Academic literature on the topic 'Forecast error variance decomposition'
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Journal articles on the topic "Forecast error variance decomposition"
McKenzie, Andrew M., Harold L. Goodwin, and Rita I. Carreira. "Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets." Journal of Agricultural and Applied Economics 41, no. 1 (April 2009): 227–40. http://dx.doi.org/10.1017/s1074070800002650.
Full textGorodnichenko, Yuriy, and Byoungchan Lee. "Forecast Error Variance Decompositions with Local Projections." Journal of Business & Economic Statistics 38, no. 4 (July 18, 2019): 921–33. http://dx.doi.org/10.1080/07350015.2019.1610661.
Full textLanne, Markku, and Henri Nyberg. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models." Oxford Bulletin of Economics and Statistics 78, no. 4 (January 26, 2016): 595–603. http://dx.doi.org/10.1111/obes.12125.
Full textStaszewska-Bystrova, Anna. "Monte Carlo Analysis of Forecast Error Variance Decompositions under Alternative Model Identification Schemes." Acta Universitatis Lodziensis. Folia Oeconomica 5, no. 338 (September 28, 2018): 115–31. http://dx.doi.org/10.18778/0208-6018.338.07.
Full textGod', N. A., and stime Osekhebhen Eigbiremolen. "Savings, investment and economic growth in Nigeria: a forecast error variance decomposition analysis." African J. of Economic and Sustainable Development 3, no. 2 (2014): 103. http://dx.doi.org/10.1504/ajesd.2014.064376.
Full textZapata, Juan, and Juan Ciro. "The communication effects on inflation forecast errors: Empirical evidence from Colombia." Panoeconomicus, no. 00 (2020): 16. http://dx.doi.org/10.2298/pan180101016z.
Full textLee, King Fuei. "An Empirical Study of Dividend Payout and Future Earnings in Singapore." Review of Pacific Basin Financial Markets and Policies 13, no. 02 (June 2010): 267–86. http://dx.doi.org/10.1142/s0219091510001949.
Full textRena, Ravinder, and Albert V. Kamuinjo. "An Empirical Analysis of the Relationship Between Capital, Market Risks, and Liquidity Shocks in the Banking Industry." Studia Universitatis Babes-Bolyai Oeconomica 67, no. 2 (August 1, 2022): 67–83. http://dx.doi.org/10.2478/subboec-2022-0010.
Full textKamuinjo, Albert V., Ravinder Rena, and Andrew Maredza. "Impact of credit risk and profitability on liquidity shocks of Namibian banks: an application of the structural VAR model." Journal of Life Economics 8, no. 3 (July 31, 2021): 349–59. http://dx.doi.org/10.15637/jlecon.8.3.07.
Full textSmith, Kenneth L., Joe Brocato, and Russell E. Dabbs. "Professional forecast error as a function of a variable forecast horizon: A decomposition analysis." International Journal of Forecasting 7, no. 2 (August 1991): 155–63. http://dx.doi.org/10.1016/0169-2070(91)90050-6.
Full textDissertations / Theses on the topic "Forecast error variance decomposition"
Agbenyegah, Benjamin K. "An econometric approach to measuring productivity: Australia as a case study." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/219.
Full textRafiq, Shuddhasattwa. "Oil consumption, pollutant emission, oil proce volatility and economic activities in selected Asian Developing Economies." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/693.
Full textAgbenyegah, Benjamin Komla. "An econometric approach to measuring productivity : Australia as a case study /." Curtin University of Technology, School of Economics and Finance, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=17375.
Full textThis study finds that Australia experienced productivity growth in the 1950s, a slow down in the mid 1960s, a very strong productivity growth in the mid 1990s and another slowdown from 2000 onwards. The study finds evidence that human capital, FDI and ICT are very strong determinants of long-run GDP and productivity growth in Australia. The study finds that the three, four and the five factor models are likely to give better measures of productivity performance in Australia as these models recognise human capital, FDI and ICT and include them as separate factors in the production function, This study finds evidence that the previous studies on the Australia’s productivity puzzle have made a very significant omission by not considering human capital, FDI and ICT as additional exogenous variables and by excluding them from the production function for productivity analysis.
Lanagan, Gareth Daniel Edward. "Weather forecast error decomposition using rearrangements of functions." Thesis, Aberystwyth University, 2012. http://hdl.handle.net/2160/b489892f-7607-4125-90fb-46d8376edf8f.
Full textWolff, Laion. "RELAÇÃO ENTRE AS DEZ PRINCIPAIS BOLSAS DE VALORES DO MUNDO E SUAS CO-INTEGRAÇÕES." Universidade Federal de Santa Maria, 2011. http://repositorio.ufsm.br/handle/1/8207.
