Relevant bibliographies by topics / Vector autoregression

Contents

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

Academic literature on the topic 'Vector autoregression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Vector autoregression"

1

Dufour, Jean-Marie. "Unbiasedness of Predictions from Etimated Vector Autoregressions." Econometric Theory 1, no. 3 (1985): 387–402. http://dx.doi.org/10.1017/s0266466600011270.

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Forecasts from a univariate autoregressive model estimated by OLS are unbiased, irrespective of whether the model fitted has the correct order; this property only requires symmetry of the distribution of the innovations. In this paper, this result is generalized to vector autoregressions and a wide class of multivariate stochastic processes (which include Gaussian stationary multivariate stochastic processes) is described for which unbiasedness of predictions holds: specifically, if a vector autoregression of arbitrary finite order is fitted to a sample from any process in this class, the fitt
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Zhu, Xuening, Rui Pan, Guodong Li, Yuewen Liu, and Hansheng Wang. "Network vector autoregression." Annals of Statistics 45, no. 3 (2017): 1096–123. http://dx.doi.org/10.1214/16-aos1476.

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Lanne, Markku, and Pentti Saikkonen. "NONCAUSAL VECTOR AUTOREGRESSION." Econometric Theory 29, no. 3 (2012): 447–81. http://dx.doi.org/10.1017/s0266466612000448.

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In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model
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Härdle, W., A. Tsybakov, and L. Yang. "Nonparametric vector autoregression." Journal of Statistical Planning and Inference 68, no. 2 (1998): 221–45. http://dx.doi.org/10.1016/s0378-3758(97)00143-2.

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Shapor, Maria Alexandrovna, and Rafael Rubenovich Gevogyan. "Features of the vector autoregression models application in macroeconomic research." Mezhdunarodnaja jekonomika (The World Economics), no. 8 (August 10, 2021): 634–49. http://dx.doi.org/10.33920/vne-04-2108-05.

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In this paper, we analyzed articles by foreign authors that use various vector autoregression models to calculate the impact of qualitative indicators on the economic processes of countries or a group of countries. In particular, the article analyzed the classical model of vector autoregression (VAR), panel model of autoregressive (PVAR), Bayesian model of autoregressive (BVAR), structural model of autoregressive (SVAR), and the global model of autoregressive (GVAR). Among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-tr
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Makletsov, Sergey V., and Nadezda A. Opokina. "The use of structural vector autoregression to assess the mutual impact of consumption and average wage levels." Journal Of Applied Informatics 20, no. 1 (2025): 5–15. https://doi.org/10.37791/2687-0649-2025-20-1-5-15.

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At present, it is an urgent task to describe macroeconomic phenomena through the construction of qualitative mathematical models. In the current economic landscape, the interaction among economic factors is of significant importance. Therefore, it is necessary to understand the relationships between key indicators that significantly contribute to economic growth. In particular, the interactions between these indicators can be investigated using vector autoregressive models, in which they are treated as endogenous variables. The strength of the relationship between variables can be determined t
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Feifei, Wang, Zhu Xuening, and Pan Rui. "Generalized network vector autoregression." SCIENTIA SINICA Mathematica 51, no. 8 (2020): 1253. http://dx.doi.org/10.1360/scm-2018-0839.

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Kalliovirta, Leena, Mika Meitz, and Pentti Saikkonen. "Gaussian mixture vector autoregression." Journal of Econometrics 192, no. 2 (2016): 485–98. http://dx.doi.org/10.1016/j.jeconom.2016.02.012.

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Lanne, Markku, and Jani Luoto. "Noncausal Bayesian Vector Autoregression." Journal of Applied Econometrics 31, no. 7 (2016): 1392–406. http://dx.doi.org/10.1002/jae.2497.

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Tovstik, T. M. "Vector autoregression process. Stationarity and simulation." Journal of Physics: Conference Series 2099, no. 1 (2021): 012068. http://dx.doi.org/10.1088/1742-6596/2099/1/012068.

