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
Variable/Parameters  Description, constraint  Comments 

N  Number of individuals, it gives the crosssection 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 crossunit by crossunit, 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 Fstatistics (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 19652001, 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(t1)k(t1).  
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 Fstatistics 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. 
Computing Date  Status  Actions 

Gilbert Colletaz
University of Orleans
France
Gilbert Colletaz also created these companion sites
Backtesting ValueatRisk: A GMM Durationbased Test
Abstract
This paper proposes a new durationbased backtesting procedure for VaR forecasts. The GMM test framework proposed by Bontemps (2006) to test for the distributional assumption (i.e. the geometric distribution) is applied to the case of the VaR forecasts validity. Using simple Jstatistic based on the moments defined by the orthonormal polynomials associated with the geometric distribution, this new approach tackles most of the drawbacks usually associated to duration based backtesting procedures. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypothesis (Christoffersen, 1998). Third, MonteCarlo simulations show that for realistic sample sizes, our GMM test outperforms traditional duration based test. Besides, we study the consequences of the estimation risk on the durationbased backtesting tests and propose a subsampling approach for robust inference derived from Escanciano and Olmo (2009). An empirical application for Nasdaq returns confirms that using GMM test leads to major consequences for the expost evaluation of the risk by regulation authorities.
Colletaz,
G.,
B.
Candelon,
C.
Hurlin,
and
S.
Tokpavi,
"Backtesting ValueatRisk: A GMM Durationbased Test",
Journal of Financial Econometrics, 9(2), 314343 .
Authors:
Candelon
Colletaz Hurlin Tokpavi
Coders:
Colletaz
Candelon Hurlin Tokpavi Last update
06/28/2012
Ranking
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Runs
23
Visits
291

Asymptotic DistributionFree Diagnostic Tests For Heteroskedastic Time Series
Abstract
This article investigates model checks for a class of possibly nonlinear heteroskedastic time series models, including but not restricted to ARMAGARCH models. We propose omnibus tests based on functionals of certain weighted standardized residual empirical processes. The new tests are asymptotically distributionfree, suitable when the conditioning set is infinitedimensional, and consistent against a class of Pitman’s local alternatives converging at the parametric rate n1/2, with n the sample size. A Monte Carlo study shows that the simulated level of the proposed tests is close to the asymptotic level already for moderate sample sizes and that tests have a satisfactory power performance. Finally, we illustrate our methodology with an application to the wellknown S&P 500 daily stock index. The paper also contains an asymptotic uniform expansion for weighted residual empirical processes when initial conditions are considered, a result of independent interest.
Colletaz,
G.,
"Asymptotic DistributionFree Diagnostic Tests For Heteroskedastic Time Series",
Econometric Theory, 26(03), 744773.
Authors:
Escanciano
Coders:
Colletaz
Last update
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Ranking
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Runs
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The Risk Map: A New Tool for Validating Risk Models
Abstract
This paper presents a new tool for validating risk models. This tool, called the Risk Map, jointly accounts for the number and the magnitude of extreme losses and graphically summarizes all information about the performance of a risk model. It relies on the concept of ValueatRisk (VaR) super exception, which is defined as a situation in which the loss exceeds both the standard VaR and a VaR defined at an extremely low coverage probability. We then formally test whether the sequences of exceptions and super exceptions is rejected by standard model validation tests. We show that the Risk Map can be used to validate market, credit, operational, or systemic (e.g. CoVaR) risk estimates or to assess the performance of the margin system of a clearing house.
Colletaz,
G.,
C.
Hurlin,
and
C.
Perignon,
"The Risk Map: A New Tool for Validating Risk Models",
SSRN.
Authors:
Colletaz
Hurlin Perignon
Coders:
Colletaz
Hurlin Perignon Last update
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Ranking
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Runs
146
Visits
431

