaiabe at this time) This artice appeared in a journa pubished by Esevier The attached copy is furnished to the author for interna
More informationChapter 7 PRODUCTION FUNCTIONS. Copyright 2005 by South-Western, a division of Thomson Learning. All rights reserved.
Chapter 7 PRODUCTION FUNCTIONS Copyright 2005 by South-Western, a division of Thomson Learning. A rights reserved. 1 Production Function The firm s production function for a particuar good (q) shows the
More informationDo Schools Matter for High Math Achievement? Evidence from the American Mathematics Competitions Glenn Ellison and Ashley Swanson Online Appendix
VOL. NO. DO SCHOOLS MATTER FOR HIGH MATH ACHIEVEMENT? 43 Do Schoos Matter for High Math Achievement? Evidence from the American Mathematics Competitions Genn Eison and Ashey Swanson Onine Appendix Appendix
More information6.434J/16.391J Statistics for Engineers and Scientists May 4 MIT, Spring 2006 Handout #17. Solution 7
6.434J/16.391J Statistics for Engineers and Scientists May 4 MIT, Spring 2006 Handout #17 Soution 7 Probem 1: Generating Random Variabes Each part of this probem requires impementation in MATLAB. For the
More informationA proposed nonparametric mixture density estimation using B-spline functions
A proposed nonparametric mixture density estimation using B-spine functions Atizez Hadrich a,b, Mourad Zribi a, Afif Masmoudi b a Laboratoire d Informatique Signa et Image de a Côte d Opae (LISIC-EA 4491),
More informationA Brief Introduction to Markov Chains and Hidden Markov Models
A Brief Introduction to Markov Chains and Hidden Markov Modes Aen B MacKenzie Notes for December 1, 3, &8, 2015 Discrete-Time Markov Chains You may reca that when we first introduced random processes,
More informationTAM 212 Worksheet 9: Cornering and banked turns
Name: Group members: TAM 212 Worksheet 9: Cornering and banked turns The aim of this worksheet is to understand how vehices drive around curves, how sipping and roing imit the maximum speed, and how banking
More informationMONTE CARLO SIMULATIONS
MONTE CARLO SIMULATIONS Current physics research 1) Theoretica 2) Experimenta 3) Computationa Monte Caro (MC) Method (1953) used to study 1) Discrete spin systems 2) Fuids 3) Poymers, membranes, soft matter
More informationDemand in Differentiated-Product Markets (part 2)
Demand in Differentiated-Product Markets (part 2) Spring 2009 1 Berry (1994): Estimating discrete-choice models of product differentiation Methodology for estimating differentiated-product discrete-choice
More informationNuclear Size and Density
Nucear Size and Density How does the imited range of the nucear force affect the size and density of the nucei? Assume a Vecro ba mode, each having radius r, voume V = 4/3π r 3. Then the voume of the entire
More informationOnline Appendix. to Add-on Policies under Vertical Differentiation: Why Do Luxury Hotels Charge for Internet While Economy Hotels Do Not?
Onine Appendix to Add-on Poicies under Vertica Differentiation: Wy Do Luxury Hotes Carge for Internet Wie Economy Hotes Do Not? Song Lin Department of Marketing, Hong Kong University of Science and Tecnoogy
More informationGauss Law. 2. Gauss s Law: connects charge and field 3. Applications of Gauss s Law
Gauss Law 1. Review on 1) Couomb s Law (charge and force) 2) Eectric Fied (fied and force) 2. Gauss s Law: connects charge and fied 3. Appications of Gauss s Law Couomb s Law and Eectric Fied Couomb s
More informationEmpirical Industrial Organization (ECO 310) University of Toronto. Department of Economics Fall Instructor: Victor Aguirregabiria
Empirical Industrial Organization (ECO 30) University of Toronto. Department of Economics Fall 208. Instructor: Victor Aguirregabiria FINAL EXAM Tuesday, December 8th, 208. From 7pm to 9pm (2 hours) Exam
More information( ) is just a function of x, with
II. MULTIVARIATE CALCULUS The first ecture covered functions where a singe input goes in, and a singe output comes out. Most economic appications aren t so simpe. In most cases, a number of variabes infuence
More informationBourgain s Theorem. Computational and Metric Geometry. Instructor: Yury Makarychev. d(s 1, s 2 ).