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Abstract For vector discrete-parameter random autoregressive processes and for a mixed autoregression/moving-average model, we obtain conditions which should be satisfied by the correlation functions or the model coefficients in order that the process be weakly stationary. Fairly simple tests are used. Algorithms for modeling such vector stationary processes are given. Examples are presented clarifying testing criteria for stationarity of models defned in terms of the coefficients or the correlation functions of the process.
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Dissertations / Theses on the topic "Vector autoregression"

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Dutta, Bordoloi Suwodi. "Interdependence of US Industry Sectors Using Vector Autoregression." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/1073.

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"In this study, we explore the interdependence among different US industries by examining their correlations of the stock portfolios. Furthermore, we focus on the dynamics of their interdependent relations during peaceful and volatile periods; as such relations may change due to different sensitivities of each industry to the macroeconomic conditions. More specifically, we apply Vector Autoregression (VAR) methodology on the US industry portfolios and we use variance decomposition and generalized impulse response functions to identify the strength of the impact of each industry on the others.
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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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Jeon, Kyung-Seong. "An examination of stock market properties : vector autoregression approach /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841304.

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Unosson, Måns. "A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297406.

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This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR m
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Zhang, Wei. "A sensitivity study on identification schemes of the structural vector autoregression /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025669.

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Petrov, Krassimir M. "Forecasting the dairy price complex : an application of Bayesian Vector autoregression modelling /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193272066522.

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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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Jordanov, Jordan V. "The size anomaly in the London Stock Exchange : an empirical investigation." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/7067.

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This study tests the size effect in the London Stock Exchange, using data for all nonfinancial listed firms from January 1985 to December 1995. The initial tests indicate that average stock returns are negatively related to firm size and that small firm portfolios earn returns in excess of the market risk. Further, the study tests whether the size effect is a proxy for variables such as the Book-to- Market Value and the Borrowing Ratio, as well as the impact of the dividend and the Bid- Ask spread on the return of the extreme size portfolios. The originality of this study is in the application
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White, Alexander B. "Pre- and post-retirement asset allocation: a simulation of retirement investment strategies for agricultural producers." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/38097.

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This research simulates pre-retirement investment scenarios for agricultural producers. Thirty-two investment scenarios are examined, with each scenario differing with respect to retirement vehicle, investment strategy of the producer, and the use of a cash margin for reinvestment in the operation versus prepaying term debt (cash preference). The retirement vehicles included in this study are Individual Retirement Accounts (IRAs), Simplified Employee Pension Plans (SEPs), and 401(k) plans. Investment strategies reflect the producer's preference for investing in conservative, balanced, or aggre
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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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Books on the topic "Vector autoregression"

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

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Crone, Theodore M. Vector-autoregression forecast models for the third district states. Federal Reserve Bank of Philadelphia, Economic Research Division, 1992.

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Ghatak, Anita. Vector autoregression modelling and forecasting growth of South Korea. De Montfort University, School of Social Sciences, 1997.

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Wright, Jonathan H. Exact confidence intervals for impulse responses in a gaussian vector autoregression. Federal Reserve Board, 2000.

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Elitzak, Howard. Quarterly forecasting of meat retail prices: A vector autoregression approach. U.S. Dept of Agriculture, Economic Research Service, Commodity Economics Division, 1989.

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Book chapters on the topic "Vector autoregression"

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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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Aljandali, Abdulkader, and Motasam Tatahi. "Vector Autoregression (VAR) Model." In Economic and Financial Modelling with EViews. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92985-9_10.

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McElroy, Tucker, and David Findley. "Fitting Constrained Vector Autoregression Models." In Empirical Economic and Financial Research. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03122-4_28.

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Ooms, Marius. "Data Analysis by Vector Autoregression." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-48792-7_3.

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Das, Panchanan. "Cointegration, Error Correction and Vector Autoregression." In Econometrics in Theory and Practice. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9019-8_12.

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McMillan, Susan M. "The Question of Causality: Vector Autoregression Analysis." In Foreign Direct Investment in Three Regions of the South at the End of the Twentieth Century. Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1007/978-1-349-27218-1_5.

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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples. CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0174.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine th
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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples. CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0008.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine th
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Hochreiter, Ronald, and Gerald Krottendorfer. "Robust Estimation of Vector Autoregression (VAR) Models Using Genetic Algorithms." In Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37192-9_23.

