Relevant bibliographies by topics / Autoregressive vectors

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Autoregressive vectors'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Autoregressive vectors"

1

Saikkonen, Pentti. "Testing normalization and overidentification of cointegrating vectors in vector autoregressive processes." Econometric Reviews 18, no. 3 (1999): 235–57. http://dx.doi.org/10.1080/07474939908800444.

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Franchi, Massimo, and Paolo Paruolo. "Cointegration, Root Functions and Minimal Bases." Econometrics 9, no. 3 (2021): 31. http://dx.doi.org/10.3390/econometrics9030031.

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This paper discusses the notion of cointegrating space for linear processes integrated of any order. It first shows that the notions of (polynomial) cointegrating vectors and of root functions coincide. Second, it discusses how the cointegrating space can be defined (i) as a vector space of polynomial vectors over complex scalars, (ii) as a free module of polynomial vectors over scalar polynomials, or finally (iii) as a vector space of rational vectors over rational scalars. Third, it shows that a canonical set of root functions can be used as a basis of the various notions of cointegrating sp
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Johansen, Soren. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models." Econometrica 59, no. 6 (1991): 1551. http://dx.doi.org/10.2307/2938278.

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Luukkonen, Ritva, Antti Ripatti, and Pentti Saikkonen. "Testing for a Valid Normalization of Cointegrating Vectors in Vector Autoregressive Processes." Journal of Business & Economic Statistics 17, no. 2 (1999): 195. http://dx.doi.org/10.2307/1392475.

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Luukkonen, Ritva, Antti Ripatti, and Pentti Saikkonen. "Testing for a Valid Normalization of Cointegrating Vectors in Vector Autoregressive Processes." Journal of Business & Economic Statistics 17, no. 2 (1999): 195–204. http://dx.doi.org/10.1080/07350015.1999.10524810.

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Nielsen, Bent. "ANALYSIS OF COEXPLOSIVE PROCESSES." Econometric Theory 26, no. 3 (2009): 882–915. http://dx.doi.org/10.1017/s0266466609990144.

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A vector autoregressive model allowing for unit roots as well as an explosive characteristic root is developed. The Granger-Johansen representation shows that this results in processes with two common features: a random walk and an explosively growing process. Cointegrating and coexplosive vectors can be found that eliminate these common factors. The likelihood ratio test for a simple hypothesis on the coexplosive vectors is analyzed. The method is illustrated using data from the extreme Yugoslavian hyperinflation of the 1990s.
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De Cao, Nicola, Ledell Wu, Kashyap Popat, et al. "Multilingual Autoregressive Entity Linking." Transactions of the Association for Computational Linguistics 10 (2022): 274–90. http://dx.doi.org/10.1162/tacl_a_00460.

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Abstract We present mGENRE, a sequence-to- sequence system for the Multilingual Entity Linking (MEL) problem—the task of resolving language-specific mentions to a multilingual Knowledge Base (KB). For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token in an autoregressive fashion. The autoregressive formulation allows us to effectively cross-encode mention string and entity names to capture more interactions than the standard dot product between mention and entity vectors. It also enables fast search within a large KB even for mentions th
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Durbin, J. "Approximate distributions of Student's t-statistics for autoregressive coefficients calculated from regression residuals." Journal of Applied Probability 23, A (1986): 173–85. http://dx.doi.org/10.2307/3214351.

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We consider a multiple regression model in which the regressors are Fourier cosine vectors. These regressors are intended as approximations to ‘slowly changing' regressors of the kind often found in time series regression applications. The errors in the model are assumed to be generated by a special type of autoregressive model defined so that the regressors are eigenvectors of the quadratic forms occurring in the exponent of the probability density of the errors. This autoregression is intended as an approximation to the usual stationary autoregression. Both approximations are adopted for the
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Durbin, J. "Approximate distributions of Student's t-statistics for autoregressive coefficients calculated from regression residuals." Journal of Applied Probability 23, A (1986): 173–85. http://dx.doi.org/10.1017/s0021900200117061.