Bourgain s Theorem Computationa and Metric Geometry Instructor: Yury Makarychev 1 Notation Given a metric space (X, d) and S X, the distance from x X to S equas d(x, S) = inf d(x, s). s S The distance
More informationManipulation in Financial Markets and the Implications for Debt Financing
Manipuation in Financia Markets and the Impications for Debt Financing Leonid Spesivtsev This paper studies the situation when the firm is in financia distress and faces bankruptcy or debt restructuring.
More informationExplicit overall risk minimization transductive bound
1 Expicit overa risk minimization transductive bound Sergio Decherchi, Paoo Gastado, Sandro Ridea, Rodofo Zunino Dept. of Biophysica and Eectronic Engineering (DIBE), Genoa University Via Opera Pia 11a,
More informationAST 418/518 Instrumentation and Statistics
AST 418/518 Instrumentation and Statistics Cass Website: http://ircamera.as.arizona.edu/astr_518 Cass Texts: Practica Statistics for Astronomers, J.V. Wa, and C.R. Jenkins, Second Edition. Measuring the
More informationLecture 9. Stability of Elastic Structures. Lecture 10. Advanced Topic in Column Buckling
Lecture 9 Stabiity of Eastic Structures Lecture 1 Advanced Topic in Coumn Bucking robem 9-1: A camped-free coumn is oaded at its tip by a oad. The issue here is to find the itica bucking oad. a) Suggest
More informationExpectation-Maximization for Estimating Parameters for a Mixture of Poissons
Expectation-Maximization for Estimating Parameters for a Mixture of Poissons Brandon Maone Department of Computer Science University of Hesini February 18, 2014 Abstract This document derives, in excrutiating
More informationCE601-Structura Anaysis I UNIT-IV SOPE-DEFECTION METHOD 1. What are the assumptions made in sope-defection method? (i) Between each pair of the supports the beam section is constant. (ii) The joint in
More informationNonlinear Analysis of Spatial Trusses
Noninear Anaysis of Spatia Trusses João Barrigó October 14 Abstract The present work addresses the noninear behavior of space trusses A formuation for geometrica noninear anaysis is presented, which incudes
More informationCompetitive Diffusion in Social Networks: Quality or Seeding?
Competitive Diffusion in Socia Networks: Quaity or Seeding? Arastoo Fazei Amir Ajorou Ai Jadbabaie arxiv:1503.01220v1 [cs.gt] 4 Mar 2015 Abstract In this paper, we study a strategic mode of marketing and
More informationMATH 172: MOTIVATION FOR FOURIER SERIES: SEPARATION OF VARIABLES
MATH 172: MOTIVATION FOR FOURIER SERIES: SEPARATION OF VARIABLES Separation of variabes is a method to sove certain PDEs which have a warped product structure. First, on R n, a inear PDE of order m is
More informationChemical Kinetics Part 2
Integrated Rate Laws Chemica Kinetics Part 2 The rate aw we have discussed thus far is the differentia rate aw. Let us consider the very simpe reaction: a A à products The differentia rate reates the rate
More informationOnline Appendices for The Economics of Nationalism (Xiaohuan Lan and Ben Li)
Onine Appendices for The Economics of Nationaism Xiaohuan Lan and Ben Li) A. Derivation of inequaities 9) and 10) Consider Home without oss of generaity. Denote gobaized and ungobaized by g and ng, respectivey.
