Estimation Bias and Feasible Conditional Forecasts from the First-Order Moving Average Model
Author
Abstract
Suggested Citation
DOI: 10.1515/jtse-2013-0015
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Phillips, Peter C. B., 1979. "The sampling distribution of forecasts from a first-order autoregression," Journal of Econometrics, Elsevier, vol. 9(3), pages 241-261, February.
- Bao, Yong, 2007. "Finite-Sample Properties Of Forecasts From The Stationary First-Order Autoregressive Model Under A General Error Distribution," Econometric Theory, Cambridge University Press, vol. 23(4), pages 767-773, August.
- Lanne, Markku & Luoto, Jani, 2012.
"Has US inflation really become harder to forecast?,"
Economics Letters, Elsevier, vol. 115(3), pages 383-386.
- Lanne, Markku & Luoto, Jani, 2010. "Has U.S. Inflation Really Become Harder to Forecast?," MPRA Paper 29992, University Library of Munich, Germany.
- James H. Stock & Mark W. Watson, 2007.
"Why Has U.S. Inflation Become Harder to Forecast?,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
- James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
- Bao, Yong & Ullah, Aman, 2007. "The second-order bias and mean squared error of estimators in time-series models," Journal of Econometrics, Elsevier, vol. 140(2), pages 650-669, October.
- James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
- James H. Stock & Mark W. Watson, 2007. "Erratum to “Why Has U.S. Inflation Become Harder to Forecast?”," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
- Rilstone, Paul & Srivastava, V. K. & Ullah, Aman, 1996. "The second-order bias and mean squared error of nonlinear estimators," Journal of Econometrics, Elsevier, vol. 75(2), pages 369-395, December.
- Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488, Decembrie.
- Schmidt, Peter, 1977. "Some Small Evidence on the Distribution of Dynamic Simulation Forecasts," Econometrica, Econometric Society, vol. 45(4), pages 997-1005, May.
- James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
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.- Verbrugge, Randal & Zaman, Saeed, 2023.
"The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model,"
Energy Economics, Elsevier, vol. 123(C).
- Randal J. Verbrugge & Saeed Zaman, 2023. "The Hard Road to a Soft Landing: Evidence from a (Modestly) Nonlinear Structural Model," Working Papers 23-03, Federal Reserve Bank of Cleveland.
- Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
- Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
- James M. Nason & Gregor W. Smith, 2021.
"Measuring the slowly evolving trend in US inflation with professional forecasts,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
- James M. Nason & Gregor W. Smith, 2013. "Measuring The Slowly Evolving Trend In Us Inflation With Professional Forecasts," Working Paper 1316, Economics Department, Queen's University.
- James M. Nason & Gregor W. Smith, 2014. "Measuring the Slowly Evolving Trend in US Inflation with Professional Forecasts," CAMA Working Papers 2014-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Ruhollah Eskandari & Morteza Zamanian, 2023. "Heterogeneous responses to corporate marginal tax rates: Evidence from small and large firms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1018-1047, November.
- Gary Koop & Dimitris Korobilis, 2023.
"Bayesian Dynamic Variable Selection In High Dimensions,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
- Gary Koop & Dimitris Korobilis, 2018. "Bayesian dynamic variable selection in high dimensions," Papers 1809.03031, arXiv.org, revised May 2020.
- Korobilis, Dimitris & Koop, Gary, 2020. "Bayesian dynamic variable selection in high dimensions," MPRA Paper 100164, University Library of Munich, Germany.
- Gary Koop & Dimitris Korobilis, 2020. "Bayesian dynamic variable selection in high dimensions," Working Papers 2020_11, Business School - Economics, University of Glasgow.
- Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
- Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023.
"From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks,"
Papers
2311.16333, arXiv.org, revised Apr 2024.
- Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
- Pablo Guerróon‐Quintana & Molin Zhong, 2023.
"Macroeconomic forecasting in times of crises,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
- Pablo Guerrón-Quintana & Molin Zhong, 2017. "Macroeconomic Forecasting in Times of Crises," Finance and Economics Discussion Series 2017-018, Board of Governors of the Federal Reserve System (U.S.).
- Martínez-García Enrique, 2018.
"Modeling time-variation over the business cycle (1960–2017): an international perspective,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-25, December.
