Discrete Economic Dynamics

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    Dynamic Macroeconomic Theory

    Thomas LuxUniversity of Kiel

    Prof. Dr. Thomas Lux, [email protected]

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    In this lecture: Focus is mainly a deterministic dynamic system without stochasticshocks

    However: Adding small amounts of noise does mostly not change the qualitativeoutcome of the dynamics so that in theoretical purpose the analysis of the deterministicanalogue is appropriate.

    Prof. Dr. Thomas Lux, [email protected]

    Dynamic Macroeconomic Theory

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    Variants of dynamic equations:

    1) difference equation:

    equivalently:

    (2) differential equation:

    but there might be also higher derivatives:

    (3) mixed difference differential equations:

    higher derivatives, more lags are possible

    nonlinear equations:

    Prof. Dr. Thomas Lux, [email protected]

    ( )tgyayay ttt +++= ...2211

    ( ) ( )tgyayay ttyy

    t

    tt

    ...1 2211

    1

    ++=

    =

    ( )tyyf tt ,...,, 21 =

    ( )tyhdt

    dyt

    t ,=

    ( ) ( ) ( )tgyayayaya nnnn =++++

    1

    1

    10 ...

    ( ) ( ) ( ) ( ) ( )tgwtybtybwtyatya =+++ 1010

    ( ) = nttt yyyg ,...,, 1

    Dynamic Macroeconomic Theory

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    1.1.1. Mathematical Background

    First-order linear difference equation:

    with time-dependent function this is called a non homogenous equation

    Homogeneous equation :

    or:

    Solution via iteration:

    Prof. Dr. Thomas Lux, [email protected]

    ( )tgycyc tt =+ 101

    ( )tg

    0

    0

    1

    101

    =+

    =+

    tt

    tt

    byy

    ycyc

    1

    0,c

    cb=

    ( ) etc.02

    01

    0

    byby

    bAbyy

    Ay

    =

    ==

    = ( )

    ( ) Ab

    yby

    t

    t

    t

    =

    =

    0

    1.1 First and Second Order DifferenceEquation

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    is a general solution as it satisfies

    irrespective of A

    to fix A, any known function value could be used, e.g.

    types of dynamic behavior:

    monotonic convergence

    oscillatory convergence

    monotonic divergence

    oscillatory divergence

    ( )Aby tt =

    ( )

    ( ) 01

    1=+

    tt y

    tt

    y

    AbbAb

    t*

    t

    (-b)

    y* AAbyty == *)(**)*,(

    :1

    :1

    :01

    :10

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    Gandolfo,1997. Fig.3.1

    1.1 First and Second Order DifferenceEquation

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    ( )tgAbycAbyc tt =+++ 101 )()(

    Solution of the non-homogeneous equation:

    General principle: Solution of non-homogeneous equation consists of solution of

    homogeneous equation plus so-called particular solution

    Hence: solution for

    Particular solution can often be interpreted as a steady state equilibrium in economicmodels

    It can be easily checked: if

    is also a solution!

    ( ) =+ tgycyc tt 101 yAby t

    t += )(

    ( )tgycyc =+ 01

    1.1 First and Second Order DifferenceEquation

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    Determination of particular solutions:

    Try a function with the same form of g(t) but with undetermined constants

    Substitute into non-homogeneous equation and determine the coefficients

    Examples:

    General solution:

    Prof. Dr. Thomas Lux, [email protected]

    ( )

    01

    01

    )1(

    cc

    ayacc

    yatg

    +===+

    ==

    :try

    101

    0 )(cc

    aA

    c

    cy

    t

    t ++=

    1.1 First and Second Order DifferenceEquation

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    Prof. Dr. Thomas Lux, [email protected]

    ( )

    t

    t

    ttt

    tt

    dcdc

    Bdy

    cdc

    BdC

    BdCcCdcd

    BdCdcCdc

    CdydBtg

    0101

    01

    1

    101

    ,

    0)(

    : try)2(

    +=

    +=

    =+

    =+

    ==

    1.1 First and Second Order DifferenceEquation

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    yAyty +== 00 )0,(

    Determination of the constant A:

    for example, for:

    Knowledge of leads to:

    Prof. Dr. Thomas Lux, [email protected]

    ( )....

    sincos: trysincos)3( 21 ttytBtBtg +=+=

    yAccy

    t

    t += )(1

    0

    yyyc

    cyyyA

    t

    t +== )()( 01

    00

    1.1 First and Second Order DifferenceEquation

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    Second-Order Linear Difference Equations

    General form:

    Homogeneous equation:

    or:

    )(c 20112 tgyycyc ttt =++

    2

    )4(

    0)(

    0

    2/1

    2

    2

    12,1

    2

    1

    22-t

    2-t

    2

    1-t

    1

    t

    1aaa

    aa

    aa

    =

    =++

    =++

    In analogy to first-order equations: try a function like:t

    ty

    2

    0

    2

    1

    c

    c2c

    c

    12211

    20112

    a,a,0a0c

    ===++=++

    ttt

    ttt

    yyayyycyc

    characteristic equation

    two solutions

    1.1 First and Second Order DifferenceEquation

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    Cases:

    (1) real-valued solutions:

    Combine both solution into:

    two constants, because two initial conditions are needed to solve a second-orderequation

    Convergence requires:

    (2) identical solutions:

    Since one only has one solution, one tests as a second solution

    Second-Order Linear Difference Equations

    04- 22

    1>= aa

    ttt AAy 2211 +=

    1,1 21

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    Prof. Dr. Thomas Lux, [email protected]

    (3) complex numbers with imaginary part:< 0

    Try a solution:

    would be real-value if A, A were complex conjugate!

    tt

    t i(Ai(Ay )) ++=

    ty

    212

    12121 42

    11

    2

    1 /, )aa(-ai ==

    Second-Order Linear Difference Equations

    S d O d Li Diff E i

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    Prof. Dr. Thomas [email protected]

    Coordinate transformation to polar coordinates:

    modulus or absolute value

    we can write:

    2122

    222

    sin,cos

    /)(r

    rrr

    +=

    +===

    tt

    t )ir(rA)ir(rAy sincossincos ++=

    Re()

    Im()

    r

    Second-Order Linear Difference Equations

    S d O d Li Diff E ti

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    Using de Moivres theorem:

    assume:

    real numbers

    Potential for true oscillatory motion already with two lags!period: 2/ , amplitude: depends on r

    [ ]

    [ ]iAAAAAA

    tAtAr

    tiAAtAArtitrAt)itrAy

    nin)i(

    t

    t

    tt

    t

    n

    )(,

    )sin()cos((

    )sin()()cos()()sin()(cos()sin()(cos(

    )sin()cos(sincos

    21

    21

    =+

    +=

    ++=++=

    =

    ==+

    =+=

    biAAaAA

    ibaAibaA

    2)(,2

    ,

    Second-Order Linear Difference Equations

    S d O d Li Diff E ti

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    Prof. Dr. Thomas Lux, [email protected]

    since:

    explosive > constant oscillations if = 1

    dampened 1 can be excluded by the condition: 1,2< 1

    0)1(,0)1(

    1)1(,1)1( 2121

    >>

    +=++=

    ff

    aafaaf

    212)( aaf ++=

    f()

    1-1

    Second-Order Linear Difference Equations

    Second Order Linear Difference Equations

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    Stability Conditions (sufficient and necessary):

    Stability can be checked without explicit solution of difference equation!

    Prof. Dr. Thomas Lux, [email protected]

    ( )

    ( ) 011)3(

    011)2(

    011)1(

    21

    2221

    21

    >+=

    >++=

    aaf

    aa

    aaf

    Second-Order Linear Difference Equations

    Second Order Linear Difference Equations

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    Solution of non-homogeneous equation: add particular solution like before

    example:

    y

    cccgttt

    ttt

    AAy

    gbcbcbcby

    gycycyc

    2102211

    012

    20112

    :

    ++

    ++=

    =++=

    =++

    Second-Order Linear Difference Equations

    Determination of constants

    via twoinitial conditions

    example: lead to

    ,

    21

    22111210

    10

    ,

    ),1(),0(

    AA

    yAAyyAAy

    ytyt

    ++=++=

    ==

    1 1 2 Economic Application: Multiplier

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    If second-order difference equation potentially generates sinusoidal fluctuations

    business cycle explanation

    Two lagged adjustment are in principle sufficient to generate economicfluctuation

    First model of the business cycle: Samuelson, 1939

    Prof. Dr. Thomas Lux, [email protected]

    1.1.2 Economic Application: MultiplierAccelerator Interaction

    :0

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    Model structure:

    consumption function with time lag, 0 < b< 1(multiplier)

    investment

    constant part: public expenditures

    induce investment: accelerator

    goods market equilibrium

    1.1.2 Economic Application: MultiplierAccelerator Interaction

    ttt

    ttt

    t

    ttt

    t-t

    IC Y

    CCkI

    GI

    III

    bYC

    +=

    =

    =

    +=

    =

    )5(

    )()4(

    )3(

    )2(

    )1(

    1

    1

    1.1.2 Economic Application: Multiplier

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    0)1(

    1

    )1(

    )1(

    )(

    )(

    """"

    2

    21

    211

    11

    21

    =++

    =

    ++=

    =++

    ++=

    =++=

    aa

    t

    t-t-t

    t-t-t-

    ttt-t

    bkkb

    b

    GY

    YbkYkbYYY

    GbkYYkbY

    GYYkbbY

    GCCkbYY

    particular solution: try

    stability: characteristic equation:

    1.1.2 Economic Application: MultiplierAccelerator Interaction

    Combine the equations:

    1.1.2 Economic Application: Multiplier-

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    Stability conditions:o.k.

    product of the roots

    o.k.