Full textGlobalization provoked in financial markets by means stock exchanges an interchange among the markets over the world. The aim of this study was to examine the relationship of the ten major main economic index of the world represented in New York (DJIA, S&P500 e Nasdaq), Tokyo (NIKKEI 225), London (FSTE 100), São Paulo (IBOV), Shanghai (SSE180), Paris (CAC-40), Frankfurt (DAX-30) and Buenos Aires (Merval) and looking for its co-integration, to demonstrate the behavior of these indexes and the long run equilibrium, from January of 2010 to March of 2011. To investigate the equilibrium and the long rum behavior the error correction model was used jointly with co-integration test and impulse response based on Cholesky decomposition. The results of this study show that the index of stock markets has long term equilibrium, and American markets, Argentina and English showed a strong influence over other markets. With this research we can infer that a relationship exists between the stock markets under study, confirming that the economy in a country can influence the others. In this sense, the contribution of this study, given this range of discussions involving the interconnection of economies with respect to trades made on the stock exchanges, was to show the relationships and influences in the world.
A internacionalização somada à abertura dos mercados financeiros transformou as economias antes fechadas em economias abertas, provocou um intercâmbio entre as economias mundiais por meio das bolsas de valores. O objetivo deste estudo é examinar a relação entre os dez principais índices econômicos do mundo, sendo eles: Nova York (DJIA, S&P500 e Nasdaq), Tóquio (Nikkei 225), Londres (FSTE 100), São Paulo (IBOV), Shangai (SSE180), Paris (CAC), Frankfurt (DAX-30) e Bueno Aires (Merval), por meio da análise de co-integrações para demonstrar o comportamento desses índices e seus equilíbrios no período de janeiro de 2010 a março de 2011. Para investigar e verificar o comportamento em longo prazo, foi utilizado o modelo de correção de erros e teste de impulso-resposta baseado na decomposição de Cholesky. Os resultados deste estudo mostram que existe equilíbrio em longo prazo entre os índices do mercado de ações. Os mercados americano, argentino e inglês mostraram forte influência sobre os demais mercados. Com esta pesquisa, verifica-se que existe uma relação entre os mercados de ações estudados, confirmando que a economia de um país influencia as demais. A contribuição deste estudo é verificar a assertiva das discussões atuais sobre a dependência das economias mundiais com as negociações por meio da bolsa de valores.
Singh, Shiu Raj. "Dynamics of macroeconomic variables in Fiji : a cointegrated VAR analysis." Diss., Lincoln University, 2008. http://hdl.handle.net/10182/774.
Full textGolab, Anna. "An investigation into the volatility and cointegration of emerging European stock markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/572.
Full textAkpan, Nkereuwem I. "The Impact of External Shocks on Nigeria’s GDP Performance within the Context of the Global Financial Crisis." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17454.
Full textGonçalves, Daniel Fernandes. "Business cycle dynamics across Europe: a cluster analysis." Master's thesis, 2016. http://hdl.handle.net/10071/13216.
Full textThis dissertation aims to analyze the dynamics of business cycles across European countries between 1960Q1 and 2016Q1. For such purpose we identify country-groups of national deviation cycles through Hierarchical Agglomerative Clustering with the Ward’s method. The clustering technique suggests the existence of three country-groups, which include, aside from other countries, France and Spain in Cluster 1, United Kingdom and Denmark in Cluster 2 and Germany and Italy in Cluster 3. We execute an extensive analysis on business cycle stylized facts, synchronization and turning points detection over the clusters’ deviation cycles. Further on, we analyze the propagation of economic shocks through a VAR model, over which we study Granger-causalities, Impulse Response Functions and Forecast Error Variance Decomposition. Our results show that both Cluster 1 and Cluster 2 share similar cyclical characteristics when compared to Cluster 3. Nevertheless, Cluster 1 and Cluster 3 appear to be the most synchronous pair, and simultaneously verify the largest proportion of time spent in the same cyclical phase. We show that there has been an increasing business cycle synchronization in Europe since the beginning of the 90’s. The structural analysis shows that Cluster 1 and Cluster 2 have the strongest permanent cumulative shocks, whereas Cluster 3 induces not only the weakest impulses but also explains the smallest fraction of the counterparts’ forecast error variance decomposition. These conclusions question the "German Dominance" hypothesis and allow the identification of alternative major economic propellers in Europe.
