Relevant bibliographies by topics / Structural Vector Autoregressive Analysi

Academic literature on the topic 'Structural Vector Autoregressive Analysi'

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Published: 11 March 2023

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Journal articles on the topic "Structural Vector Autoregressive Analysi"

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

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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-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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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 (December 17, 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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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 (December 2, 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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Kurita, Takamitsu, and Bent Nielsen. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms." Econometrics 7, no. 4 (October 6, 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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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 (April 6, 2018): 715–35. http://dx.doi.org/10.1111/obes.12238.

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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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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 (February 3, 2023): 1–18. http://dx.doi.org/10.31586/ujfe.2023.442.

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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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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 (July 10, 2010): 111–25. http://dx.doi.org/10.1007/s12076-010-0042-8.

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Dissertations / Theses on the topic "Structural Vector Autoregressive Analysi"

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Hörnell, Fredrik, and Melina Hafelt. "Responsiveness of Swedish housing prices to the 2018 amortization requirement : An investigation using a structural Vector autoregressive model to estimate the impact of macro prudential regulation on the Swedish housing market." Thesis, Södertörns högskola, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-35533.

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This thesis analyzed and estimated the impact of the March 1, 2018 loan to income amortization requirement on residential real estate prices in Sweden. A four variables vector autoregressive model (VAR) was used to study the relationships between residential real estate prices, GDP, real mortgage rate and consumer price index over a time period from 2005 to 2017. First, a structural vector autoregressive (SVAR) model was used to test how a structural innovation in the error term for real mortgage rate affected residential real estate prices. Secondly, an unconditional forecast from our reduced VAR was produced to estimate post 2017 price growth of the Swedish housing market. The impulse response function results stand in contradiction to economic intuition i.e. the price puzzle problem. The unconditional forecast indicates that the housing market will enter a period with slower price growth post 2017, which are in line with previous research. This thesis vector autoregressive model can give meaningful results with regard to trend forecasts but with regard to precise statements as anticipating drastic price depreciation, it falls short. We recommend the use of reduced VAR forecasting with regard to the Swedish housing market.
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Braun, Robin [Verfasser]. "Three Essays on Identification in Structural Vector Autoregressive Models / Robin Braun." Konstanz : KOPS Universität Konstanz, 2019. http://d-nb.info/1191693473/34.

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Kobler, Alexander. "Sources and dynamics of macroeconomic fluctuations in Switzerland : evidence from a structural vector autoregressive approach /." Bern ; Berlin ; Bruxelles [etc.] : P. Lang, 2000. http://aleph.unisg.ch/hsgscan/hm00001729.pdf.

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Uhrin, Gábor B. [Verfasser], Martin [Akademischer Betreuer] Wagner, and Walter [Gutachter] Krämer. "In search of Q: results on identification in structural vector autoregressive models / Gábor B. Uhrin ; Gutachter: Walter Krämer ; Betreuer: Martin Wagner." Dortmund : Universitätsbibliothek Dortmund, 2017. http://d-nb.info/1138115134/34.

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Akin, Serdar. "Do Riksbanken produce unbiased forecast of the inflation rate? : and can it be improved?" Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-58708.

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The focus of this paper is to evaluate if forecast produced by the Central Bank of Sweden (Riksbanken) for the 12 month change in the consumer price index is unbiased? Results shows that for shorter horizons (h < 12) the mean forecast error is unbiased but for longer horizons its negatively biased when inference is done by Maximum entropy bootstrap technique. Can the unbiasedness be improved by strict ap- pliance to econometric methodology? Forecasting with a linear univariate model (seasonal ARIMA) and a multivariate model Vector Error Correction model (VECM) shows that when controlling for the presence of structural breaks VECM outperforms both prediction produced Riksbanken and ARIMA. However Riksbanken had the best precision in their forecast, estimated as MSFE
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BERNARDINI, EMMANUELA. "On the use of shrinkage estimators in macroeconometric modeling and forecasting." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/207742.

