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About 10,000 papers for “Autoregressive model”
Jan Gottschalk, Vektorautoregressives Modell, Robert Kokta · 2011
The responsibility for the contents of the working papers rests with the author, not the Institute. Since working papers are of a preliminary nature, it may be useful to contact the author of a particular working paper about results or caveats before referring to, or quoting, a paper. Any comments on working papers should be sent directly to the author. Measuring Expected Inflation and the Ex-Ante
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Jakub Hledík · Digital Repository (National Repository of Grey Literature) · 2021
Binomial AR(1) process is a model for integer-valued time series with a fi- nite range and discrete time. It has the binomial marginal distribution and the AR(1)-like autocorrelation structure. This thesis deals with deriving some ba- sic properties of this process, methods of parameter estimation and goodness of fit testing. Three methods of parameter estimation are presented: Yule-Walker, the conditional least squares and the maximum likelihood method together with proofs of their asymptotical properties. Next, the goodness of fit testing is pre- sented. At first, two known methods based on the marginal distribution and the autocorrelation function are summarized. Then our own method is added, based on the probability generating function. Several simulations are provided to show the properties of all tests. The application of this model is illustrated on a real dataset. 1
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Aravindh Krishnamoorthy · 2016
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Jonas Ngouhouo-Poufoun (18824061), Sabine Chaupain-Guillot (18824064), Youba Ndiaye (10959648) et al. · Figshare · 2024
<div><p>In the context of emerging international trade regulations on deforestation-free commodities, the drivers of households’ deforestation in conservation landscapes are of interest. The role of households’ livelihood strategies including cocoa production, and the effects of human-elephant conflict are investigated. Using a unique dataset from a survey of 1035 households in the Tridom landscape in the Congo basin, the spatial autoregressive model shows that: (1) Households imitate the deforestation decisions of their neighbors; (2) A marginally higher income from cocoa production-based livelihood portfolios is associated with six to seven times higher deforestation compared to other livelihood strategies with a significant spillover effect on neighboring households’ deforestation. The increase in income, mainly from cocoa production-based livelihoods in open-access systems can have a negative effect on forests. Households with a higher share of auto-consumption are associated with lower deforestation. If economic development brings better market access and lower auto-consumption shares, this is likely to positively influence deforestation. Without proper land use planning/zoning associated with incentives, promoting sustainable agriculture, such as complex cocoa agroforestry systems, may lead to forest degradation and deforestation.</p></div>
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Long Zhou, Jiajun Zhang, Chengqing Zong · 2020
Autoregressive neural machine translation (NMT) models are often used to teach nonautoregressive models via knowledge distillation. However, there are few studies on improving the quality of autoregressive translation (AT) using non-autoregressive translation (NAT). In this work, we propose a novel Encoder-NAD-AD framework for NMT, aiming at boosting AT with global information produced by NAT model. Specifically, under the semantic guidance of source-side context captured by the encoder, the nonautoregressive decoder (NAD) first learns to generate target-side hidden state sequence in parallel. Then the autoregressive decoder (AD) performs translation from left to right, conditioned on source-side and target-side hidden states. Since AD has global information generated by low-latency NAD, it is more likely to produce a better translation with less time delay. Experiments on WMT14 EnDe, WMT16 EnRo, and IWSLT14 DeEn translation tasks demonstrate that our framework achieves significant improvements with only 8% speed degeneration over the autoregressive NMT.
Cited by 7Open access
WORLD SCIENTIFIC eBooks · 1998
In this paper we present the Large Inverse Cholesky (LIC) method, an efficient method for computing the coefficient matrices of a Structural Vector Autoregressive (SVAR) model.
