7 articles for “Autoregression”

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Research ArticleOpen accessEvery word in the titleBayesian Models for Time Series with Covariates, Trend, Seasonality, Autoregression and OutliersPitsanu Tongkhow, Nantachai KantananthaBayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the … …Journal of Computer Science, Vol. 9, Issue 3 · 16 Apr 2013 · 6,653 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleEstimation and Analysis of Output Gap: An Application of Structural Vector Autoregression and Hodrick-Prescott-FmethodsSaeed Dehghan Khavari, Seyed Hossein MirjaliliIn this study we examined output gap in the Iranian economy. The main question of the study is that how much is seasonal output gap in Iranian Economy and which factor affects gap variation. The other question is that whether using HP-F as a statistical based method for estimating output gap, provide different result than using SVAR as theory based … …American Journal of Economics and Business Administration, Vol. 4, Issue 3 · 27 Aug 2012 · 7,896 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsForecasting Air Temperatures Using Time Series Models and Neural-based AlgorithmsMahmoud M. Smadi, Farouq S. MjalliRecently, neural network models have been popular and found useful for forecasting a wide variety of time series data in many disciplines. This is due to their favorable modeling properties of simplicity, fault and noise tolerance and their capability to adapt to process changes. Nevertheless, applications in climatology have been less widespread than other disciplines such as economics. In this … …Journal of Mathematics and Statistics, Vol. 3, Issue 2 · 30 Jun 2007 · 7,272 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsA Deoxyribonucleic Acid Compression Algorithm Using Auto-Regression and Swarm IntelligenceWalid Aly, Basheer Yousif, Bassem ZohdyDNA compression challenge has become a major task for many researchers as a result of exponential increase of produced DNA sequences in gene databases; in this research we attempt to solve the DNA compression challenge by developing a lossless compression algorithm. The proposed algorithm works in horizontal mode using a substitutional-statistical technique which is based on Auto Regression modeling (AR), … …Journal of Computer Science, Vol. 9, Issue 6 · 30 May 2013 · 6,729 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsSome Fast Methods for Fitting Some One-parameter Spatial ModelsR. J. MartinIt is common in geographic modelling to use a one-parameter spatial model to specify the inverse covariance matrix in terms of I-βW, for some known matrix W. Exact Gaussian maximum likelihood estimation of β requires evaluation of the determinant of the covariance matrix. For large data sets, this evaluation of the determinant can be slow and good approximations can be … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,066 accessesView PDF Cite

Research ArticleOpen accessIn the abstractβ-Spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series ErrorsJinhong You, Gemai Chen, Xian ZhouWe study the estimation problems for a partly linear regression model with a nonlinear time series error structure. The model consists of a parametric linear component for the regression coefficients and a nonparametric nonlinear component. The random errors are unobservable and modeled by a first-order Markov bilinear process. Based on a B-spline series approximation of the nonlinear component function, we … …American Journal of Applied Sciences, Vol. 2, Issue 9 · 30 Sep 2005 · 6,734 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDissecting Two Approaches to Energy PricesJulius N. Esunge, Andrew Snyder-BeattieProblem statement: This research tested the viability of Geometric Brownian Motion as a stochastic model of oil prices. Approach: Using autoregressions and unit root tests, we determined that oil prices tend not to exhibit the Markov Property and thus GBM may be a problematic model. Results: Instead, oil prices seem to be mean reverting over the long run, possibly following … …Journal of Mathematics and Statistics, Vol. 7, Issue 2 · 18 May 2011 · 7,100 accessesView PDF Cite