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We consider a multiple regression model in which the regressors are Fourier cosine vectors. These regressors are intended as approximations to ‘slowly changing' regressors of the kind often found in time series regression applications. The errors in the model are assumed to be generated by a special type of autoregressive model defined so that the regressors are eigenvectors of the quadratic forms occurring in the exponent of the probability density of the errors. This autoregression is intended as an approximation to the usual stationary autoregression. Both approximations are adopted for the
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Xiong, Weili, Wei Fan, and Rui Ding. "Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems." Journal of Applied Mathematics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/684074.

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This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is
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Dissertations / Theses on the topic "Autoregressive vectors"

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Gudmundsson, Gudmundur Stefan. "Essays in network modelling." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663096.

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This thesis consists of two chapters on time series modelling. The first chapter introduces a class of vector autoregressive (VAR) models with a community structure for large panels of time series. In the model, the series are parti-tioned into latent groups such that spillovers are stronger within groups than between them. We then propose an algorithm that uses the eigenvectors of a function of the estimated autoregressive matrices to recover the communities. We study the properties of the procedure and establish its consistency. The algorithm motivates us to suggest a regularised VAR estimato
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Smaniotto, Emanuelle Nava. "O impacto da oferta de crédito no investimento privado brasileiro." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6340.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-08T12:06:15Z No. of bitstreams: 1 Emanuelle Nava Smaniotto_.pdf: 445452 bytes, checksum: 5741b34057dbeb9c0f08c71040f4979b (MD5)<br>Made available in DSpace on 2017-06-08T12:06:15Z (GMT). No. of bitstreams: 1 Emanuelle Nava Smaniotto_.pdf: 445452 bytes, checksum: 5741b34057dbeb9c0f08c71040f4979b (MD5) Previous issue date: 2017-02-24<br>Nenhuma<br>Este trabalho apresenta uma análise sobre o impacto da oferta de crédito no investimento privado no Brasil, entre 2001 e 2016. Para isso, é introduzida a metodologia de Mudança de Reg
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Ooms, M. "Empirical vector autoregressive modeling." [S.l. : Rotterdam : s.n.] ; Erasmus University [Host], 1993. http://hdl.handle.net/1765/14163.

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Polito, Vito. "Vector autoregressive analysis of macroeconomic policy." Thesis, University of York, 2007. http://etheses.whiterose.ac.uk/11068/.

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Brüggemann, Ralf. "Model reduction methods for vector autoregressive processes /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0818/2003067373-d.html.

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Sugita, Katsuhiro. "Bayesian analysis of cointegrated vector autoregressive models." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/66201/.

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This thesis concerns econometric time series modelling of cointegrated multivariate systems using a Bayesian approach. The Bayesian approach has become increasingly attractive among researchers in the fields such as biology, though still only a relatively few econometricians use these techniques. Rather than theoretical aspects of Bayesian statistics or computational techniques, we illustrate how the Bayesian methods can be useful in analysing non-linear cointegration models. In the last ten years, non-linear time series models, such as regime switching models, have become popular among applie
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Feldkircher, Martin, and Florian Huber. "Adaptive Shrinkage in Bayesian Vector Autoregressive Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4933/1/wp221.pdf.

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Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage wit
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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models /." Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1995. http://www.hhs.se/efi/summary/405.

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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1995. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-878.

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In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. Their popularity appears to be attributable to their flexibility relative to other models rather than to their virtues per se. In addition, analysts often use VAR models as benchmark models. VAR modeling has not gone uncriticised, th
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Crespo, Cuaresma Jesus, Martin Feldkircher, and Florian Huber. "Forecasting with Global Vector Autoregressive Models: A Bayesian Approach." Wiley, 2016. http://dx.doi.org/10.1002/jae.2504.

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This paper develops a Bayesian variant of global vector autoregressive (B-GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B-GVAR models in terms of point and density forecasts for one-quarter-ahead and four-quarter-ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country-specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B-GVAR specifications tend to
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Books on the topic "Autoregressive vectors"

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Ooms, Marius. Empirical Vector Autoregressive Modeling. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-48792-7.