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N. A. Cruz, J. D. Toloza-Delgado, Oscar O. Melo · arXiv (Cornell University) · 2024
This paper presents the generalized spatial autoregression (GSAR) model, a significant advance in spatial econometrics for non-normal response variables belonging to the exponential family. The GSAR model extends the logistic SAR, probit SAR, and Poisson SAR approaches by offering greater flexibility in modeling spatial dependencies while ensuring computational feasibility. Fundamentally, theoretical results are established on the convergence, efficiency, and consistency of the estimates obtained by the model. In addition, it improves the statistical properties of existing methods and extends them to new distributions. Simulation samples show the theoretical results and allow a visual comparison with existing methods. An empirical application is made to Republican voting patterns in the United States. The GSAR model outperforms standard spatial models by capturing nuanced spatial autocorrelation and accommodating regional heterogeneity, leading to more robust inferences. These findings underline the potential of the GSAR model as an analytical tool for researchers working with categorical or count data or skewed distributions with spatial dependence in diverse domains, such as political science, epidemiology, and market research. In addition, the R codes for estimating the model are provided, which allows its adaptability in these scenarios.
Cited by 2Open access
Princeton University Press eBooks · 2012
In this paper we present the Large Inverse Cholesky (LIC) method, an efficient method for computing the coefficient matrices of a Structural Vector Autoregressive (SVAR) model.
Cited by 1
Andrea Freyer Dugas (283412), Mehdi Jalalpour (283414), Yulia Gel (283416) et al. · Figshare · 2015
<p>Number of confirmed Emergency Department (ED) influenza cases (dots) compared to the base 3<sup>rd</sup> order Negative Binomial Generalized Autoregressive Poisson (GARMA) model (line) over (a) the 2008–2009 atypical influenza season and (b) the 2010–2011 typical influenza season.</p>
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Cagatay Candan · Code Ocean · 2019
Autoregressive Model Parameter Estimation An implementation of the suggested method for the autoregressive (AR) modeling of WSS random processes. The method aims to find the maximum likelihood estimate for the model parameters. Yet, the exact maximum likelihood solution is infeasible to implement in many cases and the suggested aims to provide a good approximation to the maximum likelihood solution. The provided MATLAB code makes a comparison of the likelihood value attained by the suggested method and different methods, including the method of brute force numerical search by the MATLAB's optimization routines.
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Carolina Euán, Ying Sun · Journal of Multivariate Analysis · 2020
Cited by 4Open access
Andrea Freyer Dugas (283412), Mehdi Jalalpour (283414), Yulia Gel (283416) et al. · Figshare · 2015
<p>Number of confirmed Emergency Department (ED) influenza cases (dots) compared to the final 3<sup>rd</sup> order Negative Binomial Generalized Autoregressive Poisson (GARMA) model with Google Flu Trends as an added external variable (line) over (a) the 2008–2009 atypical influenza season and (b) the 2010–2011 typical influenza season.</p>
Cited by 0Open access
Manabu Asai, Mike K. P. So · Applied Stochastic Models in Business and Industry · 2024
Abstract To accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical data, where unknown parameters of the function can be estimated from the data. We propose a simulation technique to check the stationarity condition of the LAR model and suggest a Bayesian Markov chain Monte Carlo method for estimation. Empirical results for the quarterly growth rates of gross domestic product (GDP) in 45 countries indicate that (i) the autoregressive (AR) coefficient for the aggregated growth rate is hard to distinguish from zero; (ii) a panel AR model with individual effects has a positive autocorrelation; and (iii) the alternative LAR models are preferred to the panel AR model. Depending on the specification of the linkage variables, the sign and size of the linkage effect can differ. The logistic linkage function has a flexible structure to accommodate the size of the linkage of individual GDP growth rates, and it strengthens the dynamics.