A presente tese pretende analisar as dinâmicas dos ciclos económicos na Europa no período compreendido entre 1960Q1 e 2016Q1. Como tal, procedemos à identificação de grupos de ciclos económicos nacionais através de Clusterização Hierárquica Aglomerativa com o método de Ward. A Clusterização sugere a existência de três grupos que incluem, além de outros países, França e Espanha no Cluster 1, Reino Unido e Dinamarca no Cluster 2, e Alemanha e Itália no Cluster 3. Analisamos as principais características, sincronização e cronologia de pontos de inflexão dos ciclos económicos dos clusters. Estudamos ainda a propagação de choques económicos com um modelo VAR, sobre o qual concluímos sobre causalidade à Granger, funções de impulso-resposta e decomposição de variância. Os resultados mostram que o Cluster 1 e Cluster 2 apresentam maiores semelhanças nas características dos seus ciclos quando comparados ao Cluster 3. Simultaneamente, o Cluster 1 e Cluster 3 apresentam quer o maior nível de sincronização quer a maior fração de tempo partilhada na mesma fase cíclica. Concluímos também que o nível de sincronização dos ciclos económicos na Europa apresenta uma tendência crescente, especialmente após os anos 90. A análise estrutural conclui que o Cluster 1 e Cluster 2 produzem os choques permanentes mais fortes, enquanto que o Cluster 3 induz os impulsos mais fracos, além de explicar a menor parte da decomposição de variância do erro de previsão dos restantes. As presentes conclusões questionam a hipótese de "Domínio Alemão" e permitem a identificação de outros propulsores económicos na Europa.
Xu, Jin. "Essays in Financial Econometric Investigations of Farmland Valuations." Thesis, 2013. http://hdl.handle.net/1969.1/150974.
Full textBook chapters on the topic "Forecast error variance decomposition"
Amisano, Gianni, and Carlo Giannini. "Impulse response analysis and forecast error variance decomposition in SVAR modelling." In Topics in Structural VAR Econometrics, 60–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60623-6_5.
Full textGiannini, Carlo. "Impulse Response Analysis and Forecast Error Variance Decomposition in SVAR Modeling." In Lecture Notes in Economics and Mathematical Systems, 44–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02757-8_5.
Full textAgapitos, Alexandros, Anthony Brabazon, and Michael O’Neill. "Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition." In Lecture Notes in Computer Science, 438–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32937-1_44.
Full textPeter Eze, Gbalam, and Tonprebofa Waikumo Okotori. "Exchange Rate Volatility and Monetary Policy Shocks." In Macroeconomics [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99606.
Full textGylych, Jelilov, Abdullahi Ahmad Jibrin, Bilal Celik, and Abdurrahman Isik. "Impact of Oil Price Fluctuation on the Economy of Nigeria, the Core Analysis for Energy Producing Countries." In Energy Management Systems in Process Industries - Current Practice and Challenges in Era of Industry 4.0 [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94055.
Full textOzer, Mustafa, and A. Erinç Yeldan. "The Relationship between Current Account Deficits and Unemployment in Turkey." In Handbook of Research on Comparative Economic Development Perspectives on Europe and the MENA Region, 492–510. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9548-1.ch020.
Full textPolyak, Ilya. "Variability of ARMA Processes." In Computational Statistics in Climatology. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099997.003.0006.
Full textDuell, Peter, and Xin Yao. "Implementing Negative Correlation Learning in Evolutionary Ensembles with Suitable Speciation Techniques." In Pattern Recognition Technologies and Applications, 344–69. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch016.
Full textPang, Kwok Pan. "Time Series Analysis and Structural Change Detection." In Dynamic and Advanced Data Mining for Progressing Technological Development, 377–95. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-908-3.ch015.
Full textLi, Peilin, Sang-Heon Lee, and Hung-Yao Hsu. "Use of Bi-Camera and Fusion of Pairwise Real Time Citrus Fruit Image for Classification Application." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 54–81. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6030-4.ch004.
Full textConference papers on the topic "Forecast error variance decomposition"
Charaeva, Marina V., Marina A. Kuznetsova, and Song Yansong. "The impact of commodity market volatility on China's stock market." In Sustainable and Innovative Development in the Global Digital Age. Dela Press Publishing House, 2022. http://dx.doi.org/10.56199/dpcsebm.zmib9194.
Full textDe Giorgi, Maria Grazia, Marco Tarantino, and Antonio Ficarella. "A New Hybrid Method for Wind Power Forecasting Based on Wavelet Decomposition and Artificial Neural Networks." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46382.
Full textSilva, Ramon Gomes, Matheus Henrique Dal Molin Ribeiro, José Henrique Kleinubing Larcher, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Artificial Intelligence and Signal Decomposition Approach Applied to Retail Sales Forecasting." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-25.
Full textReports on the topic "Forecast error variance decomposition"
Clark, Todd E., Gergely Ganics, and Elmar Mertens. Constructing fan charts from the ragged edge of SPF forecasts. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-202236.
Full textÁlvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23552.
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