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In the last years a growing °ow of information in the ¯eld of macroeconomy has been collected in very large databases. It is well known nevertheless that, when a large number of series is available standard statistical tools do not work well. This thesis proposes new estimators for high dimensional systems, that are an optimally weighted average of two already existing estimators, a traditional unbiased one, su®ering of a large estimation error, and a target one, having a lot of bias coming from a misspeci¯ed structural assumption, but little in terms of variance. This method is generally known as shrinkage. We derive two di®erent estimators connected with large dimensional systems. First a new estimator for the coe±cient matrix in a large dimensional vector autoregressive process (VAR) is proposed. It shows a better performance in forecasting macroeconomic time series than a set of existing estimators, including factor models and bayesian shrinkage estimators. A new estimator is also built for the variance covariance matrix in high dimensional systems. This new estimator is used to test for the presence of Serial Correlation Common Features (SCCF) in a multivariate setting involving many, noisy, and collinear time series. It shows a good performance in terms of empirical size if compared to the already existing tool of Canonical Correlation Analysis (CCA).
Negli ultimi anni un °usso crescente di informazione di carattere macroeconomico ¶e stato raccolto in ampi database. Tuttavia ¶e risaputo che, quando un gran numero di serie ¶e disponibile, gli strumenti statistici standard non forniscono risultati a±dabili. Questa tesi propone nuovi stimatori in sistemi di elevate dimensioni, che sono una media ottimamente ponderata di due stimatori gi¶a esistenti, uno stimatore tradizionale non distorto, che com- mette un grande errore di stima, e uno stimatore target, distorto a causa di un'assunzione strutturale sbagliata, ma con un basso errore di stima. Questo metodo ¶e conosciuto come shrinkage. Due stimatori di®erenti legati a sistemi di grandi dimensioni sono derivati. Per primo viene proposto un nuovo stimatore per la matrice dei coe±cienti in un modello autoregressivo vettoriale (VAR) di grandi dimensioni. Questo stimatore mostra una performance migliore nel prevedere serie storiche macroeconomiche rispetto a un set di stimatori gi¶a esistenti, tra i quali gli stimatori dei modelli fattoriali e gli stimatori shrinkage bayesiani. Viene anche costruito un nuovo stimatore per la matrice di varianza e covarianza in sistemi di grandi dimensioni. Questo nuovo stimatore ¶e usato per testare la presenza della carat- teristica comune della correlazione seriale canonica (SCCF) in un contesto multivariato che comprende molte serie storiche collineari. Questo stimatore mostra una buona performance, in termini di size empirica, se confrontato con il metodo gi¶a esistente della analisi della correlazione canonica (CCA).
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Nodari, Gabriela Thais. "Uncertainty, Fiscal, and Financial Shocks in a Nonlinear World: Empirical Investigations." Doctoral thesis, 2015. http://hdl.handle.net/11562/909410.

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Questa tesi studia gli effetti macroeconomici degli shock di incertezza, shock fiscali e shock finanziari sul ciclo economico USA. La tesi si divide in quattro capitoli indipendenti, ognuno dei quali utilizza sistemi vettoriali autoregressivi, lineari e non-lineari, e local projections, al fine di estendere la ricerca empirica e valutare l’importanza dei diversi canali di trasmissione degli shock, così come proposti da modelli teorici. I risultati ottenuti dimostrano come tali shock hanno effetti non-lineari lungo il ciclo economico, e cioè la reazione delle variabili macroeconomiche è statisticamente differente se lo shock avviene durante una recessione piuttosto che durante un’espansione. Dalla prospettiva teorica, questi risultati sottolineano l’importanza di sviluppare modelli teorici non-lineari. Dalla prospettiva di politica economica, i risultati favoriscono l’implementazione di politiche asimmetriche come risposta a instabilità a livello macroeconomico.
This thesis investigates the macroeconomic effects of uncertainty, fiscal and financial shocks on the US economy. It is set out in four self-contained chapters, which use linear and nonlinear (Smooth Transition) Vector Autoregressive models, and Local Projections techniques, to extend the literature, and evaluate the importance of different transmission channels of macroeconomic shocks suggested by theoretical models. The main results show that these shocks have nonlinear effects over the business cycle, i.e., the response of macro aggregates following the shocks are statistically different depending on whether the economy is in a recession or expansion. From a theoretical perspective, this finding highlights the importance of accounting for nonlinearities when developing macroeconomic models. From a policy perspective, the results suggest the implementation of nonlinear policy rules to properly deal with macroeconomic instability.
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Wang, Sheng-Wen, and 王聖文. "The Impact of Monetary Policy Shocks on Stock Prices:An Application of Structural Vector Autoregressive Model." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/23176771747640377306.

