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Angelia L. Grant

Personal Details

First Name:Angelia
Middle Name:L.
Last Name:Grant
Suffix:
RePEc Short-ID:pgr551
[This author has chosen not to make the email address public]

Affiliation

Economics Discipline Group
Business School
University of Technology Sydney

Sydney, Australia
http://business.uts.edu.au/economics/
RePEc:edi:edutsau (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Joshua C C Chan & Angelia L Grant, 2017. "Measuring the output gap using stochastic model specification search," CAMA Working Papers 2017-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. Joshua C.C. Chan & Angelia L. Grant, 2016. "Reconciling output gaps: unobserved components model and Hodrick-Prescott filter," CAMA Working Papers 2016-44, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  3. Angelia Grant & Wilma Gillies & Ray Harris & Melissa Ljubic, 2016. "An Australian Labour Market Conditions Index," Treasury Working Papers 2016-04, The Treasury, Australian Government, revised Dec 2016.
  4. Joshua C.C. Chan & Angelia L. Grant, 2015. "A Bayesian model comparison for trend-cycle decompositions of output," CAMA Working Papers 2015-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  5. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  6. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  7. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  8. Joshua C.C. Chan & Angelia L. Grant, 2014. "Fast Computation of the Deviance Information Criterion for Latent Variable Models," CAMA Working Papers 2014-09, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

Articles

  1. Grant, Angelia L., 2018. "The Great Recession and Okun's law," Economic Modelling, Elsevier, vol. 69(C), pages 291-300.
  2. Angelia L. Grant, 2017. "The Early Millennium Slowdown: Replicating the Peersman (2005) Results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 224-232, January.
  3. Grant, Angelia L. & Chan, Joshua C.C., 2017. "Reconciling output gaps: Unobserved components model and Hodrick–Prescott filter," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 114-121.
  4. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
  5. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
  6. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Modeling energy price dynamics: GARCH versus stochastic volatility," Energy Economics, Elsevier, vol. 54(C), pages 182-189.
  7. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(4), pages 772-802.
  8. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Angelia L. Grant, 2017. "The Early Millennium Slowdown: Replicating the Peersman (2005) Results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 224-232, January.

    Mentioned in:

    1. The Early Millennium Slowdown: Replicating the Peersman (2005) Results (Journal of Applied Econometrics 2017) in ReplicationWiki ()

Working papers

  1. Joshua C C Chan & Angelia L Grant, 2017. "Measuring the output gap using stochastic model specification search," CAMA Working Papers 2017-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Alberto Montagnoli & Konstantinos Mouratidis & Kemar Whyte, 2018. "Assessing the Cyclical Behaviour of Bank Capital Buyers in a Finance-Augmented Macro-Economy," Working Papers 2018003, The University of Sheffield, Department of Economics.
    2. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    3. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.