More informationCombining reaction kinetics to the multi-phase Gibbs energy calculation
7 th European Symposium on Computer Aided Process Engineering ESCAPE7 V. Pesu and P.S. Agachi (Editors) 2007 Esevier B.V. A rights reserved. Combining reaction inetics to the muti-phase Gibbs energy cacuation
More informationSTA 216 Project: Spline Approach to Discrete Survival Analysis
: Spine Approach to Discrete Surviva Anaysis November 4, 005 1 Introduction Athough continuous surviva anaysis differs much from the discrete surviva anaysis, there is certain ink between the two modeing
More informationMARKOV CHAINS AND MARKOV DECISION THEORY. Contents
MARKOV CHAINS AND MARKOV DECISION THEORY ARINDRIMA DATTA Abstract. In this paper, we begin with a forma introduction to probabiity and expain the concept of random variabes and stochastic processes. After
More informationII. PROBLEM. A. Description. For the space of audio signals
CS229 - Fina Report Speech Recording based Language Recognition (Natura Language) Leopod Cambier - cambier; Matan Leibovich - matane; Cindy Orozco Bohorquez - orozcocc ABSTRACT We construct a rea time
More informationSeparation of Variables and a Spherical Shell with Surface Charge
Separation of Variabes and a Spherica She with Surface Charge In cass we worked out the eectrostatic potentia due to a spherica she of radius R with a surface charge density σθ = σ cos θ. This cacuation
More informationTHE OUT-OF-PLANE BEHAVIOUR OF SPREAD-TOW FABRICS
ECCM6-6 TH EUROPEAN CONFERENCE ON COMPOSITE MATERIALS, Sevie, Spain, -6 June 04 THE OUT-OF-PLANE BEHAVIOUR OF SPREAD-TOW FABRICS M. Wysocki a,b*, M. Szpieg a, P. Heström a and F. Ohsson c a Swerea SICOMP
More informationAlberto Maydeu Olivares Instituto de Empresa Marketing Dept. C/Maria de Molina Madrid Spain
CORRECTIONS TO CLASSICAL PROCEDURES FOR ESTIMATING THURSTONE S CASE V MODEL FOR RANKING DATA Aberto Maydeu Oivares Instituto de Empresa Marketing Dept. C/Maria de Moina -5 28006 Madrid Spain Aberto.Maydeu@ie.edu
More informationBresnahan, JIE 87: Competition and Collusion in the American Automobile Industry: 1955 Price War
Bresnahan, JIE 87: Competition and Collusion in the American Automobile Industry: 1955 Price War Spring 009 Main question: In 1955 quantities of autos sold were higher while prices were lower, relative
More informationStatistical Inference, Econometric Analysis and Matrix Algebra
Statistica Inference, Econometric Anaysis and Matrix Agebra Bernhard Schipp Water Krämer Editors Statistica Inference, Econometric Anaysis and Matrix Agebra Festschrift in Honour of Götz Trenker Physica-Verag
More informationQuantum Mechanical Models of Vibration and Rotation of Molecules Chapter 18
Quantum Mechanica Modes of Vibration and Rotation of Moecues Chapter 18 Moecuar Energy Transationa Vibrationa Rotationa Eectronic Moecuar Motions Vibrations of Moecues: Mode approximates moecues to atoms
More informationLesson 1. Walrasian Equilibrium in a pure Exchange Economy. General Model
Lesson Warasian Equiibrium in a pure Exchange Economy. Genera Mode Genera Mode: Economy with n agents and k goods. Goods. Concept of good: good or service competey specified phisicay, spaciay and timey.
More informationDIGITAL FILTER DESIGN OF IIR FILTERS USING REAL VALUED GENETIC ALGORITHM
DIGITAL FILTER DESIGN OF IIR FILTERS USING REAL VALUED GENETIC ALGORITHM MIKAEL NILSSON, MATTIAS DAHL AND INGVAR CLAESSON Bekinge Institute of Technoogy Department of Teecommunications and Signa Processing
More informationTurbo Codes. Coding and Communication Laboratory. Dept. of Electrical Engineering, National Chung Hsing University
Turbo Codes Coding and Communication Laboratory Dept. of Eectrica Engineering, Nationa Chung Hsing University Turbo codes 1 Chapter 12: Turbo Codes 1. Introduction 2. Turbo code encoder 3. Design of intereaver
More informationChemical Kinetics Part 2. Chapter 16
Chemica Kinetics Part 2 Chapter 16 Integrated Rate Laws The rate aw we have discussed thus far is the differentia rate aw. Let us consider the very simpe reaction: a A à products The differentia rate reates
More informationGeneral Certificate of Education Advanced Level Examination June 2010
Genera Certificate of Education Advanced Leve Examination June 2010 Human Bioogy HBI6T/P10/task Unit 6T A2 Investigative Skis Assignment Task Sheet The effect of temperature on the rate of photosynthesis
More informationASummaryofGaussianProcesses Coryn A.L. Bailer-Jones
ASummaryofGaussianProcesses Coryn A.L. Baier-Jones Cavendish Laboratory University of Cambridge caj@mrao.cam.ac.uk Introduction A genera prediction probem can be posed as foows. We consider that the variabe
More informationUSPAS Course on Recirculated and Energy Recovered Linacs
USPAS Course on Recircuated and Energy Recovered Linacs I. V. Bazarov Corne University D.R. Dougas, G. A. Krafft, and L. Merminga Jefferson Lab Computer Cass: Linear Optics in JLAB IRFEL, Longitudina gymnastics,
More informationSTABILITY OF A PARAMETRICALLY EXCITED DAMPED INVERTED PENDULUM 1. INTRODUCTION
Journa of Sound and Vibration (996) 98(5), 643 65 STABILITY OF A PARAMETRICALLY EXCITED DAMPED INVERTED PENDULUM G. ERDOS AND T. SINGH Department of Mechanica and Aerospace Engineering, SUNY at Buffao,
More informationFORECASTING TELECOMMUNICATIONS DATA WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS
FORECASTING TEECOMMUNICATIONS DATA WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODES Niesh Subhash naawade a, Mrs. Meenakshi Pawar b a SVERI's Coege of Engineering, Pandharpur. nieshsubhash15@gmai.com
More information3.10 Implications of Redundancy
118 IB Structures 2008-9 3.10 Impications of Redundancy An important aspect of redundant structures is that it is possibe to have interna forces within the structure, with no externa oading being appied.
