Relevant bibliographies by topics / Vector autoregressive model

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

Academic literature on the topic 'Vector autoregressive model'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 May 2022

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Journal articles on the topic "Vector autoregressive model"

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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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Euán, Carolina, and Ying Sun. "Bernoulli vector autoregressive model." Journal of Multivariate Analysis 177 (May 2020): 104599. http://dx.doi.org/10.1016/j.jmva.2020.104599.

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Barr, G. D. I. "“A Vector Autoregressive Model” - Reply." Studies in Economics and Econometrics 14, no. 3 (November 30, 1990): 89. http://dx.doi.org/10.1080/03796205.1990.12128994.

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Bouwer, B. "“A Vector Autoregressive Model” - Kommentaar." Studies in Economics and Econometrics 14, no. 3 (November 30, 1990): 87. http://dx.doi.org/10.1080/03796205.1990.12128993.

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Koop, Gary, and Dimitris Korobilis. "Model uncertainty in Panel Vector Autoregressive models." European Economic Review 81 (January 2016): 115–31. http://dx.doi.org/10.1016/j.euroecorev.2015.09.006.

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Grynkiv, Galyna, and Lars Stentoft. "Stationary Threshold Vector Autoregressive Models." Journal of Risk and Financial Management 11, no. 3 (August 5, 2018): 45. http://dx.doi.org/10.3390/jrfm11030045.

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This paper examines the steady state properties of the Threshold Vector Autoregressive model. Assuming that the trigger variable is exogenous and the regime process follows a Bernoulli distribution, necessary and sufficient conditions for the existence of stationary distribution are derived. A situation related to so-called “locally explosive models”, where the stationary distribution exists though the model is explosive in one regime, is analysed. Simulations show that locally explosive models can generate some of the key properties of financial and economic data. They also show that assessing the stationarity of threshold models based on simulations might well lead to wrong conclusions.
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Zhu, Huafeng, Xingfa Zhang, Xin Liang, and Yuan Li. "On a vector double autoregressive model." Statistics & Probability Letters 129 (October 2017): 86–95. http://dx.doi.org/10.1016/j.spl.2017.05.002.

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Fong, P. W., W. K. Li, C. W. Yau, and C. S. Wong. "On a mixture vector autoregressive model." Canadian Journal of Statistics 35, no. 1 (March 2007): 135–50. http://dx.doi.org/10.1002/cjs.5550350112.

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Chapman, David, Mark A. Cane, Naomi Henderson, Dong Eun Lee, and Chen Chen. "A Vector Autoregressive ENSO Prediction Model." Journal of Climate 28, no. 21 (October 30, 2015): 8511–20. http://dx.doi.org/10.1175/jcli-d-15-0306.1.

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Abstract The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment.
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Bouwer, B. "“A Vector Autoregressive Model” - Verdere Kommentaar." Studies in Economics and Econometrics 14, no. 3 (November 30, 1990): 91–92. http://dx.doi.org/10.1080/03796205.1990.12128995.

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Dissertations / Theses on the topic "Vector autoregressive model"

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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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Schnücker, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.

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Camehl, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.

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Horton, Wendy Elizabeth. "A vector autoregressive model of a regional Phillips curve in the United States." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30515.

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Lee, Joo Young, and Youn Mi Lee. "Dynamic Impact of Aging on Income Inequality in the U.S. with Vector Autoregressive Model." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/secfr-conf/2020/schedule/57.

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Income inequality has been showing a steady increase for past decades and will be worsened in the future (Piketty, 2014). One of the most important factors to explain the worsening income inequality can be aging. Previous studies on aging focus on its impact on traditional issues such as health, retirement, and economic growth. This study finds the direct relationship between aging and income inequality using the vector autoregressive (VAR) model (Blanchard and Quah, 1989). The VAR model is useful to analyze the long-run response of aging on income inequality. The empirical results will verify the negative impact of aging on income inequality in the U.S. The governmental efforts to reduce the negative impact of aging on health care and pensions could delay the worsening income inequality.
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Serpeka, Rokas. "Analyzing and modelling exchange rate data using VAR framework." Thesis, KTH, Matematik (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-94180.

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Abstract   In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts
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Crespo, Cuaresma Jesus, Gernot Doppelhofer, Martin Feldkircher, and Florian Huber. "Spillovers from US monetary policy: Evidence from a time-varying parameter global vector autoregressive model." Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society, 2019. http://dx.doi.org/10.1111/rssa.12439.

