scholarly journals Forecasting Performances of the Reduced Form VAR and Sims-Zha Bayesian VAR Models when the Multiple Time Series are Jointly Influenced by Collinearity and Autocorrelated Error

2018 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Benjamin Agboola Oyejola
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Var Models ◽  
Short Term ◽  
Series Length ◽  
Bayesian Var ◽  
Autocorrelated Error ◽  
Different Levels

The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.

Short-Term Reactive Power Load Forecasting Using Multiple Time-Series Model

2003 ◽  
Vol 36 (20) ◽  
pp. 985-990 ◽  
Author(s):  
Jong-Man Cho ◽  
Jin-Hack Kim ◽  
Woo-Hyun Park ◽  
Yun-ho Lee ◽  
Jin-O Kim
Keyword(s):  
Time Series ◽  
Reactive Power ◽  
Load Forecasting ◽  
Time Series Model ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Economic Relationships Between Selling and Rental Prices in the Italian Housing Market

2021 ◽  
Vol 15 ◽  
pp. 78-83
Author(s):  
Pierluigi Morano ◽  
Benedetto Manganelli ◽  
Francesco Tajani
Keyword(s):  
Time Series ◽  
Housing Market ◽  
Real Estate Market ◽  
Data Series ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Short Term ◽  
Macroeconomic Variables ◽  
Economic Relationships ◽  
The Relationship

In this paper the relationship between price and rent dynamics in the Italian housing market is studied. The aim is reached through the implementation of a multivariate autoregressive model (VAR), that makes it possible to explain the interdependencies of multiple time series. The analysis considers a series of macroeconomic variables in the model that in the deductive interpretation of the phenomenon and on the basis of other experiences in current literature, were evaluated as potential keys to understanding the relationship between prices and rents. The variables selected, along with residential realestate prices and real residential rents, were: the real short term interest rate, the time series of the annual differences between the actual and the expected Gross Domestic Product, real investments in housing. The data series cover the period from 1980 to 2008. The results obtained show some peculiarities of the Italian real estate market.

Bayesian Vector Autoregressions: Estimation

2019 ◽  
Author(s):  
Silvia Miranda-Agrippino ◽  
Giovanni Ricco
Keyword(s):  
Time Series ◽  
Posterior Distribution ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Var Models ◽  
Conjugate Prior ◽  
Vector Autoregressions ◽  
Posterior Probability Distribution ◽  
Joint Dynamics

Vector autoregressions (VARs) are linear multivariate time-series models able to capture the joint dynamics of multiple time series. Bayesian inference treats the VAR parameters as random variables, and it provides a framework to estimate “posterior” probability distribution of the location of the model parameters by combining information provided by a sample of observed data and prior information derived from a variety of sources, such as other macro or micro datasets, theoretical models, other macroeconomic phenomena, or introspection. In empirical work in economics and finance, informative prior probability distributions are often adopted. These are intended to summarize stylized representations of the data generating process. For example, “Minnesota” priors, one of the most commonly adopted macroeconomic priors for the VAR coefficients, express the belief that an independent random-walk model for each variable in the system is a reasonable “center” for the beliefs about their time-series behavior. Other commonly adopted priors, the “single-unit-root” and the “sum-of-coefficients” priors are used to enforce beliefs about relations among the VAR coefficients, such as for example the existence of co-integrating relationships among variables, or of independent unit-roots. Priors for macroeconomic variables are often adopted as “conjugate prior distributions”—that is, distributions that yields a posterior distribution in the same family as the prior p.d.f.—in the form of Normal-Inverse-Wishart distributions that are conjugate prior for the likelihood of a VAR with normally distributed disturbances. Conjugate priors allow direct sampling from the posterior distribution and fast estimation. When this is not possible, numerical techniques such as Gibbs and Metropolis-Hastings sampling algorithms are adopted. Bayesian techniques allow for the estimation of an ever-expanding class of sophisticated autoregressive models that includes conventional fixed-parameters VAR models; Large VARs incorporating hundreds of variables; Panel VARs, that permit analyzing the joint dynamics of multiple time series of heterogeneous and interacting units. And VAR models that relax the assumption of fixed coefficients, such as time-varying parameters, threshold, and Markov-switching VARs.

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