Panel Smooth Transition Regression Models
Panel Smooth Transition Regression Models
By Andrés Gonzalez, Dick van Dijk, and Timo Terasvirta
SSE/EFI working paper series in economics and finance, n° 604. (2005)
Abstract Paper

Gilbert  Colletaz

University of Orleans

France

Coder Page  

Estimates a Panel Smooth Transition Regression Model with fixed effects, one transition function with one or two location parameters as developped in Gonzales, Terasvirta van Dijk : Panel Smooth Transition Regression Models, SSE/EFI Working Paper Series in Economic and Finance, n° 64, 2005.
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Abstract
We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with re- gression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an ob- servable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parame- ter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms’ investment decisions in the presence of capital market imperfections.
Gonzalez, A., D. van Dijk, and T. Terasvirta, "Panel Smooth Transition Regression Models", SSE/EFI working paper series in economics and finance, n° 604..
N
N
T
T
Dependent variable
Dependent variable
n(q)
n(q)
Threshold Variables
Threshold Variables
n(x)
n(x)
Explicatives variables X
Explicatives variables X
n(z)
n(z)
Explicatives variables Z
Explicatives variables Z
LMF
LMF
Select
Select
m
m
Waiting time

Please cite the publication as :

Gonzalez, A., D. van Dijk, and T. Terasvirta, "Panel Smooth Transition Regression Models", SSE/EFI working paper series in economics and finance, n° 604..

Please cite the companion website as :

Gonzalez, A., D. van Dijk, and T. Terasvirta, "Panel Smooth Transition Regression Models", RunMyCode companion website, http://www.execandshare.org/CompanionSite/Site370

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Variable/Parameters Description, constraint Comments
N
    Number of individuals, it gives the cross-section dimension
    T
      T denotes the time dimension of the panel
      Dependent variable
        This (TxN,1) vector contains the observations of the dependent variable y.The data are stacked cross-unit by cross-unit, such as y=(y1',y2',...,yN')'. If there are missing values, they have to be represented by "."
        n(q)
          number of transition variables. If "select = yes", an optimal transition variable is picked among these. If "select = no", the first one is used as the transition variable, the others being only considered for the test of no remaining heterogeneity.
          Threshold Variables
            A (NxT,n(q)) matrix, each column being the observations of a potential transition variable, if "select = yes".
            n(x)
              number of explicatives variables whose coefficients are changing over time and over individuals. These coefficients are continous functions of the transition variable through the logistic specification of the transition function g().
              Explicatives variables X
                A (NxT,n(x)) matrix, each of its column containing observations of an explicative variable whose coefficient is varying according to the transition function.
                n(z)
                  Number of explicative variables whose coefficients are constant over time and over individuals, i.e. they are not submitted to the transition effect.
                  Explicatives variables Z
                    A [(NxT,n(z)] matrix. Each column is filled by observations of an explicative variable whose coefficient is invariant in time and between individuals.
                    LMF
                      Version of the F-statistics (standard or Robustified)
                      Select
                        estimates a model specified a priori (Select=no) or let the program chooses the number of location parameters and eventually, the threshold variable (Select=Yes).
                        m
                          Gives the number of location parameters (1 or 2) in the transition function. This value must be specify if Select=no.
                          Variable/Parameters Description Visualisation
                          N The data are issued from Coletaz and Hurlin (2006), "Threshold Effects in the Public Capital Productivity: an International Panel Smooth Transition Approach". In this paper, the authors examine the threshold effects in the productivity of the public capital stock for a panel of 21 OECD countries observed over 1965-2001, using a PSTR model. So here, N=21.
                          T annual data obseved from 1966 to 2001, so here T=36.
                          Dependent variable The dependent variable is the productivity of private capital stock, i.e. y(t)-k(t) where y(t) is the log of GDP and k(t) the log of private capital stock. For more details, see Colletaz and Hurlin (2006), “Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach”, working paper, Laboratoire d'Economie d'Orléans, 2006.
                          n(q) The transition variable is the lagged value of the log ratio of public to private capital, i.e. g(t-1)-k(t-1).
                          Threshold Variables idc
                          n(x) two explicatives variables in the demo data
                          Explicatives variables X The two explicatives variables are the log of the ratio of employment over private capital [n(t)-k(t)] and the log of the ratio of public to private capital stocks [g(t)-k(t)].
                          n(z) In this demo data, all explicative variables have coefficients varying with the transition function. So here n(z)=0.
                          Explicatives variables Z In this demo, all explicatives variables have coefficients varying with the transition function. So there is no "z" matrix.
                          LMF This demo uses the robustified version of the F-statistics when needed.
                          Select Let the program chooses m=1 or m=2
                          m As Select=yes, we don't have to specify a value for m as the program will select an optimal number of location parameters.
                          Panel Smooth Transition Regression Models
                          G. Colletaz (2013)
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