This paper argues that inferring long-horizon asset-return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable. We illustrate the problems that can arise by re-examining the findings of Bekaert and Hodrick (1992), who detected evidence of in-sample predictability in international equity and foreign exchange markets...

Traditional vector autoregressive (VAR) modeling theory has the defect that it can not effectively utilize the multiple time scale information contained in the inner of variables. In order to discuss multiscale behavior among economic variables and capture variables’ information in different time scale, multiresolution VAR model which can also be called as MVAR model has been established in the...

This paper considers the importance of moving average errors for the Johansen trace test of cointegrating rank based upon approximating vector autoregressions (VARs). Two cases are emphasized and explored, both taking as their starting point the Wold decomposition. Though each case defined satisfies the same cointegrating vector, one is a case of multicointegration while the other, based on res...

The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singular multivariate beta distributions allowing a fully conjugate Bayesian infere...

This paper presents a menu-driven RATS-program which allows to identify structural shocks in vector-autoregressive (VAR) models. Identification is achieved by imposing short-run or long-run restrictions (or a combination of both) on the structural form of a model. The only requirement is that the matrix of restrictions consisting of rows of the restricted matrices of short-run and long-run effe...

In this paper, Bayesian estimation and hypothesis testing are introduced for identified normalized Vector Autoregressive (VAR) models. A class of priors is proposed to take advantage of the structure of normalized VAR models. Efficient Markov Chain Monte Carlo algorithms are used for sampling from the posterior of the VAR parameters without using Metropolis algorithms. Marginal likelihoods are ...

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the d...

In cointegrated vector autoregressive (VAR) analyses various nonlinear functions of the co-eecients are of interest. Notable examples are impulse responses. A general theory for asymptotic inference on such functions is developed under the assumption that the actual data generation process (DGP) is of potentially innnite VAR order although nite order VAR models are tted to the data and are used...

Abstract Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization management system. However, it’s not easy task due to dynamic changes in volume, its composition or unpredictable factors. Initially, mainly conventional descriptive statistical models with demographic socioeconomic factors were used. Methods based on machine learnin...