More informationEXPERIMENT 5 MOLAR CONDUCTIVITIES OF AQUEOUS ELECTROLYTES
EXPERIMENT 5 MOLR CONDUCTIVITIES OF QUEOUS ELECTROLYTES Objective: To determine the conductivity of various acid and the dissociation constant, K for acetic acid a Theory. Eectrica conductivity in soutions
More informationTechnical Data for Profiles. Groove position, external dimensions and modular dimensions
Technica Data for Profies Extruded Profie Symbo A Mg Si 0.5 F 25 Materia number.206.72 Status: artificiay aged Mechanica vaues (appy ony in pressing direction) Tensie strength Rm min. 245 N/mm 2 Yied point
More informationTransport Cost and Optimal Number of Public Facilities
Transport Cost an Optima Number of Pubic Faciities Kazuo Yamaguchi Grauate Schoo of Economics, University of Tokyo June 14, 2006 Abstract We consier the number an ocation probem of pubic faciities without
More informationIE 361 Exam 1. b) Give *&% confidence limits for the bias of this viscometer. (No need to simplify.)
October 9, 00 IE 6 Exam Prof. Vardeman. The viscosity of paint is measured with a "viscometer" in units of "Krebs." First, a standard iquid of "known" viscosity *# Krebs is tested with a company viscometer
More informationA Simple and Efficient Algorithm of 3-D Single-Source Localization with Uniform Cross Array Bing Xue 1 2 a) * Guangyou Fang 1 2 b and Yicai Ji 1 2 c)
A Simpe Efficient Agorithm of 3-D Singe-Source Locaization with Uniform Cross Array Bing Xue a * Guangyou Fang b Yicai Ji c Key Laboratory of Eectromagnetic Radiation Sensing Technoogy, Institute of Eectronics,
More informationOptimization Based Bidding Strategies in the Deregulated Market
Optimization ased idding Strategies in the Dereguated arket Daoyuan Zhang Ascend Communications, nc 866 North ain Street, Waingford, C 0649 Abstract With the dereguation of eectric power systems, market
More informationNEW DEVELOPMENT OF OPTIMAL COMPUTING BUDGET ALLOCATION FOR DISCRETE EVENT SIMULATION
NEW DEVELOPMENT OF OPTIMAL COMPUTING BUDGET ALLOCATION FOR DISCRETE EVENT SIMULATION Hsiao-Chang Chen Dept. of Systems Engineering University of Pennsyvania Phiadephia, PA 904-635, U.S.A. Chun-Hung Chen
More informationPhysics 235 Chapter 8. Chapter 8 Central-Force Motion
Physics 35 Chapter 8 Chapter 8 Centra-Force Motion In this Chapter we wi use the theory we have discussed in Chapter 6 and 7 and appy it to very important probems in physics, in which we study the motion
More informationu(x) s.t. px w x 0 Denote the solution to this problem by ˆx(p, x). In order to obtain ˆx we may simply solve the standard problem max x 0
Bocconi University PhD in Economics - Microeconomics I Prof M Messner Probem Set 4 - Soution Probem : If an individua has an endowment instead of a monetary income his weath depends on price eves In particuar,
More informationA Comparison Study of the Test for Right Censored and Grouped Data
Communications for Statistica Appications and Methods 2015, Vo. 22, No. 4, 313 320 DOI: http://dx.doi.org/10.5351/csam.2015.22.4.313 Print ISSN 2287-7843 / Onine ISSN 2383-4757 A Comparison Study of the
More informationResearch of Data Fusion Method of Multi-Sensor Based on Correlation Coefficient of Confidence Distance
Send Orders for Reprints to reprints@benthamscience.ae 340 The Open Cybernetics & Systemics Journa, 015, 9, 340-344 Open Access Research of Data Fusion Method of Muti-Sensor Based on Correation Coefficient
More informationCapacity sharing among truck owners: A collaborative approach to overcome overloading
