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Relevant bibliographies by topics / Structural Vector Autoregressive Analysi

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Academic literature on the topic 'Structural Vector Autoregressive Analysi'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Structural Vector Autoregressive Analysi"

1

Lütkepohl, Helmut. "Structural vector autoregressive analysis for cointegrated variables." Allgemeines Statistisches Archiv 90, no. 1 (2006): 75–88. http://dx.doi.org/10.1007/s10182-006-0222-4.

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2

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-trivial variables are rare. This is because financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. The studied articles analyze the data of both developed and developing states or groups of states in different periods. The studied articles were classified according to several criteria, which were selected by the author to structure the work. Note that among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is since financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. In the conclusion of this study, the author presented conclusions based on the analysis of autoregressive models.

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3

Park, Sunghwa, Janghan Kwon, and Taeil Kim. "An Analysis of the Dynamic Relationship between the Global Macroeconomy and Shipping and Shipbuilding Industries." Sustainability 13, no. 24 (2021): 13982. http://dx.doi.org/10.3390/su132413982.

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Using time-series data from January 2006 to February 2021, this study analyzed the effect of macroeconomic shocks on the shipping and shipbuilding industries. The Granger causality test, recursive structural vector autoregressive models, impulse response analysis, historical decomposition, and local projections model were used to identify the dynamic relationships between the variables and their dynamic effects, based on the results of the theoretical model and previous research. First, the Granger causality test demonstrated that the macroeconomic variables have causal relations with the shipping and shipbuilding industries. Second, the recursive structural vector autoregressive estimation demonstrated that the direction of the shocks from macroeconomic variables is statistically significantly, consistent with the theoretical model. The same results were found in the recursive structural vector autoregressive model and local projection impulse response analysis. Finally, the historical decomposition identified the main causal variables affecting the shipping and shipbuilding industries by period. These findings can help policymakers, operators of shipping and shipbuilding companies, and investors evaluate and make policy-supporting decisions on industry conditions.

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4

Vu, Viet-Hung, Zhaoheng Liu, Marc Thomas, and Bruce Hazel. "Modal analysis of a light-weight robot with a rotating tool installed at the end effector." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 9 (2015): 1664–76. http://dx.doi.org/10.1177/0954406215619451.

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This paper investigates vibration of a moving flexible robot through modal analysis and by constructing vibration spectra of operational signals. A vector autoregressive model combined with a sliding window technique is used for signal processing in order to take into account system nonstationarity. Modal decomposition is conducted on the state matrix constructed from the appropriate vector autoregressive model parameters. A complete modal decomposition and spectrum construction algorithm able of highlighting the structural modes and harmonic excitations is presented. Through accurate identification from the vector autoregressive model, the method presented is able to discriminate, display and monitor the harmonics and structural modes during the processes investigated. This method is validated first by numerical simulation and then experimentally with a flexible robot performing three processes: moving a manipulator through the workspace, steady rotation of a grinder on the end effector and moving the manipulator combined with rotating the grinder. It is found on the operating robot that participation of the first structural mode is negligible when rotating the grinder but must be taken into account when moving the manipulator. The analysis presented and results obtained provide a sound basis for further investigation of vibroimpact behaviour in a robotic grinding process.

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5

Kurita, Takamitsu, and Bent Nielsen. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms." Econometrics 7, no. 4 (2019): 42. http://dx.doi.org/10.3390/econometrics7040042.

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This paper proposes a class of partial cointegrated models allowing for structural breaks in the deterministic terms. Moving-average representations of the models are given. It is then shown that, under the assumption of martingale difference innovations, the limit distributions of partial quasi-likelihood ratio tests for cointegrating rank have a close connection to those for standard full models. This connection facilitates a response surface analysis that is required to extract critical information about moments from large-scale simulation studies. An empirical illustration of the proposed methodology is also provided.

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6

Lütkepohl, Helmut, and Thore Schlaak. "Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis." Oxford Bulletin of Economics and Statistics 80, no. 4 (2018): 715–35. http://dx.doi.org/10.1111/obes.12238.

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7

Waiguru Muriuki, Samuel. "Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks." International Journal of Data Science and Analysis 4, no. 5 (2018): 79. http://dx.doi.org/10.11648/j.ijdsa.20180405.12.

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8

Osman, Aminu, Joshua Sebu, Omowumi O. Iledare, Eric Amoo Bondzie, and Mubarik Salifu. "Structural Vector Autoregressive Analysis of Crude Oil Price Shocks on Ghana’s Economy." Universal Journal of Finance and Economics 3, no. 1 (2023): 1–18. http://dx.doi.org/10.31586/ujfe.2023.442.

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9

Wang, Shudong, Man Zhang, Yuanzhuo Wang, and Hui Meng. "Construction of Grain Price Determinants Analysis Model Based on Structural Vector Autoregressive Model." Scientific Programming 2022 (January 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/5694780.

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In 2020, the sudden global epidemic of novel coronavirus pneumonia (COVID-19) caused abnormal fluctuations in the global grain market and posed severe challenges to world grain security. Therefore, it is very important for countries around the world to analyze the determinants of grain price and put forward corresponding strategies to ensure grain safety. In this paper, we theoretically discussed the relationship between financial liquidity, speculation, and grain price for the first time. Based on the analysis of Fisher’s equation, this paper argues that the theoretical basis of grain financialization is closer to the volatility theory of the money market. Then, we employ the structural vector autoregression model (SVAR) to explore the impulse response of grain price to the structural shock of world grain production, demand, financial liquidity, and speculation. Our empirical results show that the effects of financial liquidity and speculation on the grain price are more significant. Meanwhile, grain demand changes caused by the global economy have no significant impact on grain price.

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10

Márquez, Miguel A., Julián Ramajo, and Geoffrey J. D. Hewings. "Measuring the spillover effects of public capital: a bi-regional structural vector autoregressive analysis." Letters in Spatial and Resource Sciences 3, no. 3 (2010): 111–25. http://dx.doi.org/10.1007/s12076-010-0042-8.

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