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Kharin, Yuriy. "Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values." In Robustness in Statistical Forecasting. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00840-0_8.

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Conference papers on the topic "Vector autoregression"

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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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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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Zhou, Lin, Shengyong Yao, Shuning Li, and Fei Xue. "Short term OD prediction of urban rail transit based on vector autoregression." In Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), edited by Hao Chen and Wei Shangguan. SPIE, 2025. https://doi.org/10.1117/12.3050919.

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Gupta, Pooja. "Overconfidence Bias And Trading Volume From Indian Stock Market By Vector Autoregression Testing." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10738910.

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Wu, Honghao, Miao Yu, Jianqun Sun, Jinyang Han, Jiaxin Yan, and Runxin Zeng. "Coordinated Analysis of “Power Quality-economic cost” Based on A Vector Autoregression and Coupling Coordination Degree Method." In 2024 6th International Conference on Power and Energy Technology (ICPET). IEEE, 2024. https://doi.org/10.1109/icpet62369.2024.10940790.

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Kshirsagar, Ishaan, Julian Simon, Nicolò Schätz, David Fraga Gonzalez, and Conor Ryan. "A Vector Autoregression Model for Depicting the Relation Between Labour Market Economic Indicators and Real Wages in the United States Manufacturing Sector." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013123000003890.

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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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Haboub, Amine, Hamza Baali, and Abdesselam Bouzerdoum. "Multichannel Signal Classification Using Vector Autoregression." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054144.

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C.J., Harivigneshwar, Dharmavenkatesan K.B., Ajith R., and Jeyanthi R. "Modeling of Multivariate Systems using Vector Autoregression(VAR)." In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2019. http://dx.doi.org/10.1109/i-pact44901.2019.8960145.

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Reports on the topic "Vector autoregression"

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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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Zhang, Zhen. Longitudinal SEM in Mplus: Latent Growth and Cross-Lagged Models. Instats Inc., 2022. http://dx.doi.org/10.61700/shmr7uf60jtgi469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar.
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Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2022-09.

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Existing estimates of the macroeconomic effects of Australian monetary policy tend to be based on strong, potentially contentious, assumptions. I estimate these effects under weaker assumptions. Specifically, I estimate a structural vector autoregression identified using a variety of sign restrictions, including restrictions on impulse responses to a monetary policy shock, the monetary policy reaction function, and the relationship between the monetary policy shock and a proxy for this shock. I use an approach to Bayesian inference that accounts for the problem of posterior sensitivity to the
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Zyphur, Michael. Longitudinal SEM in Mplus: Latent Growth and Cross-Lagged Panel Models. Instats Inc., 2022. http://dx.doi.org/10.61700/zvz8cn20pod2l469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Zhang, Zhen. Longitudinal SEM in Mplus: Latent Growth and Cross-Lagged Panel Models. Instats Inc., 2022. http://dx.doi.org/10.61700/k7ip0jnkhqk0z469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Zyphur, Michael. Longitudinal SEM in R: Latent Growth and Cross-Lagged Panel Models. Instats Inc., 2022. http://dx.doi.org/10.61700/0cgexcmkbt2w4469.

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This seminar introduces longitudinal panel data models in Lavaan using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Zhang, Zhen. Longitudinal SEM in Mplus (Free with Course Purchase). Instats Inc., 2023. http://dx.doi.org/10.61700/qbnb0rzkq0afl469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Moran, Kevin, Dalibor Stevanovic, and Adam Abdel Kader Touré. Pessimism could plunge us into a recession. CIRANO, 2024. http://dx.doi.org/10.54932/zbcm3806.

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For over a year, various studies have been reporting that Canadian businesses and consumers are fairly pessimistic about the economy. Evaluating the extent to which this wave of pessimism could cause an economic slowdown presents a methodological challenge, one the authors tackled in a recent CIRANO study. Drawing upon Vector Autoregression (VAR) methodology as well as Canadian and American historical data, the authors suggest that correlations between confidence and economic activity do indeed include a causal component. As a result, the recent declines observed in confidence indices could le
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