Capacity sharing among truck owners: A coaborative approach to overcome overoading Arindam Debroy 1 Research Schoar debroyarindam1@gmai.com S. P. Sarmah 1 Professor 1 Department of Industria and Systems
More information<C 2 2. λ 2 l. λ 1 l 1 < C 1
Teecommunication Network Contro and Management (EE E694) Prof. A. A. Lazar Notes for the ecture of 7/Feb/95 by Huayan Wang (this document was ast LaT E X-ed on May 9,995) Queueing Primer for Muticass Optima
More informationAnalysis of Emerson s Multiple Model Interpolation Estimation Algorithms: The MIMO Case
Technica Report PC-04-00 Anaysis of Emerson s Mutipe Mode Interpoation Estimation Agorithms: The MIMO Case João P. Hespanha Dae E. Seborg University of Caifornia, Santa Barbara February 0, 004 Anaysis
More informationStrain Energy in Linear Elastic Solids
Strain Energ in Linear Eastic Soids CEE L. Uncertaint, Design, and Optimiation Department of Civi and Environmenta Engineering Duke Universit Henri P. Gavin Spring, 5 Consider a force, F i, appied gradua
More informationIn-plane shear stiffness of bare steel deck through shell finite element models. G. Bian, B.W. Schafer. June 2017
In-pane shear stiffness of bare stee deck through she finite eement modes G. Bian, B.W. Schafer June 7 COLD-FORMED STEEL RESEARCH CONSORTIUM REPORT SERIES CFSRC R-7- SDII Stee Diaphragm Innovation Initiative
More informationPhysicsAndMathsTutor.com
. Two points A and B ie on a smooth horizonta tabe with AB = a. One end of a ight eastic spring, of natura ength a and moduus of easticity mg, is attached to A. The other end of the spring is attached
More informationProcess Capability Proposal. with Polynomial Profile
Contemporary Engineering Sciences, Vo. 11, 2018, no. 85, 4227-4236 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ces.2018.88467 Process Capabiity Proposa with Poynomia Profie Roberto José Herrera
More informationBP neural network-based sports performance prediction model applied research
Avaiabe onine www.jocpr.com Journa of Chemica and Pharmaceutica Research, 204, 6(7:93-936 Research Artice ISSN : 0975-7384 CODEN(USA : JCPRC5 BP neura networ-based sports performance prediction mode appied
More information1D Heat Propagation Problems
Chapter 1 1D Heat Propagation Probems If the ambient space of the heat conduction has ony one dimension, the Fourier equation reduces to the foowing for an homogeneous body cρ T t = T λ 2 + Q, 1.1) x2
More informationTechnical Appendix for Voting, Speechmaking, and the Dimensions of Conflict in the US Senate
Technica Appendix for Voting, Speechmaking, and the Dimensions of Confict in the US Senate In Song Kim John Londregan Marc Ratkovic Juy 6, 205 Abstract We incude here severa technica appendices. First,
More informationSequential Decoding of Polar Codes with Arbitrary Binary Kernel
Sequentia Decoding of Poar Codes with Arbitrary Binary Kerne Vera Miosavskaya, Peter Trifonov Saint-Petersburg State Poytechnic University Emai: veram,petert}@dcn.icc.spbstu.ru Abstract The probem of efficient
More informationForces of Friction. through a viscous medium, there will be a resistance to the motion. and its environment
Forces of Friction When an object is in motion on a surface or through a viscous medium, there wi be a resistance to the motion This is due to the interactions between the object and its environment This
More informationLecture 6 Povh Krane Enge Williams Properties of 2-nucleon potential
Lecture 6 Povh Krane Enge Wiiams Properties of -nuceon potentia 16.1 4.4 3.6 9.9 Meson Theory of Nucear potentia 4.5 3.11 9.10 I recommend Eisberg and Resnik notes as distributed Probems, Lecture 6 1 Consider