  2. Joshua C.C. Chan & Angelia L. Grant, 2016. "Reconciling output gaps: unobserved components model and Hodrick-Prescott filter," CAMA Working Papers 2016-44, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    2. Luis Eduardo Castillo & David Florián Hoyle, 2019. "Measuring the output gap, potential output growth and natural interest rate from a semi-structural dynamic model for Peru," Working Papers 159, Peruvian Economic Association.
    3. Kristian Jönsson, 2018. "Extending the state-space representation of the judgement-augmented Hodrick-Prescott filter," Economics Bulletin, AccessEcon, vol. 38(1), pages 623-628.
    4. Lang, Jan Hannes & Welz, Peter, 2018. "Semi-structural credit gap estimation," Working Paper Series 2194, European Central Bank.
    5. Canova, Fabio & Ferroni, Filippo, 2020. "A hitchhiker guide to empirical macro models," CEPR Discussion Papers 15446, C.E.P.R. Discussion Papers.
    6. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.
    7. Gabor Katay & Lisa Kerdelhué & Matthieu Lequien, 2020. "Semi-Structural VAR and Unobserved Components Models to Estimate Finance-Neutral Output Gap," Working Papers 2020-11, Joint Research Centre, European Commission (Ispra site).
    8. Daniel Buncic, 2020. "Econometric issues with Laubach and Williams' estimates of the natural rate of interest," Papers 2002.11583, arXiv.org, revised Aug 2020.
    9. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    10. Nicolo Maffei-Faccioli, 2020. "Identifying the Sources of the Slowdown in Growth: Demand vs. Supply," 2020 Papers pma2978, Job Market Papers.
    11. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    12. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  3. Joshua C.C. Chan & Angelia L. Grant, 2015. "A Bayesian model comparison for trend-cycle decompositions of output," CAMA Working Papers 2015-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Güneş Kamber & James Morley & Benjamin Wong, 2017. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," Reserve Bank of New Zealand Discussion Paper Series DP2017/01, Reserve Bank of New Zealand.
    2. Jaeho Kim & Sora Chon, 2020. "Why are Bayesian trend-cycle decompositions of US real GDP so different?," Empirical Economics, Springer, vol. 58(3), pages 1339-1354, March.
    3. Bofinger, Peter & Feld, Lars P. & Schmidt, Christoph M. & Schnabel, Isabel & Wieland, Volker, 2018. "Vor wichtigen wirtschaftspolitischen Weichenstellungen. Jahresgutachten 2018/19," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201819, November.
    4. David Kohns & Arnab Bhattacharjee, 2020. "Developments on the Bayesian Structural Time Series Model: Trending Growth," Papers 2011.00938, arXiv.org.
    5. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    6. Weber, Enzo & Gehrke, Britta, 2018. "Identifying Asymmetric Effects of Labor Market Reforms," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181513, Verein für Socialpolitik / German Economic Association.
    7. Marko Melolinna & Máté Tóth, 2019. "Output gaps, inflation and financial cycles in the UK," Empirical Economics, Springer, vol. 56(3), pages 1039-1070, March.
    8. Canova, Fabio, 2020. "FAQ: How do I extract the output gap?," Working Paper Series 386, Sveriges Riksbank (Central Bank of Sweden).
    9. Melolinna, Marko & Tóth, Máté, 2019. "Trend and cycle shocks in Bayesian unobserved components models for UK productivity," Bank of England working papers 826, Bank of England.
    10. Eo, Yunjong & Morley, James, 2017. "Why has the US economy stagnated since the Great Recession?," Working Papers 2017-14, University of Sydney, School of Economics, revised Jun 2019.
    11. Weiske, Sebastian, 2018. "Indicator-based estimates of the output gap in the euro area," Working Papers 12/2018, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    12. Weiske, Sebastian, 2019. "Indicator-based estimates of the output gap in the euro area," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203604, Verein für Socialpolitik / German Economic Association.
    13. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    14. Hang Pham, 2020. "Estimating the Output Gap for Emerging Countries: Evidence from Five Southeast Asia Countries," International Journal of Applied Economics, Finance and Accounting, Online Academic Press, vol. 7(2), pages 61-73.
    15. Ms. Elena Loukoianova & Mr. Harald Finger & Siddharth Kothari & Mr. Geoffrey J Bannister, 2020. "Addressing the Pandemic's Medium-Term Fallout in Australia and New Zealand," IMF Working Papers 2020/272, International Monetary Fund.

  4. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    2. Jamie L. Cross & Chenghan Hou & Bao H. Nguyen, 2018. "On the China factor in international oil markets: A regime switching approach," Working Papers No 11/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2019. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," EMF Research Papers 20, Economic Modelling and Forecasting Group.
    4. Gholamreza Hajargasht & D.S. Prasada Rao, 2019. "Multilateral Index Number Systems for International Price Comparisons: Properties, Existence and Uniqueness," CEPA Working Papers Series WP032019, School of Economics, University of Queensland, Australia.
    5. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    6. Joshua C C Chan & Eric Eisenstat & Gary Koop, 2014. "Large Bayesian VARMAs," Working Papers 1409, University of Strathclyde Business School, Department of Economics.
    7. Pacifico, Antonio, 2020. "Structural Panel Bayesian VAR with Multivariate Time-varying Volatility to jointly deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," MPRA Paper 104292, University Library of Munich, Germany.
    8. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    10. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, Open Access Journal, vol. 9(2), pages 1-35, May.
    11. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Jamie L. Cross & Aubrey Poon, 2020. "On the contribution of international shocks in Australian business cycle fluctuations," Empirical Economics, Springer, vol. 59(6), pages 2613-2637, December.
    13. Benjamin K. Johannsen & Elmar Mertens, 2016. "A Time Series Model of Interest Rates With the Effective Lower Bound," Finance and Economics Discussion Series 2016-033, Board of Governors of the Federal Reserve System (U.S.).
    14. Aubrey Poon, 2018. "Assessing the Synchronicity and Nature of Australian State Business Cycles," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 372-390, December.

