) from i = 0, instead of i = 1, we have =

Similar documents
p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have:

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

CHAPTER 10: LINEAR DISCRIMINATION

1 Constant Real Rate C 1

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED)

Name of the Student:

Answers to Tutorial Questions

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova

Monetary policy and models

Graduate Macroeconomics 2 Problem set 5. - Solutions

The balanced budget multiplier and labour intensity in home production

to Assess Climate Change Mitigation International Energy Workshop, Paris, June 2013

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland

Handling Fuzzy Constraints in Flow Shop Problem

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.

IMES DISCUSSION PAPER SERIES

Notes on McCall s Model of Job Search. Timothy J. Kehoe March if job offer has been accepted. b if searching

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter 3: Vectors and Two-Dimensional Motion

Suppose we have observed values t 1, t 2, t n of a random variable T.

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

CHAPTER 10: LINEAR DISCRIMINATION

Solution to Problem First, the firm minimizes the cost of the inputs: min wl + rk + sf

s = rθ Chapter 10: Rotation 10.1: What is physics?

A Comment on Increasing Returns and Spatial. Unemployment Disparities

7 Wave Equation in Higher Dimensions

A multi-band approach to arterial traffic signal optimization. Nathan H. Gartner Susan F. Assmann Fernando Lasaga Dennin L. Hou

Chapter Fifiteen. Surfaces Revisited

Lecture 5. Plane Wave Reflection and Transmission

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

Field due to a collection of N discrete point charges: r is in the direction from

MCTDH Approach to Strong Field Dynamics

SCIENCE CHINA Technological Sciences

Rotations.

Solution in semi infinite diffusion couples (error function analysis)

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

Reinforcement learning

I-Hsuan Hong Hsi-Mei Hsu Yi-Mu Wu Chun-Shao Yeh


Econ 201: Problem Set 2 Answers

Comparative Study of Inventory Model for Duopolistic Market under Trade Credit Deepa H Kandpal *, Khimya S Tinani #

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe

Lecture 2 M/G/1 queues. M/G/1-queue

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

Fast Calibration for Robot Welding System with Laser Vision

P 365. r r r )...(1 365

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t

A. Thicknesses and Densities

Computer Propagation Analysis Tools

Volatility Interpolation

Integer Programming Models for Decision Making of. Order Entry Stage in Make to Order Companies 1. INTRODUCTION

ROBUST EXPONENTIAL ATTRACTORS FOR MEMORY RELAXATION OF PATTERN FORMATION EQUATIONS

APPROXIMATIONS FOR AND CONVEXITY OF PROBABILISTICALLY CONSTRAINED PROBLEMS WITH RANDOM RIGHT-HAND SIDES

Tecnologia e Inovação, Lisboa, Portugal. ABB Corporate Research Center, Wallstadter Str. 59, Ladenburg, Germany,

Variability Aware Network Utility Maximization

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

ajanuary't I11 F or,'.

FI 3103 Quantum Physics

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

A DISCRETE PARAMETRIC MARKOV-CHAIN MODEL OF A TWO NON-IDENTICAL UNIT COLD STANDBY SYSTEM WITH PREVENTIVE-MAINTENANCE

156 There are 9 books stacked on a shelf. The thickness of each book is either 1 inch or 2

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs)

Bethe-Salpeter Equation Green s Function and the Bethe-Salpeter Equation for Effective Interaction in the Ladder Approximation

Simulation of Non-normal Autocorrelated Variables

L-1. Intertemporal Trade in a Two- Period Model

If there are k binding constraints at x then re-label these constraints so that they are the first k constraints.

Lecture 6: Learning for Control (Generalised Linear Regression)

Reflection and Refraction

c- : r - C ' ',. A a \ V

When to Treat Prostate Cancer Patients Based on their PSA Dynamics

Linear Response Theory: The connection between QFT and experiments

Lecture 11 SVM cont

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations

3. A Review of Some Existing AW (BT, CT) Algorithms

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions

Accelerated Sequen.al Probability Ra.o Test (SPRT) for Ongoing Reliability Tes.ng (ORT)

An Open cycle and Closed cycle Gas Turbine Engines. Methods to improve the performance of simple gas turbine plants

Real-coded Quantum Evolutionary Algorithm for Global Numerical Optimization with Continuous Variables

ME 3600 Control Systems Frequency Domain Analysis

_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

Backcalculation Analysis of Pavement-layer Moduli Using Pattern Search Algorithms

Lecture VI Regression

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Chapters 2 Kinematics. Position, Distance, Displacement