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Holden, K. Vector autoregression modelling and forecasting. Liverpool Business School, 1994.

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1943-, Holden K., ed. Vector autoregression modelling and forecasting. John Wiley, 1995.

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Brüggemann, Ralf. Model Reduction Methods for Vector Autoregressive Processes. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17029-4.

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Aggarwal, Shalini, Sunita Arora, and Arvinder Kaur. How to Use Vector Autoregressive (VAR) Models. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036213596.

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Johansen, Søren. Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, 1995.

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Brännström, Tomas. Bias approximation and reduction in vector autoregressive models. typescript, 1995.

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Ramaswamy, Ramana. Japan's stagnant nineties: A vector autoregression retrospective. International Monetary Fund, Asia and Pacific Department, 1999.

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universitet, Uppsala, ed. Essays on vector autoregressions with cointegrating restrictions. Uppsala University, 1992.

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Kadiyala, K. R. Forecasting with Bayesian vector autoregressions. Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1989.

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Book chapters on the topic "Autoregressive vectors"

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Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. "Vector Autoregressive Processes." In Introduction to Modern Time Series Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33436-8_4.

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Kirchgässner, Gebhard, and Jürgen Wolters. "Vector Autoregressive Processes." In Introduction to Modern Time Series Analysis. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73291-4_4.

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Lütkepohl, Helmut. "Vector Autoregressive Models." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_609.

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Cao, Zheng Chris. "Vector autoregressive models." In Econometric Modelling and Forecasting of Tourism Demand. Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-5.

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Lütkepohl, Helmut. "Vector Autoregressive Models." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-69359-9_745.

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Lütkepohl, Helmut. "Stable Vector Autoregressive Processes." In Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02691-5_2.

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Lütkepohl, Helmut. "Stable Vector Autoregressive Processes." In Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-61695-2_2.

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Lütkepohl, Helmut. "Stable Vector Autoregressive Processes." In New Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-27752-1_2.

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Min, Chung-ki. "Vector autoregressive (VAR) models." In Applied Econometrics. Routledge, 2019. http://dx.doi.org/10.4324/9780429024429-9.

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Gates, Kathleen M., Sy-Miin Chow, and Peter C. M. Molenaar. "Vector Autoregression (VAR)." In Intensive Longitudinal Analysis of Human Processes. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429172649-4.

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Conference papers on the topic "Autoregressive vectors"

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Butler, Kurt, Marija Iloska, and Petar M. Djurić. "On Counterfactual Interventions in Vector Autoregressive Models." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715108.

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Fang, Gang, and Yiqi Liu. "An Online Strategy for Nonlinear Vector Autoregression." In 2024 7th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2024. https://doi.org/10.1109/rcae62637.2024.10834233.

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Chen, Shumei, and S. Joe Qin. "Latent Vector Autoregressive Modeling with Maximum Predicted Variance for Dynamic Process Monitoring." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831176.

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Yin, Aoxiong, Haoyuan Li, Kai Shen, Siliang Tang, and Yueting Zhuang. "T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.183.

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Yu, Jiaxin, Yining Dong, and S. Joe Qin. "Kernel Latent Vector Autoregressive Model for Nonlinear Dynamic Data Modeling and Monitoring." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10885978.

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Karkazan, Kalthoum, Haluk Topcuoglu, and Shaaban Sahmoud. "A Vector Autoregression-Based Algorithm for Dynamic Many-Objective Optimization Problems." In 16th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013009200003837.

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Barapatre, Sarvesh, Omkar Gangurde, Rohit Bibwe, and Shweta Tiwaskar. "Digital Sensor Forecasting for Metro Air Units: A Vector Autoregression Approach." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625539.

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Tan, Zeqi, Yongliang Shen, Shuai Zhang, Weiming Lu, and Yueting Zhuang. "A Sequence-to-Set Network for Nested Named Entity Recognition." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/542.

