With the purpose of analyzing non-stationary time series, this paper innovates the nonlinear cointegration discriminate analysis by introducing support vector machine and innovated feature-weighting model to overcome existing limitations of two methods, that is, the statistical approach and the neural network used by the nonlinear cointegration theory. Then, the application of the innovated met...

Fractional cointegration is viewed from a semiparametric viewpoint as a narrow-band phenomenon at frequency zero. Recent semiparametric methods of inference on memory parameters are developed to explore the possibility of fractional cointegration by means of testing the memory of observables and also new tests for the presence of fractional cointegration. These, along with narrow band estimates...

A neglected aspect of the otherwise fairly well developed Bayesian analysis of cointegration is the point estimation of the cointegration space. It is pointed out here that, due to the well known non-identification of the cointegration vectors, the parameter space is not an inner product space and conventional Bayes estimators therefore stand without their usual decision theoretic foundation. W...

This paper proposes Bayesian estimation of cointegrated VAR systems and a simple method of estimating the cointegration rank using the Bayes factors for the adjustment term. Monte Carlo experiments show that the method proposed is more powerful in selecting the rank, especially with a small sample size, than Johansen's LR test. This is due to the fact that Bayesian analysis uses the exact distr...

The concept of cointegration is widely used in applied non-stationary time series analysis to describe the co-movement of data measured over time. In this paper, we proposed a Bayesian model for cointegration test and analysis, based on the dynamic latent factor framework. Efficient computational algorithms are also developed based on Markov Chain Monte Carlo (MCMC). Performance and efficiency ...

This paper provides a theoretical fractional cointegration analysis in a nonparametric framework. We solve a generalized eigenvalues problem, extending Bierens’ (1997) approach. To this end, a couple of random matrices is constructed, distinguishing between stationary and nonstationary part of the fractional integrated process. The asymptotic behavior of such matrices is studied and convergence...

By undertaking a cointegration analysis with annual data over the period 1985~2005 in China, the estimation results show that there is cointegration relationship between electrical energy consumption and economic growth taking into account industry structure changes and technical efficiency. The model shows that three explanatory variables, the GDP per capita, heavy industry share and efficienc...

Cointegration analysis involves the solution of a generalized eigenproblem involving moment matrices and inverted moment matrices. These formulae are unsuitable for actual computations because the condition numbers of the resulting matrices are unnecessarily increased. Our note discusses how to use the structure of the problem to achieve numerically stable computations, based on QR and singular...

This paper proposes new iterative reduced-rank regression procedures for seasonal cointegration analysis. The suggested methods are motivated by the idea that modelling jointly the cointegration restrictions at the different frequencies may induce some efficiency gain in finite samples. Monte Carlo simulations indicate that the new tests and estimators perform well with respect to already exist...