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.

In this paper a Bayesian approach to unit root testing for panel data models is proposed based on the comparison of stationary autoregressive models with and without individual deterministic trends, with their counterpart models with a unit autoregressive root. This is done under cross-sectional dependence among the units of the panel. Simulation experiments are conducted with the aim to assess...

 We consider the problem of model selection in vector autoregressive model with Normal innovation. Tests such as Vuong's and Cox's tests are provided for order and model selection, i.e. for selecting the order and a suitable subset of regressors, in vector autoregressive model. We propose a test as a modified log-likelihood ratio test for selecting subsets of regressors. The Europe oil prices, ...

Autoregressive models factorize a multivariate joint probability distribution into a product of one-dimensional conditional distributions. The variables are assigned an ordering, and the conditional distribution of each variable modelled using all variables preceding it in that ordering as predictors. Calculating normalized probabilities and sampling has polynomial computational complexity unde...

This correspondence addreskes the problem of order determination of autoregressive models by Bayesian predictive densities. A criterion is derived employing noninformative prior densities of the model parameters. The form of the obtained criterion coincides with that of Rissanen in 1161. Simulation results are presented which demonstrate the good performance of the criterion, and comparisons wi...

It is well known that standard estimators of an AR(p) model are biased in finite samples, yet little is done in practice to remove the bias. Apparently small biases have important implications for the estimation of the impulse response function, which is a nonlinear function of the original coefficients. This note shows how to obtain estimators adjusted for first order bias based on Stine and S...