Testing for Unit Roots in Nonlinear Dynamic Heterogeneous Panels

In this paper we present a unit root test against a nonlinear dynamic heterogenous panel with each cross section modelled as an LSTAR model. All parameters are viewed as cross section specific. We allow for serially correlated residuals over time and heterogenous variance among cross sections. The test is derived under three special cases: (i) the number of cross sections and observations over time are fixed, (ii) observations over time are fixed and the number of cross sections tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of cross sections. Small sample properties of the test show modest size distortions and satisfactory power being superior to the Im, Pesaran, and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for nonlinearities under the alternative hypothesis.Dynamic nonlinear heterogenous panels; Structural breaks; Unit roots; t-statistics; Central limit theorem;

Testing Parameter Constancy in Unit Root Autoregressive Models Against Continuous Change

In this paper we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots. The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory.Parameter constancy; LSTAR; Unit root; Brownian; motion; Strong mixing;

An application of the analogy between vector ARCH and vector random coefficient autoregressive models

In this paper we derive conditions for the conditional covariance matrix to be positive definite in a general vector ARCH model. The conditions can be easily extended to the diagonal vector GARCH model. For the general vector GARCH model, analytical expressions for the conditions in terms of the parameters become complicated, but their validity can in principle be checked numerically once the values of the parameters are given.conditional covariance matrix; multivariate GARCH; multivariate volatility model; random coefficient model; volatility forecasting

Inference for Unit Roots in a Panel Smooth Transition Autoregressive Model where the Time Dimension is Fixed

In this paper we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test.Dynamic nonlinear panel; Smooth transitions; Structural breaks; Unit roots; LSDV estimation; Central limit theorem;

Dickey-Fuller Type of Tests against Nonlinear Dynamic Models

In this paper we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power.Dickey-Fuller test; LSTAR(p); LSTART(p); Nonlinear trends; Parameter constancy; Unit root; Brownian motion;

Parameterizing Unconditional Skewness in Models for Financial Time Series

In this paper we consider the third-moment structure of a class of nonlinear time series models. Empirically it is often found that the marginal distribution of financial time series is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate for unconditional skewness. We consider modelling the unconditional mean and variance using models which respond nonlinearly or asymmetrically to shocks. We investigate the implications these models have on the third moment structure of the marginal distribution and different conditions under which the unconditional distribution exhibits skewness as well as nonzero third-order autocovariance structure. With this respect, the asymmetric or nonlinear specification of the conditional mean is found to be of greater importance than the properties of the conditional variance. Several examples are discussed and, whenever possible, explicit analytical expressions are provided for all third order moments and cross-moments. Finally, we introduce a new tool, shock impact curve, that can be used to investigate the impact of shocks on the conditional mean squared error of the return.asymmetry; GARCH; nonlinearity; stock impact curve; time series; unconditional skewness

Testing for unit roots in nonlinear dynamic heterogeneous panels

In this paper we present a unit root test against a nonlinear dynamic heterogenous panel with each cross section modelled as an LSTAR model. All parameters are viewed as cross section specific. We allow for serially correlated residuals over time and heterogenous variance among cross sections. The test is derived under three special cases: (i) the number of cross sections and observations over time are fixed, (ii) observations over time are fixed and the number of cross sections tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of cross sections. Small sample properties of the test show modest size distortions and satisfactory power being superior to the Im, Pesaran, and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for nonlinearities under the alternative hypothesis

Inference for unit roots in a panel smooth transition autoregressive model where the time dimension is fixed

In this paper we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test

Testing parameter constancy in unit root autoregressive models against continuous change

In this paper we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots. The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory

An application of the analogy between vector ARCH and vector random coefficient autoregressive models

In this paper we derive conditions for the conditional covariance matrix to be positive definite in a general vector ARCH model. The conditions can be easily extended to the diagonal vector GARCH model. For the general vector GARCH model, analytical expressions for the conditions in terms of the parameters become complicated, but their validity can in principle be checked numerically once the values of the parameters are given