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The paper develops a global vector auto-regressive model with time varying pa- rameters and stochastic volatility to analyse whether international spillovers of US monetary policy have changed over time. The model proposed enables us to assess whether coefficients evolve gradually over time or are better characterized by infrequent, but large, breaks. Our find- ings point towards pronounced changes in the international transmission of US monetary policy throughout the sample period, especially so for the reaction of international output, equity prices and exchange rates against the US dollar. In general, the strength of spillovers has weakened in the aftermath of the global financial crisis. Using simple panel regressions, we link the vari- ation in international responses to measures of trade and financial globalization. We find that a broad trade base and a high degree of financial integration with the world economy tend to cushion risks stemming from a foreign shock such as US tightening of monetary policy, whereas a reduction in trade barriers and/or a liberalization of the capital account increase these risks.
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Degerli, Mecit Mert. "Fault detection of bearings based on AutoRegressive modelling and Support Vector Machine classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Bearings are the common mechanical components and they have an authentic role generally in every kind of machines or applications. Therefore it becomes more and more important to understand properly the behaviour and the nature of the bearing and also their working conditions. For this reason, the data from the components on the machine as bearings in the modern industry; the importance of reducing machine downtime and machine costs, increasing machine performance and achieving a more stable operation is increasing day by day. For that purpose, different solutions have been provided to detect and diagnose faulty situations in bearings with different application-oriented approaches. In this study, by means of vibration based method; the vibration signals coming from the bearings are filtered by Wavelet analysis to avoid non-stationarities and then the filtered signal is modeled according to the AutoRegressive (AR) process. The coefficients of the related model is the basis for the Machine Learning algorithm Support Vector Machines (SVM) by optimizing with Bayesian Optimization to perform Fault Detection and Identification (FDI). This usage of AR process provides a useful forward-looking method for appropriate feature selection. In this study the data which was provided by Center for Intelligent Maintenance Systems is used and the results have been discussed and concluded.
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Louw, Riëtte. "Forecasting tourism demand for South Africa / Louw R." Thesis, North-West University, 2011. http://hdl.handle.net/10394/7607.

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Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations.
Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.
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Dashti, Hossein, Antonio J. Conejo, Ruiwei Jiang, and Jianhui Wang. "Weekly Two-Stage Robust Generation Scheduling for Hydrothermal Power Systems." IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/622668.

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As compared to short-term forecasting (e.g., 1 day), it is often challenging to accurately forecast the volume of precipitation in a medium-term horizon (e.g., 1 week). As a result, fluctuations in water inflow can trigger generation shortage and electricity price spikes in a power system with major or predominant hydro resources. In this paper, we study a two-stage robust scheduling approach for a hydrothermal power system. We consider water inflow uncertainty and employ a vector autoregressive (VAR) model to represent its seasonality and accordingly construct an uncertainty set in the robust optimization approach. We design a Benders' decomposition algorithm to solve this problem. Results are presented for the proposed approach on a real-world case study.
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Books on the topic "Vector autoregressive model"

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

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Mocan, H. Naci. Business cycles and fertility dynamics in the U.S.: A vector-autoregressive model. Cambridge, MA (1050 Massachusetts Avenue, Cambridge, MA 02138): National Bureau of Economic Research, 1989.

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Johansen, Søren. The asymptotic variance of the estimated roots in a cointegrated vector autoregressive model. Florence: European University Institute, Department of Economics, 2001.

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Hasan, Mohammad S. Monetary policy, fiscal policy, and aggregate economic activity in a vector autoregressive model. Newcastle upon Tyne: University of Northumbria at Newcastle, 1995.

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Johansen, Søren. Controlling inflation in a cointegrated vector autoregressive model with an application to US data. San Domenico: European University Institute, Department of Economics, 2001.

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Johansen, Søren. A small sample correction of the test for cointegrating rank in the vector autoregressive model. San Domenico (FI) Italy: European University Institute, 2000.

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Johansen, Søren. A small sample correction of the test for cointegrating rank in the vector autoregressive model. Badia Fiesolana, San Domenico: European University Institute, 2000.

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Empirical vector autoregressive modeling. Berlin: Springer-Verlag, 1994.

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

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

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Book chapters on the topic "Vector autoregressive model"

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Akkaya, Murat. "Vector Autoregressive Model and Analysis." In Handbook of Research on Emerging Theories, Models, and Applications of Financial Econometrics, 197–214. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54108-8_8.

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Krolzig, Hans-Martin. "The Markov-Switching Vector Autoregressive Model." In Lecture Notes in Economics and Mathematical Systems, 6–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-51684-9_2.

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Xiao, Xiong, Haizhou Li, and Eng Siong Chng. "Vector Autoregressive Model for Missing Feature Reconstruction." In Chinese Spoken Language Processing, 315–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11939993_35.