More informationPREDICTION OF DEFORMED AND ANNEALED MICROSTRUCTURES USING BAYESIAN NEURAL NETWORKS AND GAUSSIAN PROCESSES
PREDICTION OF DEFORMED AND ANNEALED MICROSTRUCTURES USING BAYESIAN NEURAL NETWORKS AND GAUSSIAN PROCESSES C.A.L. Baier-Jones, T.J. Sabin, D.J.C. MacKay, P.J. Withers Department of Materias Science and
More informationComputer class: Linear Optics in JLAB: Longitudinal Dynamics and BBU
USPAS course on Recircuated and Energy Recovered Linacs Ivan Bazarov, Corne University Geoff Krafft and Dave Dougas, JLAB Computer cass: Linear Optics in JLAB: Longitudina Dynamics and BBU JLAB IRFEL Spreadsheet
More information17 Lecture 17: Recombination and Dark Matter Production
PYS 652: Astrophysics 88 17 Lecture 17: Recombination and Dark Matter Production New ideas pass through three periods: It can t be done. It probaby can be done, but it s not worth doing. I knew it was
More informationAPPENDIX C FLEXING OF LENGTH BARS
Fexing of ength bars 83 APPENDIX C FLEXING OF LENGTH BARS C.1 FLEXING OF A LENGTH BAR DUE TO ITS OWN WEIGHT Any object ying in a horizonta pane wi sag under its own weight uness it is infinitey stiff or
More informationInductive Bias: How to generalize on novel data. CS Inductive Bias 1
Inductive Bias: How to generaize on nove data CS 478 - Inductive Bias 1 Overfitting Noise vs. Exceptions CS 478 - Inductive Bias 2 Non-Linear Tasks Linear Regression wi not generaize we to the task beow
More informationIterative Decoding Performance Bounds for LDPC Codes on Noisy Channels
Iterative Decoding Performance Bounds for LDPC Codes on Noisy Channes arxiv:cs/060700v1 [cs.it] 6 Ju 006 Chun-Hao Hsu and Achieas Anastasopouos Eectrica Engineering and Computer Science Department University
More informationReliability Improvement with Optimal Placement of Distributed Generation in Distribution System
Reiabiity Improvement with Optima Pacement of Distributed Generation in Distribution System N. Rugthaicharoencheep, T. Langtharthong Abstract This paper presents the optima pacement and sizing of distributed
More informationTwo-Stage Least Squares as Minimum Distance
Two-Stage Least Squares as Minimum Distance Frank Windmeijer Discussion Paper 17 / 683 7 June 2017 Department of Economics University of Bristo Priory Road Compex Bristo BS8 1TU United Kingdom Two-Stage
More informationRecursive Constructions of Parallel FIFO and LIFO Queues with Switched Delay Lines
Recursive Constructions of Parae FIFO and LIFO Queues with Switched Deay Lines Po-Kai Huang, Cheng-Shang Chang, Feow, IEEE, Jay Cheng, Member, IEEE, and Duan-Shin Lee, Senior Member, IEEE Abstract One
More informationSchedulability Analysis of Deferrable Scheduling Algorithms for Maintaining Real-Time Data Freshness
1 Scheduabiity Anaysis of Deferrabe Scheduing Agorithms for Maintaining Rea-Time Data Freshness Song Han, Deji Chen, Ming Xiong, Kam-yiu Lam, Aoysius K. Mok, Krithi Ramamritham UT Austin, Emerson Process
More informationTHE THREE POINT STEINER PROBLEM ON THE FLAT TORUS: THE MINIMAL LUNE CASE
THE THREE POINT STEINER PROBLEM ON THE FLAT TORUS: THE MINIMAL LUNE CASE KATIE L. MAY AND MELISSA A. MITCHELL Abstract. We show how to identify the minima path network connecting three fixed points on
More informationTRAVEL TIME ESTIMATION FOR URBAN ROAD NETWORKS USING LOW FREQUENCY PROBE VEHICLE DATA
