This note investigates long-run exclusion in a cointegrated vector autoregressive (VAR) model from the viewpoint of …nite-sample statistical inference. Monte Carlo experiments show that, in various circumstances, a mis-speci…ed partial VAR model, which is justi…ed by the existence of a long-run excluded variable, can lead to better …nite-sample inference for cointegrating rank than a fully-spec...

We introduce the new time series analysis features of scikits.statsmodels. This includes descriptive statistics, statistical tests and several linear model classes, autoregressive, AR, autoregressive moving-average, ARMA, and vector autoregressive models VAR.

It is well known that many countries around the world depend on the US as their major trade partner. As a result, if something does happen to US economy it surely will affect the economy of all these countries. In this study, we investigate the relationship between the US and four Asian emerging stock markets namely Hong Kong, India, South Korea and Malaysia using monthly data between 1996 and ...

Real economic data always present nonlinear properties such as asymmetry and radically change in the series through time. Missing data and jumps as well as breaks also common reported in economic time series model. Thus, linear models are no longer suitable used in estimate the economic data and markov switching vector autoregressive model (MS-VAR) is applied in study the economic model. This p...

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly a...

This article compares the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian vector error correction (BVEC) models in forecasting the exchange rates for ve Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Polish Zloty, Slovak Koruna and Slovenian Tolar) agains...

This paper pursues an econometric investigation of the interactions between Japan’s housing investment and gross domestic product (GDP). A cointegrated vector autoregressive (VAR) analysis of Japan’s recent time series data reveals two cointegrating relationships, which characterize the underlying long-run interactions of the variables in question. The cointegrated VAR model is then reduced to ...

It is well known that in a vector autoregressive (VAR) model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that if this assumption is violated, then the characterization of Granger non-causality in a VAR model fail...

The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VARmodel implicitly captures subsurface forcing ob...

This paper proposes a Vector Autoregressive (VAR) model as a new technique for missing feature reconstruction in ASR. We model the spectral features using multiple VAR models. A VAR model predicts missing features as a linear function of a block of feature frames. We also propose two schemes for VAR training and testing. The experiments on AURORA-2 database have validated the modeling methodolo...