  5. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Dunne, Peter G., 2018. "Positive Liquidity Spillovers from Sovereign Bond-Backed Securities," Research Technical Papers 5/RT/18, Central Bank of Ireland.
    3. Brix, Anne Floor & Lunde, Asger & Wei, Wei, 2018. "A generalized Schwartz model for energy spot prices — Estimation using a particle MCMC method," Energy Economics, Elsevier, vol. 72(C), pages 560-582.
    4. Beyer, Robert & Milivojevic, Lazar, 2021. "Dynamics and synchronization of global equilibrium interest rates," IMFS Working Paper Series 146, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    5. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    6. Afees A. Salisu & Idris Adediran, 2018. "Testing for time-varying stochastic volatility in Bitcoin returns," Working Papers 060, Centre for Econometric and Allied Research, University of Ibadan.
    7. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    8. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Businesss School.
    9. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    10. David Cronin & Peter Dunne, 2019. "Have Sovereign Bond Market Relationships Changed in the Euro Area? Evidence from Italy," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 54(4), pages 250-258, July.
    11. Schmidt, Torsten, 2018. "Inflation Expectation Uncertainty, Inflation and the Outputgap," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181575, Verein für Socialpolitik / German Economic Association.
    12. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    13. Będowska-Sójka, Barbara & Kliber, Agata, 2021. "Is there one safe-haven for various turbulences? The evidence from gold, Bitcoin and Ether," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    14. De Sola Perea, Maite & Dunne, Peter G. & Puhl, Martin & Reininger, Thomas, 2018. "Sovereign Bond-Backed Securities: A VAR-for-VaR and Marginal Expected Shortfall Assessment," Research Technical Papers 3/RT/18, Central Bank of Ireland.
    15. Nima Nonejad, 2021. "Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 769-791, August.
    16. William Barnett & Fredj Jawadi & Zied Ftiti, 2020. "Causal Relationships Between Inflation and Inflation Uncertainty," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202010, University of Kansas, Department of Economics, revised Jul 2020.
    17. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    18. Afees A. Salisu & Ahamuefula E. Ogbonna & Tirimisiyu F. Oloko & Idris A. Adediran, 2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic," Sustainability, MDPI, Open Access Journal, vol. 13(6), pages 1-18, March.
    19. Paul Bui Quang & Tony Klein & Nam H. Nguyen & Thomas Walther, 2018. "Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(2), pages 1-20, April.
    20. Kshatriya, Saranya & Prasanna, Krishna, 2021. "Jump Interdependencies: Stochastic linkages among international stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    21. Yu-Ling Hsiao, Cody & Ai, Dan & Wei, Xinyang & Sheng, Ni, 2021. "The contagious effect of China’s energy policy on stock markets: The case of the solar photovoltaic industry," Renewable Energy, Elsevier, vol. 164(C), pages 74-86.
    22. Shang, Yuhuang & Zheng, Tingguo, 2021. "Mixed-frequency SV model for stock volatility and macroeconomics," Economic Modelling, Elsevier, vol. 95(C), pages 462-472.
    23. Sabet, Amir H. & Heaney, Richard, 2016. "An event study analysis of oil and gas firm acreage and reserve acquisitions," Energy Economics, Elsevier, vol. 57(C), pages 215-227.
    24. Jiang, Yong & Zhou, Zhongbao & Liu, Qing & Lin, Ling & Xiao, Helu, 2020. "How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks," Energy Economics, Elsevier, vol. 87(C).
    25. Martin Iseringhausen, 2018. "The Time-Varying Asymmetry Of Exchange Rate Returns: A Stochastic Volatility – Stochastic Skewness Model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 18/944, Ghent University, Faculty of Economics and Business Administration.
    26. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, Open Access Journal, vol. 11(10), pages 1-13, October.
    27. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    28. Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
    29. Kubinschi Matei & Barnea Dinu & Zlatcu Iuliana, 2019. "Estimating fuel price volatility and spillover effects across different European countries," Management & Marketing, Sciendo, vol. 14(4), pages 419-430, December.
    30. Cronin, David & Dunne, Peter & McQuinn, Kieran, 2019. "Have Irish sovereign bonds decoupled from the euro area periphery, and why?," Papers WP625, Economic and Social Research Institute (ESRI).
    31. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    32. Muhammad Omer, 2018. "Estimating Elasticity of Transport Fuel Demand in Pakistan," Working Papers id:12811, eSocialSciences.
    33. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    34. Yong Jiang & Chao-Qun Ma & Xiao-Guang Yang & Yi-Shuai Ren, 2018. "Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-17, December.
    35. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    36. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
    37. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
    38. Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
    39. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
    40. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.
    41. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    42. Jiang, Yong & Ren, Yi-Shuai & Ma, Chao-Qun & Liu, Jiang-Long & Sharp, Basil, 2020. "Does the price of strategic commodities respond to U.S. partisan conflict?," Resources Policy, Elsevier, vol. 66(C).
    43. Fuest, Angela & Schmidt, Torsten, 2020. "Inflation expectation uncertainty in a New Keynesian framework," Ruhr Economic Papers 867, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    44. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    45. Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    46. Saranya, K. & Prasanna, P. Krishna, 2018. "Estimating stochastic volatility with jumps and asymmetry in Asian markets," Finance Research Letters, Elsevier, vol. 25(C), pages 145-153.
    47. Ahsan, Md. Nazmul & Dufour, Jean-Marie, 2021. "Simple estimators and inference for higher-order stochastic volatility models," Journal of Econometrics, Elsevier, vol. 224(1), pages 181-197.
    48. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    49. McCausland, William & Miller, Shirley & Pelletier, Denis, 2021. "Multivariate stochastic volatility using the HESSIAN method," Econometrics and Statistics, Elsevier, vol. 17(C), pages 76-94.
    50. Jinzhi Li, 2021. "Bayesian estimation of the stochastic volatility model with double exponential jumps," Review of Derivatives Research, Springer, vol. 24(2), pages 157-172, July.
    51. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    52. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).
    53. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.
    54. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
    55. Si, Deng-Kui & Zhao, Bing & Li, Xiao-Lin & Ding, Hui, 2021. "Policy uncertainty and sectoral stock market volatility in China," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 557-573.