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碩士
國立嘉義大學
應用經濟學系研究所
99
This study uses structural vector autoregressive models to investigate the impact of monetary policy shocks from one country on the stock prices in another countries.We choose several large and relatively closed economies (such as the U.S. and China), and then select several small open economies (eg Taiwan and Canada), in order to set up the empirical models for comparison purposes. The empirical results show that short-term interest rates in response to domestic monetary policy shocks have different dynamic reactions; furthermore, the short-term interest rates in a small open economy quite quickly reacts, but not very sustainable, while the response in a relatively large country is long-run and persists for a period of time. In addition, we found there were differences in the macroeconomic interdependence between the two economies. The monetary policy in a relatively large economy affects the stock prices in a small economy substantially, but not sustainably. it is because the floating exchange rate regime can form a strong self-stabilizer in small open economics. Finally, this research proposes that it would be helpful on understanding the monetary policy transmission mechanism when the wealth effect is taken into account in the empirical open economies.
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PENG, SZ-DA, and 彭四達. "On the study and application of modal analysis of structure by vector autoregressive and moving average model." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/54458655903092521907.

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Chao, Pei Ting, and 趙珮廷. "Assessing Optimum Currency Area Criteria by Using Structural Vector Autoregressive Approach: Evidence from Asia and Europe Area." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/88797773694649177396.

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碩士
長庚大學
工商管理學系
101
Because it is not clear whether recent attempt for monetary integration is justified. This research aims to assess the suitability of forming a currency area in Asia. The main purpose is to examine the macroeconomic shocks among the Asian economics and its persistence properties in comparison with the European Monetary Union (EMU) countries. Estimates of structural VARs are used to ascertain if the countries under review meet the essential ingredients of an Optimum Currency Area (OCA) and thus are candidates for a monetary union. The result shows that in recent Asia area it is possible for forming a common Asian currency from a smaller currency groupings, Newly Industrialized Economies(NIEs) and ASEAN regions. The identifying candidates of economies for potential monetary union are China, South Korea, Taiwan, India and three ASEAN countries.
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Books on the topic "Structural Vector Autoregressive Analysi"

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Kobler, Alexander E. Sources and dynamics of macroeconomic fluctuations in Switzerland: Evidence from a structural vector autoregressive approach. Bern: Peter Lang, 2000.

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Fund, International Monetary, ed. Italian unemployment 1975-95: An analysis of macroeconomic shocks and policies using evidence from a structural vector autoregression. Washington, D.C: International Monetary Fund, 1996.

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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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Lütkepohl, Helmut, and Lutz Kilian. Structural Vector Autoregressive Analysis. Cambridge University Press, 2017.

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Lütkepohl, Helmut, and Lutz Kilian. Structural Vector Autoregressive Analysis. Cambridge University Press, 2017.

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Structural Vector Autoregressive Analysis. Cambridge University Press, 2017.

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Lütkepohl, Helmut, and Lutz Kilian. Structural Vector Autoregressive Analysis. Cambridge University Press, 2017.

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Sources and Dynamics of Macroeconomic Fluctuations in Switzerland: Evidence from a Structural Vector Autoregressive Approach (European University Studies: Series 5, Economics and Management. Vol. 2525. Peter Lang Publishing, 1999.

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Gereziher, Hayelom Yrgaw, and Naser Yenus Nuru. Structural estimates of the South African sacrifice ratio. 12th ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/946-4.