Network Effects and Infrastructure Productivity in Developing Countries
Abstract
This paper proposes to investigate the threshold effects of the productivity of infrastructure investment in developing countries within a panel data framework. Various speci.cations of an augmented production function that allow for endogenous thresholds are considered. The overwhelming outcome is the presence of strong threshold effects in the relationship between output and private and public inputs. Whatever the transition mechanism used, the testing procedures lead to strong rejection of the linearity of this relationship. In particular, the productivity of infrastructure investment generally exhibits some network effects. When the available stock of infrastructure is very low, investment in this sector has the same productivity as noninfrastructure investment. On the contrary, when a minimumnetwork is available, the marginal productivity of infrastructure investment is generally largely greater than the productivity of other investments. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.
Candelon,
B.,
G.
Colletaz,
and
C.
Hurlin,
"Network Effects and Infrastructure Productivity in Developing Countries",
Maastricht University.
Authors:
Candelon
Colletaz Hurlin
Coders:
Candelon
Colletaz Hurlin Last update
03/14/2013
Ranking
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Runs
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A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR
Abstract
In this paper, we propose a theoretical and empirical comparison of two popular systemic risk measures  Marginal Expected Shortfall (MES) and Delta Conditional Value at Risk (ΔCoVaR)  that can be estimated using publicly available data. First, we assume that the timevarying correlation completely captures the dependence between firm and market returns. Under this assumption, we derive three analytical results: (i) we show that the MES corresponds to the product of the conditional ES of market returns and the timevarying beta of this institution, (ii) we give an analytical expression of the ΔCoVaR and show that the CoVaR corresponds to the product of the VaR of the firm's returns and the timevarying linear projection coefficient of the market returns on the firm's returns and (iii) we derive the ratio of the MES to the ΔCoVaR. Second, we relax this assumption and propose an empirical comparison for a panel of 61 US financial institutions over the period from January 2000 to December 2010. For each measure, we propose a crosssectional analysis, a timeseries comparison and rankings analysis of these institutions based on the two measures.
Benoit,
S.,
G.
Colletaz,
C.
Hurlin,
and
C.
Perignon,
"A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR",
SSRN.
Authors:
Benoit
Colletaz Hurlin Perignon
Coders:
Benoit
Colletaz Hurlin Perignon Last update
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Ranking
53
Runs
181
Visits
398

A Generalized Asymmetric Studentt Distribution with Application to Financial Econometrics
Abstract
This paper proposes a new class of asymmetric Studentt (AST) distributions, and investigates its properties, gives procedures for estimation, and indicates applications in financial econometrics. We derive analytical expressions for the cdf, quantile function, moments, and quantities useful in financial econometric applications such as the Expected Shortfall. A stochastic representation of the distribution is also given. Although the AST density does not satisfy the usual regularity conditions for maximum likelihood estimation, we establish consistency, asymptotic normality and efficiency of ML estimators and derive an explicit analytical expression for the asymptotic covariance matrix. A Monte Carlo study indicates generally good finitesample conformity with these asymptotic properties.
Colletaz,
G.,
"A Generalized Asymmetric Studentt Distribution with Application to Financial Econometrics",
Journal of Econometrics, 157, 297305.
Authors:
Zhu
Galbraith
Coders:
Colletaz
Last update
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Ranking
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Runs
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Visits
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Testing for Unit Roots in the Presence of Uncertainty Over Both the Trend and Initial Condition
Abstract
In this paper we provide a joint treatment of two major problems that surround testing for a unit root in practice: uncertainty as to whether or not a linear deterministic trend is present in the data, and uncertainty as to whether the initial condition of the process is (asymptotically) negligible or not. We suggest decision rules based on the union of rejections of four standard unit root tests (OLS and quasidifferenced demeaned and detrended ADF unit root tests), along with information regarding the magnitude of the trend and initial condition, to allow simultaneously for both trend and initial condition uncertainty.
Colletaz,
G.,
"Testing for Unit Roots in the Presence of Uncertainty Over Both the Trend and Initial Condition",
Journal of Econometrics, 169, 18895.
Authors:
Harvey
Leybourne Taylor
Coders:
Colletaz
Last update
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Other Companion Sites on same paper
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Testing for Granger Causality in Heterogeneous Mixed Panels
Abstract
In this paper, we propose a simple Granger causality procedure based on Meta analysis in heterogeneous mixed panels. Firstly, we examine the finite sample properties of the causality test through Monte Carlo experiments for panels characterized by both crosssection independency and crosssection dependency. Then, we apply the procedure for investigating the export led growth hypothesis in a panel data of twenty OECD countries.
Emirmahmutoglu,
F.,
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Unit Root Tests for Panel Data
Abstract
This paper develops unit root tests for panel data. These tests are devised under more general assumptions than the tests previously proposed. First, the number of groups in the panel data is assumed to be either finite or infinite. Second, each group is assumed to have different types of nonstochastic and stochastic components. Third, the time series spans for the groups are assumed to be all different. Fourth, the alternative where some groups have a unit root and others do not can be dealt with by the tests. The tests can also be used for the null of stationarity and for cointegration, once relevant changes are made in the model, hypotheses, assumptions and underlying tests. The main idea for our unit root tests is to combine pvalues from a unit root test applied to each group in the panel data. Combining pvalues to formulate tests is a common practice in metaanalysis. This paper also reports the finite sample performance of our combination unit root tests and Im et al.'s [Mimeo (1995)] tbar test. The results show that most of the combination tests are more powerful than the tbar test in finite samples. Application of the combination unit root tests to the postBretton Woods US real exchange rate data provides some evidence in favor of the PPP hypothesis.
Hurlin,
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Coders:
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Testing for Unit Roots in Heterogeneous Panels
Abstract
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized tbar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite nonnegative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized tbar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the tbar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
Hurlin,
C.,
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Testing for a Unit Root in Panels with Dynamic Factors
Abstract
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Hurlin,
C.,
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