The purpose of this work is to study a discrete-time nonlinear business cycle model of the Kaldor-type. The model is an extended Kaldor model and it is described by a two-dimensional dynamical system with income and capital as variables. We check the orbits of the system, their changes related to changes of the system parameters and their basins of attraction in order to understand the dynamic features of the model

Grácio, Clara

Januario, Cristina

Ramos, José Sousa

English

Repositório Científico da Universidade de Évora

10      Clara Grácio, Cristina Januário and J. Sousa Ramos Chaotic behavior in an economic model     9Chaotic behavior in an economic modelClara Grácio, Cristina Januário and J. Sousa RamosDepartment of Mathematics, Universidade de Évora, Rua Romão Ramalho, 59, 7000-585 Évora, Portugal. E-mail: mgracio@uevora.ptDepartment of Chemistry, Mathematics Unit, Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal. E-mail: cjanuario@deq.isel.ipl.ptDepartment of Mathematics, Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal. E-mail: sramos@math.ist.utl.ptAbstractThe purpose of this work is to study a discrete-time nonlinear business cycle model of the Kaldor-type. The model is an extended Kaldor model and it is described by a two-dimensional dynamical system with income and capital as variables. We check the orbits of the system, their changes related to changes of the system parameters and their basins of attraction in order to understand the dynamic features of the model.1. IntroductionThe complexity of most important and interesting economic phenomena can only be explained by the use of nonlinear models. In favor of linearity and the main reason for its adoption is its simplicity. But simplicity is sometimes not enough, particularly in the case of macrodynamics and economic fluctuations. As a result, many economists have insisted that non-linear structures should be employed instead. It is impossible to ignore that non-linear dynamical structures are clearly the more general and common case and restricting attention to linear structures limits the type of dynamics that are possible.In what follows a discrete-time economic model is considered. It is a particular case of the Kaldor-type business cycle model and it is described by a two-dimensional dynamical system. In this work, it is suggested an extension of the model proposed by Bischi, Dieci, Rodano, Saltari (2001) and Dieci, Bischi and Gardini (2004). We can find the original Kaldor’s model in Kaldor (1940) as an elementary example of the limit cycle in macroeconomic models and as one of the early non-linear models.Consider the following discrete-time version of the Kaldor model: ,	(1)where the variables Y, K, I and S represent, respectively, the income, the capital stock, the investment and savings. We have that the change of income, between two consecutive instants, is proportional to the difference between in investment and savings and α (α>0) is the parameter of proportionality. The capital stock in the next period is equal to new investment plus what remains after depreciation. The parameter, δ, represents the capital stock depreciation rate (0<δ<1). The investment function is assumed to take the form of an increasing arc tangent type function of income, like proposed in Bischi, Dieci, Rodano, Saltari (2001) and Dieci, Bischi and Gardini (2004). Since Kaldor concluded, it might be sensible to assume that the S and I curves are non-linear, in general, he assumed I = I(Y, K) and S = S(Y, K), where investment and savings are non-linear functions of income and capital. The introduction of nonlinearity to the capital stock variable is done, considering it a decreasing trigonometric function of the sin type (Fig. 1.), instead of being just a linear decreasing function. Therefore, ,	(2)where the parameters σ, δ, β, γ1, μ, and σ are such that 0<σ, δ<1 and β, γ1, μ, σ>0. Fig. 1.  Effect of the nonlinearity in the capital stock, K.Concerning savings, they are assumed to be nonlinear. So, it depends also on the capital stock in a decreasing way: ,	(3)with γ2 a positive parameter (γ2>0).Considering the particular case, when γ1 = γ2 = γ, for convenience, and replacing expressions (2) and (3) in (1), we get the following two-dimensional map in income and capital stock variables:	(4)2. The model and some considerations about two-dimensional mapsLet us change the notation of the model (4) to y:=Y and k:=K. Consider the family of two-dimensional nonlinear maps, , given by 	(5)where, α, δ, β, γ, μ and σ  are real parameters such that α, β, γ, μ>0 and 0<σ, δ<1.The map is continuously differentiable and has the structure of a triangular map (skew map) since it has the form F(y, k) = (f(y), g(y, k)), that is, the variable y does not depend on k. From the economic point of view, that means that the dynamic of income is only affected by income itself. The first component of the map is called the basis map and the second is the fiber map. In  Fig. 2. it is shown the graphical representation of the map F for some values of the parameters.  Fig. 2.  Graphical representation of the map F for some values of the parameters. In plot a) α =8.9, μ=100, σ=0.3, δ=0.3, β=9.5, γ=1.0 and in plot b) α =10.0, μ=100, σ=0.5, δ=0.4, β=9.5, γ=0.8.As a consequence of the triangular structure, the dynamics of the map F is deeply influenced by the dynamics of the one-dimensional basis map, f(y). In particular, many of its bifurcations are associated to those of the one-dimensional. The map f is a bimodal map when ασ>1. Fig. 3.  Graphical representation of the map f when ασ>1.In this case the turning points are given by  and   .	