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This paper estimates the output cost of fighting inflation—the sacrifice ratio—for the South African economy using quarterly data spanning the period 1998Q1–2019Q3. To compute the sacrifice ratio, the structural vector autoregressive model developed by Cecchetti and Rich (2001) based on Cecchetti (1994) is employed. Our findings show us a small sacrifice ratio, which lies within the range 0.00002–0.231 per cent with an average of 0.031 per cent, indicating a low level of output to be sacrificed while fighting inflation. Hence, the reserve bank is recommended to sustain an inflation rate within the target range and reap the benefits of a predictable and stable price path, as restrictive monetary policy has only a transitory effect on real variables like output.
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Book chapters on the topic "Structural Vector Autoregressive Analysi"

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Phadkantha, Rungrapee, Woraphon Yamaka, and Songsak Sriboonchitta. "Forecasting Exchange Rate with Linear and Non-linear Vector Autoregressive." In Structural Changes and their Econometric Modeling, 541–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04263-9_42.

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Diwambuena, Josué Mabulango, Amon Magwiro, Heinz Eckart Klingelhöfer, and Martin Kaggwa. "Foreign Direct Investment and Economic Growth: The Structural Vector Autoregressive Approach for South Africa." In Development Finance, 199–223. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54166-2_7.

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"Analysis of the Monetary Policy Dynamics in Romania Using a Structural Vector Autoregressive Model." In Emerging Research on Monetary Policy, Banking, and Financial Markets, 146–61. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9269-3.ch006.

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This chapter aims to provide an elaborate empirical analysis of the monetary policy dynamics in Romania using a structural vector autoregressive model. This chapter contributes to literature based on an empirical framework regarding the implications of exchange rate channel within the monetary policy, and the impact of the monetary aggregates channels in order to explain the evolution of the prices level in Romania.
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Spulbar, Cristi, and Ramona Birau. "Analysis of the Monetary Policy Dynamics in Romania Using a Structural Vector Autoregressive Model." In Research Anthology on Macroeconomics and the Achievement of Global Stability, 788–99. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7460-0.ch043.

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This chapter aims to provide an elaborate empirical analysis of the monetary policy dynamics in Romania using a structural vector autoregressive model. This chapter contributes to literature based on an empirical framework regarding the implications of exchange rate channel within the monetary policy, and the impact of the monetary aggregates channels in order to explain the evolution of the prices level in Romania.
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Rapoo, Mogari I., Elias Munapo, Martin M. Chanza, and Olusegun Sunday Ewemooje. "Modelling and Forecasting Portfolio Inflows." In Research Anthology on Artificial Neural Network Applications, 1427–48. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch069.

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This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.
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Rapoo, Mogari I., Elias Munapo, Martin M. Chanza, and Olusegun Sunday Ewemooje. "Modelling and Forecasting Portfolio Inflows." In Handbook of Research on Smart Technology Models for Business and Industry, 329–50. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3645-2.ch014.

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This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.
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"Results and Discussion 2." In Post-Keynesian Empirical Research and the Debate on Financial Market Development, 180–225. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6018-2.ch008.

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The empirical analysis of this chapter provides insights into the functioning of the economies of three selected countries. Later in the chapter, the dynamic responses of the model to shocks in indicators of financial development are investigated. To obtain credible impulse response analysis, economic theory is used to set the required identifying restrictions instead of using an “unrestricted” vector autoregressive model. The structural form of the model then is summarised in the chapter by the variance decomposition and impulse response functions. The general results from impulse response functions advocate the theory of financial intermediation arguing that the development of the financial market helps to promote economic growth. Furthermore, the results of variance decomposition shows that different measures of financial development influence the variation of growth variables, particularly investment, savings, and productivity growth.
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Breitung, Jörg, Ralf Brüggemann, and Helmut Lütkepohl. "Structural Vector Autoregressive Modeling and Impulse Responses." In Applied Time Series Econometrics, 159–96. Cambridge University Press, 2004. http://dx.doi.org/10.1017/cbo9780511606885.005.

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Juselius, Katarina. "Imperfect Knowledge, Asset Price Swings, and Structural Slumps." In Rethinking Expectations. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691155234.003.0011.

