The Use of Combined Models in the Construction of Foodstuffs Consumption Forecasting in the Czech Republic
Author
Abstract
Suggested Citation
DOI: 10.22004/ag.econ.276075
Download full text from publisher
References listed on IDEAS
- Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
- Papagera, A. & Ioannou, K. & Zaimes, G. & Iakovoglou, V. & Simeonidou, M., 2014. "Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(4), pages 1-11, December.
- Martin,Vance & Hurn,Stan & Harris,David, 2013. "Econometric Modelling with Time Series," Cambridge Books, Cambridge University Press, number 9780521196604.
- Martin,Vance & Hurn,Stan & Harris,David, 2013. "Econometric Modelling with Time Series," Cambridge Books, Cambridge University Press, number 9780521139816.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Köppelová, J. & Jindrová, A., 2017. "Comparative Study of Short-Term Time Series Models: Use of Mobile Telecommunication Services in CR Regions," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(1), March.
- Vito Polito & Yunyi Zhang, 2021. "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," CESifo Working Paper Series 9395, CESifo.
- Roman Hušek & Tomáš Formánek, 2014. "Alternative specification, estimation and identification of vector autoregressions [Alternativní specifikace, odhad a identifikace vektorových autoregresí]," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2014(4), pages 52-72.
- Esposti, Roberto, 2017. "What Makes Commodity Prices Move Together? An Answer From A Dynamic Factor Model," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 260889, European Association of Agricultural Economists.
- Dante Amengual & Enrique Sentana & Zhanyuan Tian, 2022.
"Gaussian Rank Correlation and Regression,"
Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 269-306,
Emerald Group Publishing Limited.
- Dante Amengual & Enrique Sentana & Zhanyuan Tian, 2020. "Gaussian Rank Correlation and Regression," Working Papers wp2020_2004, CEMFI.
- Sentana, Enrique & Amengual, Dante & Tian, Zhanyuan, 2020. "Gaussian rank correlation and regression," CEPR Discussion Papers 14914, C.E.P.R. Discussion Papers.
- Mansur, Alfan, 2016. "Kebijakan Moneter dan Volatilitas Pasar Keuangan [Monetary Policy and the Financial Market's Volatility]," MPRA Paper 93880, University Library of Munich, Germany, revised 15 Sep 2016.
- Eduardo Silva & Alex Ferreira, 2023. "Risk-sharing within Brazil and South America," Empirical Economics, Springer, vol. 65(2), pages 661-695, August.
- Dery, Cosmas & Serletis, Apostolos, 2021.
"Interest Rates, Money, And Economic Activity,"
Macroeconomic Dynamics, Cambridge University Press, vol. 25(7), pages 1842-1891, October.
- Apostolos Serletis & Cosmas Dery, "undated". "Interest Rates, Money, and Economic Activity," Working Papers 2019-16, Department of Economics, University of Calgary, revised 08 Oct 2019.
- Dungey Mardi & Martin Vance L. & Tang Chrismin & Tremayne Andrew, 2020. "A threshold mixed count time series model: estimation and application," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-18, April.
- Gregor Dorfleitner & Carina Lung, 2018. "Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect," Journal of Asset Management, Palgrave Macmillan, vol. 19(7), pages 472-494, December.
- Mansur, Alfan, 2015. "Identifying Shocks on the Economic Fluctuations in Indonesia and US: The Role of Oil Price Shocks in a Structural Vector Autoregression Model," MPRA Paper 94018, University Library of Munich, Germany, revised 09 Jun 2015.
- Fatih Chellai, 2021. "What Can SVAR Models Tell us About the Impact of Public Expenditure Shocks on Macroeconomic Variables in Algeria? A Slight Hint to the COVID-19 Pandemic," Folia Oeconomica Stetinensia, Sciendo, vol. 21(2), pages 21-37, December.
- Vito Polito, 2020. "Nonlinear Business Cycle and Optimal Policy: A VSTAR Perspective," CESifo Working Paper Series 8060, CESifo.
- Anke D. Leroux & Vance L. Martin & Kathryn A. St. John, 2022. "Modeling time varying risk of natural resource assets: Implications of climate change," Quantitative Economics, Econometric Society, vol. 13(1), pages 225-257, January.
- Eduardo de Sa Fortes Leitao Rodrigues, 2023. "Uncertainty and the effectiveness of fiscal policy in the United States and Brasil: SVAR Approach," Working Papers 2023.03, International Network for Economic Research - INFER.
- Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
- Yu kun Wang & Li Zhang, 2021. "Underground economy and GDP growth: Evidence from China’s tax reforms," Journal of Tax Reform, Graduate School of Economics and Management, Ural Federal University, vol. 7(1), pages 87-107.
- David Harris & Hsein Kew, 2014. "Portmanteau Autocorrelation Tests Under Q-Dependence And Heteroskedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 203-217, May.
- Fabio L. Mattos & Rodrigo Lanna Franco da Silveira, 2018. "The Expansion of the Brazilian Winter Corn Crop and Its Impact on Price Transmission," IJFS, MDPI, vol. 6(2), pages 1-17, April.
- Eduardo de Sá Fortes Leitão Rodrigues, 2020. "Uncertainty And The Effectiveness Of Fiscal Policy In The United States And Brazil: Svar Approach," Working Papers REM 2020/0150, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
More about this item
Keywords
Research Methods/ Statistical Methods;Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aolpei:276075. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/fevszcz.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.