In this paper we propose a stationary nonlinear dynamic functional coefficient panel data models with fixed effects and develops semiparametric estimation procedure using series approximation. Convergence rate and asymptotic distribution of the proposed series estimators are derived in which...

Structural breaks in relationships between macroeconomic and financial time series are likely a result of financial crises or local reforms. If such structural breaks exist, cointegration tests have to take them into account. Arai and Kurozumi (2007), Carrion-i-Silvestre and Sanso (2006) and...

Using Monte Carlo methods, the behaviour of the momentum threshold autoregressive (MTAR) unit root test of Enders and Granger (1998) is examined in the presence of structural breaks under the null. It is found that for level breaks the MTAR test exhibits similar behaviour to that derived by...

We propose a detailed Monte Carlo study of model selection criteria when the exact maximum likelihood (EML) method is used to estimate ARFIMA processes. More specifically, our object is to assess the performance of two automatic selection criteria in the presence of long-term memory: Akaike and...

This paper investigates if the recursive detrending method that works well for linear unit root tests also provides good outcomes for nonlinear unit root tests. It is found that the method improves the power of the nonlinear test when only a non-trending mean needs to be removed. The test,...

This paper introduces a Lagrange Multiplier (LM) test for testing an autoregressive structure in a binary time series model proposed by Kauppi and Saikkonen (2008). Simulation results indicate that the two versions of the proposed LM test have reasonable size and power properties when the sample...

Monte Carlo simulations are used to study the size and power properties of two stationarity tests developed by Kwiatkowski et al. (1992) [KPSS] and Leybourne and McCabe (1994) [LMC] when the data contain additive outliers. We show that the KPSS tests are very robust to additive outliers whereas...

Using efficient Monte Carlo methods, the performance of two-step Generalized Least Squares (GLS) estimators for the one-way error components models in small samples is analyzed. In our approach, we focus on the two-step GLS estimators provided by the programs LIMDEP, RATS and TSP, which mainly...

This paper considers the distance functional weight matrix in spatial autoregressive and spatial error models from a Bayesian point of view. We considered the Markov chain Monte Carlo methods to estimate the parameters of the models. Our approach is illustrated with simulated data set.

This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator proposed for least squares can be applied to the...