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Li, Yuemeng, Aidong Lu, Xintao Wu, and Shuhan Yuan. "Dynamic Anomaly Detection Using Vector Autoregressive Model." In Advances in Knowledge Discovery and Data Mining, 600–611. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16148-4_46.

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Hosoya, Yuzo, Kosuke Oya, Taro Takimoto, and Ryo Kinoshita. "Inference Based on the Vector Autoregressive and Moving Average Model." In Characterizing Interdependencies of Multiple Time Series, 65–102. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6436-4_4.

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Zhang, Qianqian, and Qingming Gui. "Carrier-Phase RAIM Algorithm Based on a Vector Autoregressive Model." In Lecture Notes in Electrical Engineering, 125–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46635-3_11.

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Teetranont, Teerawut, Woraphon Yamaka, and Songsak Sriboonchitta. "Generalize Weighted in Interval Data for Fitting a Vector Autoregressive Model." In Predictive Econometrics and Big Data, 600–612. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_43.

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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, 174–93. Wallingford: 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 the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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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, 174–93. Wallingford: 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 the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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Jefferson, Al, and J. Pabelic. "Bootstrap Efficiency on Granger Causality Test in Bivariate Vector Autoregressive (VAR) Model." In Lecture Notes in Electrical Engineering, 339–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40630-0_43.

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Conference papers on the topic "Vector autoregressive model"

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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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"Constrained Estimation of Mixture Vector Autoregressive Model." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.d2.wong.

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Huan Wang, Lun Bai, Jianmei Xu, and Wanchun Fei. "EEG recognition through Time-varying Vector Autoregressive Model." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7381956.

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He, Kaijian, Yingchao Zou, and Kin Keung Lai. "Exchange Rate Forecasting Using Multiscale Vector Autoregressive Model." In 2013 Sixth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2013. http://dx.doi.org/10.1109/bife.2013.40.

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Fujiki, Jun, Yasuhiko Kiuchi, Masaru Tanaka, and Taketoshi Mishima. "Invariants from the three-dimensional vector autoregressive model." In International Symposium on Optical Science and Technology, edited by Longin J. Latecki, David M. Mount, Angela Y. Wu, and Robert A. Melter. SPIE, 2001. http://dx.doi.org/10.1117/12.447269.

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Goyal, Abhishek, and Rahul Garg. "Effective EEG Connectivity by Sparse Vector Autoregressive Model." In CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3371158.3371163.

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Fujiki, Jun, and Masaru Tanaka. "Properties of the three-dimensional vector autoregressive model." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Longin J. Latecki, Robert A. Melter, David M. Mount, and Angela Y. Wu. SPIE, 1999. http://dx.doi.org/10.1117/12.364097.

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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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SALCEDO, G. E., O. E. MOLINA, and R. F. PORTO. "A WAVELET-BASED TIME-VARYING IRREGULAR VECTOR AUTOREGRESSIVE MODEL." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814520829_0022.

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Goyal, Vipul, Mengyu Xu, and Jayanta Kapat. "Use of Vector Autoregressive Model for Anomaly Detection in Utility Gas Turbines." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90995.

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Abstract This study is based on time-series data from the combined cycle utility gas turbines consisting of three-gas turbine units and one steam turbine unit. We construct a multi-stage vector autoregressive model for the nominal operation of powerplant assuming sparsity in the association among variables and use this as a basis for anomaly detection and prediction. This prediction is compared with the time-series data of the plant-operation containing anomalies. Granger causality networks, which are based on the associations between the time series streams, are learned as an important implication from the vector autoregressive modelling. Anomaly is detected by comparing the observed measurements against their predicted value.
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Reports on the topic "Vector autoregressive model"

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

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

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5

Dime, Roselle, Juzhong Zhuang, and Edimon Ginting. Estimating Fiscal Multipliers in Selected Asian Economies. Asian Development Bank, August 2021. http://dx.doi.org/10.22617/wps210309-2.

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Abstract:
The surge of the coronavirus disease (COVID-19) pandemic has driven countries worldwide to launch substantial stimulus packages to support economic recovery. This paper estimates effects of fiscal measures on output using data from 2000 to 2019 for a panel of nine developing Asian economies and a vector autoregression model. Results show that (i) the 4-quarter and 8-quarter cumulative fiscal multipliers for general government spending range between 0.73 and 0.88 in baselines, in line with recent estimates for developed countries but larger than those for developing countries; (ii) government spending is more effective than tax cuts in boosting the economy; and (iii) an accommodative monetary policy regime can make fiscal measures more effective.
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