    Goods market equilibrium is stable, if

    Oscillations:

    1.1.2 Economic Application: MultiplierAccelerator Interaction

    2

    22

    )1(

    4if0

    4)1(

    /11

    0)1(1

    (?)01

    010)1(1

    k

    kb

    bkkb

    kbkb

    bkkb

    bk

    bbkkb

    +

    >

    >++

    1.1 First and Second Order Difference

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    Gandolfo,1997. Fig.6.1

    1.1 First and Second Order DifferenceEquation

    Prof. Dr. Thomas Lux, WSP1 Room 507, [email protected], +49 431 880-3661

    1.1.2 Economic Application: Multiplier

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    [ ]{ }

    0)1()1)(1()1(

    )1(

    0)1()1)(1()1()1(

    )1()1()1)(1()1(

    )1(

    )1(

    2

    20

    20

    22

    021

    0

    >+++++

    +=

    =++++++

    +=+++++

    +=

    +=

    t

    t

    tttt

    t

    t

    gbkgkbg

    gGy

    gGbkgkbgAg

    gGgbkAgkbgA

    gAy

    gGG

    fluctuations around growth path: assume

    for particular solution: try

    pp pAccelerator Interaction

    1.1.3 A First Look at Anticipation:

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    One finds that is valid particular solution since

    it satisfies the difference equation

    Assume g(t) is not a known function, but a sequence of (stochastic) realizations ofexogenous variables

    different approach for determination of particular solution: operational method

    use lag operator

    Application to first-order equation:

    pBackward and Forward Solutions

    )1(

    )1(

    1

    1

    tt

    tt

    tt-t

    xbLy

    xybL

    xbyy

    +=

    =+

    =+

    =

    ===

    0 0

    )()(i i

    it

    i

    t

    iixbxLby t

    1

    1

    1 ,, +

    === ttnttn

    tt yyLyyLyLy

    1.1.3 A First Look at Anticipation:

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    This holds because:

    Operator expansion like Taylor-series expansion about 0!

    Note: these sequences converge only if |b| < 1 or || < 1, i.e. if the system is stable

    If |b| > 1, consider:

    which also fulfills the difference equation and is bounded since |1/b| < 1.

    =

    =+++=0

    221 1)1i

    iiL...LLL-(

    pBackward and Forward Solutions

    it

    i

    i

    bt

    i

    i

    i

    bt xxLy +

    =

    = == 11

    1

    1

    )()(

    1.1.3 A First Look at Anticipation:

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    Particular solution here = geometrically declining sum of all past or future values of x0depending on whether the equation is stable or unstable

    alternative expansion:

    Derivation, reformulate and develop the second term in a Taylor series of

    Application: forward looking models are typically mathematically unstable, but can berepresented via second type of solution concept for particular solution.

    Backward and Forward Solutions

    )()11

    11

    =

    =i

    ii

    LL(

    11111 )1()1 =L

    LL(

    1.1.3 A First Look at Anticipation:

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    Illustration: Cobweb model

    equilibrium price is particular solution,

    since b < 0: improper oscillations

    stable if supply should have smaller slope than demand

    Backward and Forward Solutions

    1

    11

    1

    1

    1

    111

    111

    )(

    ,

    bb

    aa

    b

    bAp

    b

    aap

    b

    bp

    aapbbpSD

    pbaSbpaD

    t

    t

    tt

    tttt

    tttt

    +=

    =

    ==

    +=+=

    11 ++=

    =+++

    =+++

    =+++

    ==

    +

    ++

    xppp

    xppp

    pppp

    tttt

    tttt

    t

    e

    tt

    e

    tin a deterministic context: perfect foresight:

    homogeneous part leads to characteristic equation:

    Discriminant:

    Since a2=1stability conditions are violated; note:one stable, one unstable

    Backward and Forward Solutions

    1.1.3 A First Look at Anticipation:Backward and Forward Solutions

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    Particular solution: Operational method for knownsequence of

    for second-order equations:

    Denote by F = L-1the forward operator:

    with F1, F2roots of the polynomial which coincide with1, 2

    )(1

    1

    1

    )1(

    ))((

    21

    2

    2221

    2

    21

    2

    21

    2211

    21

    FFFF

    tt

    tt

    tttt

    aFaFLLaLa

    XLaLa

    y

    XyLaLa

    Xyayay

    =

    ++=++

    ++=

    =++

    =++

    Backward and Forward Solutions

    1

    1

    tt xX

    1.1.3 A First Look at Anticipation:Backward and Forward Solutions

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    it

    i

    it

    i

    i

    tLtLt

    LLLL

    XX

    XXy

    LL

    LLFLLF

    FFLLaLa

    =

    =

    +=

    +=

    =

    =+=

    =

    ==

    ==++

    0

    221

    0

    11

    11

    2

    111)1)(1(

    1

    21

    21

    212

    21

    ,

    )1)(1(

    ))((

    ))((1

    2

    2

    1

    1

    21

    2

    21

    1

    2

    2

    1

    1

    21

    Hence:

    and

    hence:

    backward solution

    Backward and Forward Solutions

    1.1.3 A First Look at Anticipation:Backward and Forward Solutions

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    or

    In the RE cobweb case: backward solution for stable root, forward solution for unstableroot.

    particular solution is geometrically weighted average of all past, present and (known)future shocks.

    ttt

    t

    it

    i

    iit

    i

    i

    it

    i

    iit

    i

    it

    it

    i

    iit

    i

    it

    pAAp

    XX

    XXp

    XXy

    i

    i

    ++=