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Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods, considering the entity recognition as a span classification task, can deal with nested entities naturally. But they suffer from the huge search space and the lack of interactions between entities. To address these issues, we propose a novel sequence-to-set neural network for nested NER. Instead of specifying candidate spans in advance, we provide a fixed set of learnable vectors to learn the patterns of the valuabl
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Panazan, Oana, and Catalin Gheorghe. "Influence of geopolitical risk on stock volatility in the Middle East and North Africa states." In 14th International Scientific Conference „Business and Management 2024“. Vilnius Gediminas Technical University, 2024. http://dx.doi.org/10.3846/bm.2024.1274.

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The paper aims to explore the impact of geopolitical risk (GPR) on volatility dynamics in the Middle East and North Africa (MENA) states following the conflict between Ukraine and Russia (started in February 2022) and Israel and Hamas (started in October 2023). Fourteen states were analyzed between 01 January 2022 and 31 December 2023 using exponential general autoregressive conditional heteroskedastic (EGARCH) and vector autoregression (VAR). We find that GPR influenced the MENA markets slightly, and only Iraq reacted significantly to the Ukraine conflict. We also observed a clustering tenden
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Iseki, Toshio. "Instantaneous Spectral Analysis of Non-Stationary Ship Motion Data." In 25th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/omae2006-92197.

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The time varying coefficient vector autoregressive (TVVAR) modeling is applied to the cross-spectral analysis of non-stationary ship motion data. Introducing the instantaneous response, a vector autoregressive model can be reduced to simple time varying coefficient autoregressive (TVAR) models for each ship motion and the required CPU time is effectively reduced. The TVVAR model and stochastic perturbed difference equations are transformed into a state space model. The vector-valued unknown coefficients can be evaluated and the instantaneous cross spectra of ship motions can be calculated at e
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Reports on the topic "Autoregressive vectors"

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Rosser, J. Barkley, and Richard G. Sheehan. A Vector Autoregressive Model of Saudi Arabian Inflation. Federal Reserve Bank of St. Louis, 1985. http://dx.doi.org/10.20955/wp.1985.011.

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Kim, Sei-Wan, Donghyun Park, and Shu Tian. How Does Inflation in Advanced Economies Affect Emerging Market Bond Yields? Empirical Evidence from Two Channels. Asian Development Bank, 2023. http://dx.doi.org/10.22617/wps230372-2.

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This study uses multivariable smooth transition autoregressive–vector autoregressive (STAR–VAR), a novel model to explore how inflation in advanced economies affects emerging market bond yields. Results reveal two key findings. First, advanced economy inflation has a significant effect on emerging market bond yields. Second, the short-run effect of advanced economy inflation on the bond yields of emerging markets is asymmetric between the expansion and contraction regimes. The effect is mostly positive in both regimes but stronger in a bond yield’s contraction regime.
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Ahmed, Ehsan, J. Barkley Rosser, and Richard G. Sheehan. A Model of Global Aggregate Supply and Demand Using Vector Autoregressive Techniques. Federal Reserve Bank of St. Louis, 1986. http://dx.doi.org/10.20955/wp.1986.004.

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Xu, J., S. Yoo, J. Heiser, and P. Kalb. Sensor network based solar forecasting using a local vector autoregressive ridge framework. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1336118.

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Mocan, Naci. Business Cycles and Fertility Dynamics in the U.S.: A Vector-Autoregressive Model. National Bureau of Economic Research, 1989. http://dx.doi.org/10.3386/w3177.

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Bernanke, Ben, Jean Boivin, and Piotr Eliasz. Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10220.

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Yamada, Tadashi. The Crime Rate and the Condition of the Labor Market: A Vector Autoregressive Model. National Bureau of Economic Research, 1985. http://dx.doi.org/10.3386/w1782.

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressi
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Ang, Andrew, and Monika Piazzesi. A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/w8363.

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du Rand, Gideon, Hylton Hollander, and Dawie van Lill. Time-varying fiscal multipliers for South Africa: A large time-varying parameter vector autoregression approach. UNU-WIDER, 2023. http://dx.doi.org/10.35188/unu-wider/2023/414-4.

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