(7)Cm represents the relative minimum and cM the relative maximum.To begin with, we present the bifurcation diagrams of the map f, when the parameters α and σ change:  Fig. 3.  Bifurcation diagram of the map f as a function of α (plot a)) and as a function of σ (in plot b)), for μ=100, σ=0.2 with the initial conditions cm and cM.The bifurcation diagrams above shows the dynamic properties of the map f. The long term behavior of the income variable, y, is deeply influenced by both the parameters.To study the dynamic behavior of the map F, first it is important to make some considerations about two-dimensional maps. Let us first define critical curve of rank -1, LC-1. When a map is continuous and continuously differentiable, then LC-1 is included in the set of points at which de jacobian determinant vanishes, that is, ,	(6)where J denotes the jacobian matrix and the symbol, |.|, the determinant.The curve LC-1 is the two-dimensional analogue of the points of local extrema of a differentiable one-dimensional map. Therefore, the iteration of the critical lines can give us important information about the dynamic features of the model. The curve LC satisfies F-1(LC)LC-1 and T(LC-1)=LC. The rank k image of LC is a critical curve of rank k+1, LCk=Fk(LC). For more detailed information see Mira, Gardini, Barugola, Cathala (1996).In this case, the jacobian matrix has the form	(6)and the roots of its determinant are given by:   and   .	(7)Obviously, the two first zeros correspond to the solutions of the equation f´(y)=0 (local extrema of the basis map). From the geometric point of view, c1 and c2 correspond to vertical straight lines and c3 symbolizes the infinite horizontal straight lines (see Fig. 3.).Fig. 3.  Some critical curves for the values of the parameters: α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8.The first iteration of LC-1 gives origin to the following pictures:    Fig. 4.  The result of the first iteration of the critical lines. In plot a) the vertical straight lines and in plot b) one of the horizontal straight lines. The values of the parameters are: α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8.Numerical simulations show that the successive images of the critical lines corresponding to c1 and c2 are always. Concerning the critical lines corresponding to c3, they start to appear very complex (see Fig. 5.).Fig. 5.  The result of five iterations of one horizontal critical lines. The values of the parameters are: α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8.If we iterate all the critical lines a sufficient great number of times, we get the following picture:Fig. 6.  Asymptotic behavior of the critical line for the values of the parameters:   α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8.The figure shows that, asymptotically, the critical lines “end” at 6 points.Such sets of points are called ω-limit sets. That is, the ω-limit set of a point x corresponds to the points q such that the points Fnk (x) (k=1, 2,…) tend to q (clearly, q belongs to the limit set of the trajectory). The set ω(x) is invariant and can give us information about the long run behavior of the trajectory from x. The points in the figure correspond precisely to the points of the orbit of period 2 or period 4 of the attractor, depending on the considered initial condition.The points with the same x-coordinate (that is, in the same fiber) are due to the fact that, for these parameter values, the basis map converges to one of the two existing fix points, depending on the starting point, cm or cM. Then, in each fiber, the asymptotic behavior of the fiber map converges to a period-2 or period-4 orbit (Fig. 7. and Fig. 8.).    Fig. 7.  Representation of the (a) income and (b) capital stock as a function of time, with initial condition (c2, 30.0). The values of the parameters: α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8. Fig. 8.  Representation of the (a) income and (b) capital stock as a function of time, with initial condition (c2, 30.0). The values of the parameters: α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8. Fig. 9.  The period-2 (in plot a)) and period-4 (in plot b)) orbits in each fiber. The values of the parameters are α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8.Therefore, it seams pertinent to illustrate in a color scheme the initial conditions that, by iteration of F, define a trajectory that leads to each of the two periodic orbits. The regions colored with the same color correspond to the basins of attraction of one orbit. The dark grey color corresponds to the period-2 orbit and the light grey color to the period-4 orbit.   Fig. 9.  Basins of attraction. The values of the parameters are: α =10.0, μ=100, σ=0.2, δ=0.4, β=9.5, γ=0.8.The figures show light and dark grey vertical stripes revealing the two types of regions mentioned above.   3. ConclusionsIn this work it was given a contribution to understand economic fluctuations with business cycle models. 5. References Bischi GI.; Dieci R.; Rodano R.; Saltari S. 2001, Multiple attractors and global bifurcations in a Kaldor-type business cycle model, J. Evol Econ, no. 11, pp. 527-554.Dieci R.; Bischi GI.; Gardini, L. 2004, From bi-stability to chaotic oscillations in a macroeconomic model, Chaos Solitons Fractals, 21, no. 2, pp. 403-412.Kaldor N. 1940, The Model of the Trade Cycle. Economic Journal no. 50, pp. 78-92.Mira C; Gardini L.; Barugola A; Cathala J-C 1996 Chaotic dynamics in two-dimensional noninvertible maps, World Scientific Publishing Co. Pte. Ltd,  

Chaotic behavior in an economic model

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Chaotic behavior in an economic model

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