  6. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Tao Zeng & Yong Li & Jun Yu, 2014. "Deviance Information Criterion for Comparing VAR Models," Advances in Econometrics, in: Yoosoon Chang & Thomas B. Fomby & Joon Y. Park (ed.), Essays in Honor of Peter C. B. Phillips, volume 33, pages 615-637, Emerald Publishing Ltd.
    2. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    3. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    5. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    7. Aknouche, Abdelhakim, 2013. "Periodic autoregressive stochastic volatility," MPRA Paper 69571, University Library of Munich, Germany, revised 2015.
    8. Joshua C.C. Chan & Angelia L. Grant, 2015. "Modeling energy price dynamics: GARCH versus stochastic volatility," CAMA Working Papers 2015-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.

  7. Joshua C.C. Chan & Angelia L. Grant, 2014. "Fast Computation of the Deviance Information Criterion for Latent Variable Models," CAMA Working Papers 2014-09, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    2. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    4. Adam, Marc C. & Jansson, Walter, 2019. "Credit constraints and the propagation of the Great Depression in Germany," Discussion Papers 2019/12, Free University Berlin, School of Business & Economics.
    5. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    6. Joshua C C Chan & Eric Eisenstat & Gary Koop, 2014. "Large Bayesian VARMAs," Working Papers 1409, University of Strathclyde Business School, Department of Economics.
    7. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    8. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    10. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    11. Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    13. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    14. Nima Nonejad, 2019. "Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 246-276, May.
    15. Melolinna, Marko & Tóth, Máté, 2019. "Trend and cycle shocks in Bayesian unobserved components models for UK productivity," Bank of England working papers 826, Bank of England.
    16. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(4), pages 772-802.
    17. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    18. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.
    19. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    20. Joshua C.C. Chan & Eric Eisenstat, 2018. "Comparing hybrid time-varying parameter VARs," CAMA Working Papers 2018-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

Articles

  1. Grant, Angelia L., 2018. "The Great Recession and Okun's law," Economic Modelling, Elsevier, vol. 69(C), pages 291-300.

    Cited by:

    1. Goto, Eiji & Bürgi, Constantin, 2021. "Sectoral Okun's law and cross-country cyclical differences," Economic Modelling, Elsevier, vol. 94(C), pages 91-103.
    2. Kambale Kavese & Andrew Phiri, 2018. "A provincial perspective of nonlinear Okun's law for emerging markets: The case of South Africa," Working Papers 1819, Department of Economics, Nelson Mandela University.
    3. Porras-Arena, M. Sylvina & Martín-Román, Ángel L., 2019. "Self-employment and the Okun's law," Economic Modelling, Elsevier, vol. 77(C), pages 253-265.
    4. Lutho Mbekeni & Andrew Phiri, 2019. "South African unemployment in the post-financial crisis era: What are the determinants?," Working Papers 1903, Department of Economics, Nelson Mandela University, revised May 2019.