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Mituaki Huzii, Theodore W. Anderson, Project Director et al. · 1976
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Euán, Carolina, Sun, Ying · RePEc: Research Papers in Economics · 2020
In this paper, we propose a vector autoregressive (VAR) model of order one for multivariate binary time series. Multivariate binary time series data are used in many fields such as biology and environmental sciences. However, modeling the dynamics in multiple binary time series is not an easy task. Most existing methods model the joint transition probabilities from marginals pairwisely for which the resulting cross-dependency may not be flexible enough. Our proposed model, Bernoulli VAR (BerVAR) model, is constructed using latent multivariate Bernoulli random vectors. The BerVAR model represents the instantaneous dependency between components via latent processes, and the autoregressive structure represents a switch between the hidden vectors depending on the past. We derive the mean and matrix-valued autocovariance functions for the BerVAR model analytically and propose a quasi-likelihood approach to estimate the model parameters. We prove that our estimator is consistent under mild conditions. We perform a simulation study to show the finite sample properties of the proposed estimators and to compare the prediction power with existing methods for binary time series. Finally, we fit our model to time series of drought events from different regions in Mexico to study the temporal dependence, in a given region and across different regions. By using the BerVAR model, we found that the cross-region dependence of drought events is stronger if a rain event preceded it.
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Considine, Jennifer, Hatipoglu, Emre, Aldayel, Abdullah · KAPSARC Models · 2024
This model enables us to compute actual quantitative value, or economic cost, of shocks to world oil markets. This is a analytical framework to analyze shocks to world oil markets. The model can predict the economic implications of disruptions, such as economic sanctions, war, financial crises, natural disasters and monetary and fiscal policy changes. It's built on global vector autoregression model. The GVAR utilizes a stylized representation of the global economy: 36 countries, 7 regions or subregions, and 8 country-specific variables: World oil price, GDP, inventories, crude oil production, inflation, long and short-term interest rates, critical mineral prices, OPEC Spare capacity, and equity prices.
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Yuanbo Li, Kun Chen, Xunze Zheng et al. · Statistica Sinica · 2022
This article proposes a functional threshold autoregressive model for flexible functional time series modeling.In particular, the behavior of a function at a given time point can be described by different autoregressive mechanisms according to different values of a threshold variable at a past time point.Sufficient conditions for strict stationarity and ergodicity of the functional threshold autoregressive process are investigated.A novel criterion based method is develop to simultaneously conduct dimension reduction and estimation of thresholds, autoregressive orders, and model parameters.The consistency and asymptotic distributions of the estimators of both thresholds and underlying autoregressive models are established.Simulation studies and a real application of U.S. Treasury zero-coupon yield rates are provided to illustrate the effectiveness and usefulness of the proposed methodology.
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Carlos Geraldes, J. Leonel Rocha, Filipe Martins · Mathematics · 2026
This work proposes a novel autoregressive (AR) modeling framework in which the model structure and coefficients are induced from the analytical properties of Butterworth filters. By exploiting the equivalence between AR models and all-pole discrete-time filters, the proposed approach derives the AR coefficients directly from the pole locations of a continuous-time Butterworth prototype mapped to the discrete-time domain. In this formulation, the filter order and stopband attenuation act as hyperparameters controlling the complexity and frequency-selective behavior of the resulting predictor, while a scalar gain parameter is estimated from data using a maximum likelihood criterion. Model selection is carried out through a nested cross-validation strategy tailored to time series data, employing a rolling-origin scheme to prevent look-ahead bias. The predictive performance of the resulting Butterworth-induced AR models is evaluated using one-step-ahead forecasts and compared against classical ARIMA models on simulated data. Experimental results show that the proposed approach achieves competitive predictive accuracy, while offering a structured and interpretable link between frequency-domain filter design and time-domain autoregressive modeling.
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Lei Qin, Xiaomei Zhang, Yingqiu Zhu et al. · Computational Statistics & Data Analysis · 2025
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Sjoerd Hermes · 2023
Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) <<a href="https://doi.org/10.48550%2FarXiv.2308.04325" target="_top">doi:10.48550/arXiv.2308.04325</a>>, and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. The package allows for the generation of spatial weight matrices to capture asymmetric effects for strip-type intercropping designs, although it can handle any type of spatial data commonly found in other sciences.
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