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This chapter examines the relationship between speculation in the currency markets and aggregate activity in the real economy by drawing on the Structural Slumps theory and the theory of Imperfect Knowledge Economics (IKE). It first considers exchange rate determination in two models, one based on the Rational Expectations Hypothesis (REH) and the other on the theory of IKE, before discussing some general principles for how to structure the observed persistence in the data, and how these principles can be used in the cointegrated vector autoregressive model. The chapter also explains how foreign currency speculation under IKE interacts with a customer market economy where profit shares are adjusting to fluctuations in real exchange rates and where the natural rate of unemployment is a function of nonstationary real long-term interest rates.
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Takaya, Sadayoshi. "International Capital Movements, Currency Crisis, and ICT Innovation." In Global Information Technology and Competitive Financial Alliances, 143–61. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-881-9.ch008.

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This chapter investigates the effects generated by the currency crisis. The countries experienced the currency turmoil confronted financial crisis, economic deterioration, and increase of unemployment. This chapter empirically examines the effect of currency depreciation on the real GDP and the unemployment rates in those countries by employing the structural vector autoregressive model, which attempts to clarify whether identified supply or demand shocks can be caused by exchange rate depreciation. This study suggests currency crisis might generate demand shock, to result in harmful impacts to real economy in those countries. Those could be considered as negative effects of the ICT innovation.
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Conference papers on the topic "Structural Vector Autoregressive Analysi"

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Bal, Harun, Mehmet Demiral, and Filiz Yetiz. "Exchange Rate Pass-Through to Domestic Prices: Evidence from OECD Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01951.

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There is an immense literature on the effects of exchange rate changes on macroeconomic indicators, specifically on the trade balance, growth, inflation, and overall productivity in open economies. One of the main attempts in the related literature is about ascertaining whether the exchange rate fluctuations alter domestic prices. This possible mechanism is called as the pass-through effect which is getting more important since the argument that exchange rate adjustment is a part of the solution for global rebalancing is empirically well-supported. Starting from this claim, this study purposes to explore whether there is an exchange rate pass-through effect in 19 high-income OECD countries over the period 1990-2015. To this end, using a panel data set of consumer price index, producer price index proxied by wholesale price index, the nominal effective exchange rates, and industrial production presented by the value-added share of industry sectors in gross domestic product, structural vector autoregressive (VAR) and autoregressive distributed lag (ARDL) models are estimated in an unbalanced panel data analysis procedure. Results reveal that exchange rate pass-through effects on the domestic prices are significant but not that strong in both the short-run and the long-run. Expectedly, the pass-through effects tend to diminish over time. The study concludes that policy-makers need to consider policy actions accompanying the exchange rate changes to ensure domestic price stability which consequently interacts with many macroeconomic indicators.
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Charfeddine, Lanouar, and Karim Barkat. "Do Oil and Gas Revenues promote Economic Diversification in Qatar?" In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0048.

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The aim of this paper is to explore the short- and long-term asymmetric impact of oil prices shocks and oil and gas revenues changes on the total real GDP, and the level of economic diversification of the Qatar economy. To this end, two econometric approaches have been used: (1) the A-B structural vector autoregressive (AB − SVARX) model with exogenous variables where four different asymmetric oil prices and oil and gas revenues measures have been employed, and (2) the nonlinear autoregressive distributed lag (NARDL) model. The results show that, in the short-run, the responses of both total real GDP and non-oil real GDP to negative shocks on real oil prices and real oil and gas revenues are higher than the impact of positive shocks, indicating evidence for the existence of asymmetric impact of shocks in the short-run. However, the results suggest that the impact of shocks do not last more than three quarters. This evidence for the existence of asymmetric behavior is also confirmed by the NARDL analysis, which shows that, in the long run, positive oil prices shocks and oil and gas revenues changes have higher impact on the two proxies of economic activity than negative changes do. A result that confirms the resilience of the Qatar economy to negative shocks and the positive role played by the energy sector in improving the Qatar economic diversification degree. Finally, the results show that the non-oil sector is completely resilient to negative shocks in the long run as the impact of negative shocks are insignificant on the non-oil real GDP. Several policies aimed to improve the level of economic diversification of the country and delink the government revenues from oil and gas revenues are proposed and discussed.
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Sağlam, Yağmur, and Hüseyin Avni Egeli. "Real Exchange Rate Effects on Trade and Immiserizing Growth: The Case of Turkey 2003-2013." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.00863.