TRAVEL TIME ESTIMATIO FOR URBA ROAD ETWORKS USIG LOW FREQUECY PROBE VEHICLE DATA Erik Jeneius Corresponding author KTH Roya Institute of Technoogy Department of Transport Science Emai: erik.jeneius@abe.kth.se
More informationSUPPLEMENTARY MATERIAL TO INNOVATED SCALABLE EFFICIENT ESTIMATION IN ULTRA-LARGE GAUSSIAN GRAPHICAL MODELS
ISEE 1 SUPPLEMENTARY MATERIAL TO INNOVATED SCALABLE EFFICIENT ESTIMATION IN ULTRA-LARGE GAUSSIAN GRAPHICAL MODELS By Yingying Fan and Jinchi Lv University of Southern Caifornia This Suppementary Materia
More informationAppendix A: MATLAB commands for neural networks
Appendix A: MATLAB commands for neura networks 132 Appendix A: MATLAB commands for neura networks p=importdata('pn.xs'); t=importdata('tn.xs'); [pn,meanp,stdp,tn,meant,stdt]=prestd(p,t); for m=1:10 net=newff(minmax(pn),[m,1],{'tansig','purein'},'trainm');
More informationA MODEL FOR ESTIMATING THE LATERAL OVERLAP PROBABILITY OF AIRCRAFT WITH RNP ALERTING CAPABILITY IN PARALLEL RNAV ROUTES
6 TH INTERNATIONAL CONGRESS OF THE AERONAUTICAL SCIENCES A MODEL FOR ESTIMATING THE LATERAL OVERLAP PROBABILITY OF AIRCRAFT WITH RNP ALERTING CAPABILITY IN PARALLEL RNAV ROUTES Sakae NAGAOKA* *Eectronic
More informationThe EM Algorithm applied to determining new limit points of Mahler measures
Contro and Cybernetics vo. 39 (2010) No. 4 The EM Agorithm appied to determining new imit points of Maher measures by Souad E Otmani, Georges Rhin and Jean-Marc Sac-Épée Université Pau Veraine-Metz, LMAM,
More informationTesting for the Existence of Clusters
Testing for the Existence of Custers Caudio Fuentes and George Casea University of Forida November 13, 2008 Abstract The detection and determination of custers has been of specia interest, among researchers
More informationResearch Article On the Lower Bound for the Number of Real Roots of a Random Algebraic Equation
Appied Mathematics and Stochastic Anaysis Voume 007, Artice ID 74191, 8 pages doi:10.1155/007/74191 Research Artice On the Lower Bound for the Number of Rea Roots of a Random Agebraic Equation Takashi
More informationGeneral Certificate of Education Advanced Level Examination June 2010
Genera Certificate of Education Advanced Leve Examination June 2010 Human Bioogy HBI6T/Q10/task Unit 6T A2 Investigative Skis Assignment Task Sheet The effect of using one or two eyes on the perception
More informationA Robust Voice Activity Detection based on Noise Eigenspace Projection
A Robust Voice Activity Detection based on Noise Eigenspace Projection Dongwen Ying 1, Yu Shi 2, Frank Soong 2, Jianwu Dang 1, and Xugang Lu 1 1 Japan Advanced Institute of Science and Technoogy, Nomi
More informationAALBORG UNIVERSITY. The distribution of communication cost for a mobile service scenario. Jesper Møller and Man Lung Yiu. R June 2009
AALBORG UNIVERSITY The distribution of communication cost for a mobie service scenario by Jesper Møer and Man Lung Yiu R-29-11 June 29 Department of Mathematica Sciences Aaborg University Fredrik Bajers
More informationStochastic Complement Analysis of Multi-Server Threshold Queues. with Hysteresis. Abstract
Stochastic Compement Anaysis of Muti-Server Threshod Queues with Hysteresis John C.S. Lui The Dept. of Computer Science & Engineering The Chinese University of Hong Kong Leana Goubchik Dept. of Computer
More informationCompetition Between Networks: A Study in the Market for Yellow Pages Mark Rysman
Competition Between Networks: A Study in the Market for Yellow Pages Mark Rysman 1 Network effects between consumers and advertisers. Consumers: Choose how much to use the yellow page directory j, given
More information