Solution of Non-homogeneous bulk arrival Two-node Tandem Queuing Model using Intervention Poisson distribution

Let s treat the problem of the response of a system to an applied external force. Again,

N 1. Time points are determined by the

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard

Multistage Median Ranked Set Sampling for Estimating the Population Median

Hierarchical Production Planning in Make to Order System Based on Work Load Control Method

Thermodynamics of solids 4. Statistical thermodynamics and the 3 rd law. Kwangheon Park Kyung Hee University Department of Nuclear Engineering

Sterilization of Capital Inflows and Balance of. Payments Crises

Inventory Policy Implications of On-Line Customer Purchase Behavior

Transcription:

Chape 3: Adjusmen Coss n he abou Make I Movaonal Quesons and Execses: Execse 3 (p 6): Illusae he devaon of equaon (35) of he exbook Soluon: The neempoal magnal poduc of labou s epesened by (3) = = E λ hee ( ) s he mmedae magnal poduc of labou s he eal nees ae denoes he poduc demand s he level of employmen [ ] E s he expecaon opeao based on he nfomaon se avalable a and s he age Usng dynamc pogammng e can ee (3) as follos: (3) = = E λ If e e-ndex ( = ) fom = nsead of = e have = and heefoe (33) = = E λ By he la of eave expecaons he above equaon can be en as (34) = = E E λ By (3) e can defne λ as follos (35) = = E λ Subsung (35) no (34) gves

(36) λ ( ) E [ λ ] = hch s equaon (35) n he exbook Execse 3 (p 5): Consde he o-sae Makov pocess (36) x a = b h pob p f x = a; h pob h pob q f x = b; h pob ( p) ( q) f x = b f x = a Wha s he expeced duaon of he Makov pocess n sae a? Soluon: Gven ha he pocess s cuenly n sae a p s he anson pobably fo he Makov pocess o sa and say n sae a and D s he duaon of sae a e have (37) D = f x = a and x a; pob D = f x = x = a and x D = 3 f x = x = x = a and x M ( D = ) = ( p) a; pob( D = ) = p( p) a; pob( D = 3) = p ( 3 p ) The expeced duaon of sae a can hen be deved as (38) E = ( D) = a pob( D a) M = a= pob pob 3 pob ( x a x = a) ( x = a x a x = a) ( = a = a a = a) ( p) p( p) 3 p ( p) ( p) = x x x 3 x Execse 33 [Blanchad and Fshe (989) Poblem 3 p 49]: Consde a fm and a unon ho bagan h each ohe ove ages and employmen Wages ae chosen so as o maxmse ( - A)[R() ] subjec o he gh-o-manage consan R () = s employmen R() s he evenue funcon and A s he expeced level of ncome fo hose ho ae no employed by he fm (a) Deve he age level esulng fom he ash bagan as a funcon of he labou ncome o pofs ao θ he age elascy of labou demand η and A (b) e A be gven by ( u) Bu hee u s he ae of unemploymen he ousde age f employed elsehee and B he ncome f unemployed Deve he ae of unemploymen conssen h a symmec equlbum assumng fo convenence ha B s consan and denoed by ρ

Soluon: (a) Consde he gh-o-manage model n hch he age s deemned n a non-coopeave ash bagan and n hch he fm subsequenly chooses opmal labou demand fom he labou demand cuve The objecve funcon of he unon s (-A) hle he objecve funcon of he fm s [R() - ] Consequenly he ash baganng soluon s deemned by (39) ( A)[ R( ) ] [ R ( ) ] max λ We ge he follong o fs-ode condons: (3) [ ( A) ] [ R( ) ] ( A) [ R' ( ) ] λ [ R' ] = and (3) R ( ) = Equaon ( ) mples ( ) = We heefoe ge R (3) [ ( A) ] [ R( ) ] ( A) [ R' ( ) ] = Snce (33) ( R' ( ) = [ R' ( ) ] = R ) = e have [ ( A) ] [ R( ) ] = ( A) η θ η A ( A) = θ A η = θ θ A = η θ ( A) [ R( ) ] = ( A) ( A) = ( A) Thus hee e have used he defnons of he age elascy of labou demand labou ncome o pof shae θ = ( R( ) ) η = and he When η s nceasng he unons ancpae he lage mpac of hghe ages upon employmen and heefoe he age level ll be loe In he lm fo η e ge = A (b) Inseng A = ( u) Bu = [ ( u) ρ] = [ ( ρ) u] hee ρ = B no he eal age equaon and ecognsng R ( ) = e ave a 3