- Enrique Martínez García, 2018. "Modeling Time-Variation Over the Business Cycle (1960-2017): An International Perspective," Globalization Institute Working Papers 348, Federal Reserve Bank of Dallas.
- Lukmanova, Elizaveta & Rabitsch, Katrin, 2023. "Evidence on monetary transmission and the role of imperfect information: Interest rate versus inflation target shocks," European Economic Review, Elsevier, vol. 158(C).
- Anjara Lalaina Jocelyn Rakotoarisoa, 2024. "Modélisations Univariées de l’Inflation Mensuelle à Madagascar : l’Atout du Modèle LSTM, un Réseau de Neurones Récurrents," Post-Print hal-04766563, HAL.
- Bhattacharya, Rudrani & Kapoor, Mrigankshi, 2020. "Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series," Working Papers 20/323, National Institute of Public Finance and Policy.
- Yong Bao, 2015. "Should We Demean the Data?," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 163-171, May.
- Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023.
"Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks,"
International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2020. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Papers 2011.07920, arXiv.org, revised Feb 2022.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers 2021.06, Bank of Israel.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print emse-04624940, HAL.
- Hauzenberger Niko & Huber Florian & Pfarrhofer Michael & Zörner Thomas O., 2021.
"Stochastic model specification in Markov switching vector error correction models,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
- Huber, Florian & Pfarrhofer, Michael & Zörner, Thomas O., 2018. "Stochastic model specification in Markov switching vector error correction models," Working Papers in Economics 2018-3, University of Salzburg.
- Niko Hauzenberger & Florian Huber & Michael Pfarrhofer & Thomas O. Zorner, 2018. "Stochastic model specification in Markov switching vector error correction models," Papers 1807.00529, arXiv.org, revised Sep 2019.
- Mikael Juselius & Claudio Borio & Piti Disyatat & Mathias Drehmann, 2017.
"Monetary Policy, the Financial Cycle, and Ultra-Low Interest Rates,"
International Journal of Central Banking, International Journal of Central Banking, vol. 13(3), pages 55-89, September.
- Juselius, Mikael & Borio, Claudio & Disyatat, Piti & Drehmann, Mathias, 2016. "Monetary policy, the financial cycle and ultralow interest rates," Bank of Finland Research Discussion Papers 24/2016, Bank of Finland.
- Mikael Juselius & Claudio Borio & Piti Disyatat & Mathias Drehmann, 2017. "Monetary Policy, the Financial Cycle and Ultra-low Interest Rates," PIER Discussion Papers 55, Puey Ungphakorn Institute for Economic Research.
- Mikael Juselius & Claudio Borio & Piti Disyatat & Mathias Drehmann, 2016. "Monetary policy, the financial cycle and ultra-low interest rates," BIS Working Papers 569, Bank for International Settlements.
- Lansing, Kevin J., 2021.
"Endogenous forecast switching near the zero lower bound,"
Journal of Monetary Economics, Elsevier, vol. 117(C), pages 153-169.
- Kevin J. Lansing, 2019. "Endogenous Forecast Switching Near the Zero Lower Bound," Working Paper Series 2017-24, Federal Reserve Bank of San Francisco.
- Takushi Kurozumi & Willem Van Zandweghe, 2023.
"A Theory of Intrinsic Inflation Persistence,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 1961-2000, December.
- Takushi Kurozumi & Willem Van Zandweghe, 2016. "Price Dispersion and Inflation Persistence," Research Working Paper RWP 16-9, Federal Reserve Bank of Kansas City.
- Takushi Kurozumi & Willem Van Zandweghe, 2019. "A Theory of Intrinsic Inflation Persistence," Working Papers 19-16, Federal Reserve Bank of Cleveland.
- Takushi Kurozumi & Willem Van Zandweghe, 2023. "A Theory of Intrinsic Inflation Persistence," Bank of Japan Working Paper Series 23-E-3, Bank of Japan.
- Chen, Jiazi & Hong, Zhiwu & Niu, Linlin, 2025. "Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution," International Journal of Forecasting, Elsevier, vol. 41(1), pages 153-174.
More about this item
Keywords
bias; moving average; feasible forecasts;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation 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:bpj:jtsmet:v:6:y:2013:i:1:p:63-80:n:4. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.