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The aim of this study is to examine the long run relationship between real exchange rates and trade within terms of immiserizing growth for Turkey. The relationship between real exchange rates and trade are taking into consideration with two approach which are pass-through and standard theory. So it is observed that which approach is valid for Turkey. Also we tried to see if trade policies which on terms of trade create immiserizing growth or not during the period. We used unrestricted Vector Autoregressive Model (VAR) analysis to see the relationship between real exchange rate and trade rate, growth. Also we added a dummy into the VAR which demonstrate the structural break for global economic crisis as an exogenous variable. The results of the application are; in the long term there is no cointegration between trade rate and real exchange rates and growth. So for the short term; the dummy was statistically significant and affects the distribution of series; the relationship between trade rate and real exchange rates are very weak and rarely supports the Standard Theory. Also terms of trade impacts growth rate positively but the effects of growth rate on terms of trade is indeterminate. So there is not an immiserizing growth in Turkey for the period between 2003 and 2013.
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Li, Tao, Xueyu Li, and Xu Zhang. "The Design and Implementation of Vector Autoregressive Model and Structural Vector Autoregressive Model Based on Spark." In 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM). IEEE, 2017. http://dx.doi.org/10.1109/bigcom.2017.46.

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Shen, Yanning, Brian Baingana, and Georgios B. Giannakis. "Topology inference of directed graphs using nonlinear structural vector autoregressive models." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953411.

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Wai, Phoong Seuk, Sek Siok Kun, Mohd Tahir Ismail, Samsul Ariffin, and Abdul Karim. "Model performance between linear vector autoregressive and Markov switching vector autoregressive models on modelling structural change in time series data." In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC). IEEE, 2015. http://dx.doi.org/10.1109/ismsc.2015.7594083.

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Omenzetter, Piotr, and Simon Hoell. "Improved statistical damage classification in an experimental wind turbine blade based on vector autoregressive coefficients and sequential projection pursuit." In Health Monitoring of Structural and Biological Systems XII, edited by Tribikram Kundu. SPIE, 2018. http://dx.doi.org/10.1117/12.2295019.

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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 every moment. The results showed good agreements with one of the TVAR modeling and also with the stationary autoregressive (SAR) modeling analysis under stationary conditions. Furthermore, the instantaneous relative noise contribution was also estimated using the TVVAR coefficients and illustrated how the structure of a spectrum changed according to the ship manoeuvres for the first time. Optimum order of the model and Akaike’s information criterion were also examined for several changes of parameters. Moreover, it is confirmed that the TVVAR modeling can estimate the instantaneous cross spectra and relative noise contribution of ship motions even under non-stationary conditions.
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Ye, Awella. "Discover Contemporaneous Connections in Dynamic Network: Regularized Unified Structural Equation Modeling as a Hybrid Vector Autoregressive Model." In 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1588434.

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Yansong, Diao, Meng Dongmei, and Cao Yadong. "Structural Damage Identification of Offshore Jacket Platform Based on the Pseudofree Response Data." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23726.

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Due to the influences of the excitation and measurement noise, the misjudgments are inevitable when the coefficients of an autoregressive (AR) model in the time series combined with neural networks are used to identify the damage. So, in this study, the pseudofree response data are extracted from the acceleration responses data with the random decrement (RD) technique, the AR model is employed to fit the pseudofree response data, the Akaike information criterion (AIC) is used to estimate the order of AR model, the coefficients of the AR model will be changed if the structure is damaged, so we can determine if the structure is damaged according to the changes of the coefficients of the AR model. If the structure is damaged, the differences of the first 4 order AR coefficients pre and post damage are extracted and composed as damage sensitive vector which is put into back-propagation (BP) neural network to identify the damage location. The numerical model of a four-floor offshore jacket platform excited with white noise is used to testify the proposed damage identification method, the different amplitudes of the white noise excitation and the different level of the measurement noise are also considered. The simulation results show the proposed method is almost not affected by the changes of excitation amplitude, and when the noise level is no more than 5 percent, the damage location can be identified by the method correctly. The proposed method uses the acceleration responses to identify the damage directly, which is not dependent on the modal parameters (frequency, mode shape, damping), therefore, it is suitable for the on-line damage identification.
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