(34) A = A = A = [( u) ub] ( u) u [ ( u) uρ] ( u) u B A hghe ρ = B and heefoe a moe geneous unemploymen benef sysem ll ncease equlbum unemploymen In a symmec equlbum e have = and heefoe equaon (34) smplfes o A (35) = u( ρ) The coespondng equlbum unemploymen ae s A (36) u = ρ Addonal Refeence: Blanchad OJ and S Fshe (989) ecues on Macoeconomcs Cambdge (MIT Pess) II Sofae Tools Sofae Execse 3: Exploe he cyclcal behavou of and neempoal magnal value of pofs n execse 3 on p 3 and pp 44-45 Soluon: The sang pons of hng and fng decsons ae llusaed by he equaons (*) and (**) on p 44: [ ] β τ (37) h = e dτ τ τ [ ] β τ (38) f = e dτ τ τ hee s he numbe of employees s dscoun ae β > epesens he concavy of poducon funcon h s he magnal cos of hng f s he magnal cos of fng and denoes he π deemnscally flucuang level of demand ( τ ) = K K sn τ hee K > K > The p values of K K and p deemne he magnude and he lengh of he busness cycle I s assumed ha employees do no qu and ha hee ae no quadac coss of hng and fng he fm hes o β fes mmedaely so ha [ ] ( τ ) τ τ e dτ ll neve be geae han h o loe han f The nacon aea s defned by he egme of β ( τ ) [ ] e f < dτ < h τ τ 4

= τ e τ dτ can be β ( τ ) Fo he nacon aea hee no hng/fng happens he negal [ ] en as follos: v (39) v = K β K β π sn τ e p ( τ ) dτ hee v solve β ( τ ) [ ] e = τ τ π τ p sn τ e dτ : λx λe γ sn γx e dτ = sn γx cos γx γ λ λ λx (3) dτ The negal fomula on p 45 of he exbook can be used o Subsung he above equaon no (39) yelds (3) v = K β K β λx e γ π π π sn x cos x p p p Fo ( ) h v > he fm mmedaely hes h so ha ( ) h v h = The nely hed okes h lead o a loe magnal poduc of labou so ha v = h agan Smlaly fo v( ) < f he fm mmedaely lays off f so ha v( f ) = f The fng of f employees leads o a hghe magnal poduc of labou so ha v = f agan The Malab fle man_execse_3-m allos o analyse such a sysem of flucuaons due o hng and fng ove he deemnsc busness cycles hch s denoed by he sn funcon n % begnnng of npu daa fo (76) and (77) = ; = ; = ; %nal value of bea = 3; h = ; f = ; K = ; K = 5; p = 4; % end of npu daa 5

8 Sofae execse 3: he cyclcal movemenns of and v 6 4 and v 8 6 4 v - 4 6 8 4 Tme oe ha hee ae some dffeences beeen he me sees of and v snce v s he neempoal value The peaks and oughs of do no concde h he maxmum pons of v The eason s ha v ll only flucuae n he aeas of f v h When v s beyond he peak he fm sops hng Convesely he fm sops fng afe v eaches s ough If hng and fng coss ae vey small say h = and f = hen he flucuaons of ll mosly lead o vaaons n no n v 8 6 4 Sofae execse 3: he cyclcal movemenns of and v and v 8 6 4 v h= f= - 4 6 8 4 Tme On he conay f h = and hee exs subsanal fng coss say f = 6 hen he flucuaons of ll mosly lead o bgge vaaons n v bu smalle flucuaons n 6

Sofae execse 3: he cyclcal movemenns of and v 5 and v 5 v h= f=6-5 - 4 6 8 4 Tme Sofae-Execse 3: Demonsae he mpacs of changes n he qu ae fng coss and unceany upon he hng and fng hesholds n execse 4 on p 4 and pp 46-47 Soluons: The soluons fo v n he dffeenal equaon on p 46 ae denoed by (3) v η θ δ δ = α α Kη K η ( β ) β hee η = s he numbe of employees s he nees ae δ s he qu ae θ s he df paamee of geomecal Bonan moons of demand σ s he sandad devaon s he age level and K and K ae paamees elaed o dffuson pocesses and had o be solved by he valuemachng and smooh-pasng condons oe ha hee s a ypo n he equaon fo he pacula soluon n he exbook! e and epesen he hng and fng hesholds The value-machng condons deved usng he dynamc pogammng appoach ae denoed by (33) θ β ( β ) K δ δ β α β α ( ) K ( ) = H and (34) θ β ( β ) K δ δ β α β α ( ) K ( ) = F hee H s he magnal hng cos and F s he magnal fng cos The coespondng smooh-pasng condons ae (35) θ β ( β ) α δ α βα α βα K α K = 7

