In this paper we propose an alternative method for investigating the sources behind the behavior of real wages and unemployment. The statistical model we study is a certain structural error correction model, a so called common trends model, which has become popular in the empirical...

In this paper we use Monte Carlo testing techniques for testing linearity against the smooth transition models. The Monte Carlo approach allows us to introduce a new test that differs from the tests existing in the literature in two respects. First, the test is exact in the sense that the...

This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap...

In this paper two simple tests to distinguish between unit root processes and stationary nonlinear processes are proposed. New limit distribution results are provided, together with two F type test statistics for the joint unit root and linearity hypothesis against a specific nonlinear...

Simulations are used to check the probability of detecting a time-varying equilibrium correction by applying the existing tests of no cointegration and parameter constancy. Smooth transition regressions are chosen to describe the nonlinearity and the Johansen cointegration test and the Lin and...

This paper investigates the small sample size and power properties of the likelihood ratio test in the seasonal error correction model. Two specifications of the model at the annual frequency are analyzed. One is more restricted (RS), designed for the particular case of 'synchronous...

The purpose of this paper is to use the bootstrap resampling technique to calculate confidence intervals for efficiency measures and Malmquist productivity indices. The efficiency and productivity measures are obtained from non-parametric linear programming models using primal production data....