and (36) θ β ( β ) α δ α βα α βα K α K = Equaons (35) and (36) can be solved numecally usng he Malab m fles fun_execse_3_m and man_execse m The npu daa ae as follos: % begnnng of npu daa = ; bea = 3; dela = 5; hea = ; sgma = ; = ; age = ; H = ; F = 6; choceofplo = ; % "" fo plo of F; % "" fo plo of sgma; % "3" fo plo of dela fo execse 3 (c); % end of npu daa Gven he choce of he value of choceofplo o o 3 he Malab m fles ll geneae a plo ha shos he effec of F o σ o δ on he hng hesholds and he fng hesholds The Effec of F on Thesholds As F nceases he hng hesholds fall he fms ae moe elucan o fe magnal employees The effec of F on hng hesholds oks ndecly va he dffuson em K ( β α ) Hghe F leads o α ( β a loe value of K ) a he fng hesholds and also loes K ( β ) a he hng hesholds Theefoe he hng hesholds ae hghe fo sng F α 8

4 3 Sofae execse 3: he effec of F on hesholds - and - 9 8 7 3 4 5 6 7 8 9 F The Effec of σ on Thesholds The value of σ only affecs he dffuson ems of he soluons As expeced a ske envonmen (hghe σ ) dens he nacon aea Thus a se n σ makes he fm elucan o he o fe magnal employees 4 3 Sofae execse 3: he effec of sgma on hesholds - and - 9 8 7 5 5 3 35 sgma The Effec of δ on Thesholds As δ nceases he pacula negal falls and leads o hghe hng and fng hesholds Hoeve a sng δ has a negave mpac on he opon (dffuson) ems and leads o loe hesholds As β α β α K K ) s negave ndcaed by he gaph belo he sum of he dffuson ems ( 9

4 3 Sofae execse 3: he effec of dela on hesholds - and - 9 8 7 3 4 5 6 7 8 9 dela Sofae-Execse 33: Wokng me and employmen decsons An exenson of sofae execse 3 Assume ha a fm has he follong mmedae pof: β Π = ( g( H )) [ ( H ) x] β hee x s he fxed coss of employmen g s a funcon elaed o okng hous age s also a funcon of okng hous H denoes okng hous and all ohe vaables ae he same as n sofae execse 3 agan follos a geomecal Bonan moon The exsence of fxed coss pe oke x ends o make fms demand longe okng hous n ode o spead hese coss ove moe hous of ok g and have he follong funconal foms: g( H ) H = H γ s γ H / H s δ H H > H H s s ( H ) s = H s H s a s H H s H H > H H s s I s assumed ha < δ < so ha g(h) s scly concave and he poblem of he fm s ell defned An exogenous educon of sandad hous H s may ncease o decease g(h) and dependng on he oveme age pemum ncease o decease employmen and labou sevces The fm pays a consan pemum a > on oveme hous (H H s ) The magnal coss of hng and fng ae denoed by T and F especvely and he employees neve qu The fm smulaneously chooses acual hous and employmen o maxmse s expeced dscouned value of pofs (a) Deve he Bellman equaon of he above se-up and solve he okng me employmen decsons se-up (b) Analyse numecally he mpac of he oveme age pemum a fng coss F and hng coss T on he employmen hesholds and hous oked

Soluon: (a) The fm s expeced value of dscouned pofs hou any fng and/o hng coss s β s (37) V max E ( g( H )) [ ( H ) x] e ds = H β hee s he eal ae of nees and E[ ] s he expecaon opeao Accodng o equaon (37) he fm chooses ho many people o employ and he specfc numbe of hous gven he age schedule Usng Iô s emma he Bellman equaon fo he value V a me zeo n he connuaon egon s β (38) V = max ( g( H )) [ ( H ) x] ηv H σ V The fs em on he gh-hand sde s evenue [ ( H ) x] s he employmen-elaed bll ηv s he gan due o a shock and he las em s he change n he value of he fm caused by changes n demand The fs-ode condons fo H ae: β β (39) ( β ) g ( H) g' ( H) ' ( H) = Afe solvng he above equaon he vaable H becomes a funcon of gven he funcons of and g The fs-ode condon h espec o s denoed by σ β β (33) v = g ( H ) [ ( H ) x] η v v hee v = V s he value of employng he magnal oke As n he pevous execse he nacon aea fo hng and fng s deemned by he condon (33) F < v < T The hng hesholds ae deemned hen v = T and he fng hesholds happen hen F = v In he absence of hng and fng coss he pacula negal may be expessed as β β (33) v ( ) = E g ( H ) [ ( H ) z] β β s g [ ] ( H ) ( H ) e ds = P θ The fm s opon value of hng n he fuue and s opon value of fng once he oke s employed ae measued by he homogenous pa of he equaon z (333) v = θ v σ v α α The hng opons ae denoed by A and he fng opons ae epesened by A hee A and A ae paamees o be deemned by he bounday condons and α and α ae he posve and negave oos of he follong chaacesc equaon:

σ α β θα = (334) ( ) The value-machng condons of hng and fng follo: (335) β g θ β ( H ) ( H ) x α α A = T A and (336) ( H ) ( H ) x α α β β g θ A = F A The lef-hand sdes of (335) and (336) sho he magnal benef fom hng/fng a oke and he gh-hand sdes gve he coespondng magnal coss The smooh-pasng condons ensue ha hng (fng) s no opmal ehe befoe o afe he hng (fng) heshold s eached: (337) β β g θ ( H ) α α A α T = Aα T and (338) β β g θ ( H ) α α A α = Aα Equaons (335) - (338) fom a non-lnea sysem of equaons h fou unknon paamees A and A and can be solved numecally once he soluons fo α and α ae obaned fom (334) and opmal values fo H ae found fo he values of and va equaon (39): β β (339) ( β ) g ( H) g' ( H) ' ( H) = and β β (34) ( β ) g ( H) g' ( H) ' ( H) = fo hng and hng hesholds especvely T F Equaons (335) - (34) can be solved numecally usng he follong Malab m fles: fun_execse_3_m fo value-machng and smooh-pasng condons fun_execse_3 gm fo funcon g(h) fun_execse_3 g_dm fo he devave of g(h) fun_execse_3 m fo funcon (H) fun_execse_3 dm fo he devave of (H) fun_execse_3 Hhm fo deemnaon of hous n hng hesholds fun_execse_3 Hfm fo deemnaon of hous n fng hesholds man_execse_3_3m fo he man pogam The npu daa ae as follos:

% begnnng of npu daa = 8; % nees aes bea = 3; % fo CD funcon hea = ; % df paamee fo geomecal Bonan moons sgma = 3; % sk paamee fo geomecal Bonan moons = ; % he level of employees T = ; % magnal hng cos F = 6; % maganl fng cos a = 4; % pemum ae fo ove me HS = 5; % benchmak value fo hous WS = ; % benchmak value fo ages gama = 8; % fo age funcon dela = 8; % fo age funcon xx = 5; % x he fxed cos elaed ages choceofplo = 3; % "" fo plo of F; % "" fo plo of T; % "3" fo plo of a fo execse 3 (c); % end of npu daa Gven he choce of he value of choceofplo o o 3 he Malab m fles ll geneae a plo ha shos he effec of F o T o a on he hng hesholds and he fng hesholds and coespondng hous The Effec of F on Thesholds The effec of F on he hng and fng hesholds ae smla o he ones n sofae execse 3 excep he nacon aea s de As F nceases he fm loes he okng hous fuhe befoe fng; he se n F also leads o hghe hng hesholds snce he fm ops nsead fo nceasng okng hous Sofae execse 3: he effec of F on hesholds and hous 8 and - and Hh and Hf 6 4 8 - H H- 6 4 3 4 5 6 7 8 9 F The Effec of T on he Thesholds The ncease n T has a dec mpac on hng hesholds amplfed by he hghe okng hous noduced o offse sng hng coss The mpac of changes n T on fng hesholds s mnmal due o smalle fng opons (fom hgh fng coss n he benchmak value) 3

Sofae execse 3: he effec of sgma on hesholds and hous 4 and - and Hh and Hf 8 6 4 8 - H H- 6 4 5 5 5 3 35 4 45 5 T The Effec of he Oveme Pemum (a) on he Thesholds Fo loe oveme pemums he fm pefes nceasng okng hous ahe han hng addonal employees The mpac of changes n a on he fng hesholds s smalle snce he oveme pemum only decly affecs he age funcon n hng decsons Sofae execse 3: he effec of dela on hesholds and hous 8 6 and - and Hh and Hf 4 8-6 H H- 4 35 36 37 38 39 4 4 4 a oveme pemum Addonal Refeence: Chen Y-F and Funke M (4) Wokng Tme and Employmen Unde Unceany Sudes n onlnea Dynamcs and Economecs 8 Issue 3 Acle 5 (bepesscom/snde/vol8/ss3/a5) 4