  2. Angelia L. Grant, 2017. "The Early Millennium Slowdown: Replicating the Peersman (2005) Results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 224-232, January.

    Cited by:

    1. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.

  3. Grant, Angelia L. & Chan, Joshua C.C., 2017. "Reconciling output gaps: Unobserved components model and Hodrick–Prescott filter," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 114-121.
    See citations under working paper version above.
  4. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    See citations under working paper version above.
  5. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    See citations under working paper version above.
  6. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Modeling energy price dynamics: GARCH versus stochastic volatility," Energy Economics, Elsevier, vol. 54(C), pages 182-189.
    See citations under working paper version above.
  7. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(4), pages 772-802.

    Cited by:

    1. Nima Nonejad, 2020. "Reproducing the results in “Does the time-consistency problem explain the behavior of inflation in the United States?” using the Metropolis–Hastings algorithm," Empirical Economics, Springer, vol. 59(5), pages 2559-2571, November.
    2. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Businesss School.
    4. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.
    5. George P. Papaioannou & Christos Dikaiakos & Akylas C. Stratigakos & Panos C. Papageorgiou & Konstantinos F. Krommydas, 2019. "Testing the Efficiency of Electricity Markets Using a New Composite Measure Based on Nonlinear TS Tools," Energies, MDPI, Open Access Journal, vol. 12(4), pages 1-30, February.
    6. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    7. Dominik Bertsche & Robin Braun, 2018. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Working Paper Series of the Department of Economics, University of Konstanz 2018-03, Department of Economics, University of Konstanz.
    8. Aknouche Abdelhakim & Demmouche Nacer & Dimitrakopoulos Stefanos & Touche Nassim, 2020. "Bayesian analysis of periodic asymmetric power GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-24, September.
    9. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    10. Zhongxian Men & Adam W. Kolkiewicz & Tony S. Wirjanto, 2019. "Threshold Stochastic Conditional Duration Model for Financial Transaction Data," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-21, May.
    11. Florian Huber & Michael Pfarrhofer, 2020. "Dynamic shrinkage in time-varying parameter stochastic volatility in mean models," Papers 2005.06851, arXiv.org.
    12. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    13. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Yoshihiro Ohtsuka, 2018. "Large Shocks and the Business Cycle: The Effect of Outlier Adjustments," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 143-178, April.
    15. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2018. "Does time-variation matter in the stochastic volatility components for G7 stock returns," Working Papers 062, Centre for Econometric and Allied Research, University of Ibadan.
    16. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    17. Daniel J. Lewis, 2019. "Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks and Their Macroeconomic Effects," Staff Reports 891, Federal Reserve Bank of New York.
    18. Chon, Sora & Kim, Jaeho, 2021. "Does the Financial Leverage Effect Depend on Volatility Regimes?," Finance Research Letters, Elsevier, vol. 39(C).
    19. Martin Iseringhausen, 2018. "The Time-Varying Asymmetry Of Exchange Rate Returns: A Stochastic Volatility – Stochastic Skewness Model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 18/944, Ghent University, Faculty of Economics and Business Administration.
    20. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    21. Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021. "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, vol. 93(C).
    22. Wilson Ye Chen & Richard H. Gerlach, 2017. "Semiparametric GARCH via Bayesian model averaging," Papers 1708.07587, arXiv.org.
    23. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    24. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.
    25. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    26. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    27. Daniel J. Lewis, 2018. "Identifying shocks via time-varying volatility," Staff Reports 871, Federal Reserve Bank of New York.
    28. Fedele Greco & Carlo Trivisano, 2018. "Comments on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 549-553, September.
    29. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).

  8. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ORE: Operations Research (5) 2014-02-15 2014-07-21 2015-03-22 2015-06-13 2017-01-22. Author is listed
  2. NEP-ECM: Econometrics (4) 2014-02-15 2014-07-21 2015-03-22 2017-01-22. Author is listed
  3. NEP-ETS: Econometric Time Series (3) 2015-06-13 2015-08-25 2016-07-30. Author is listed
  4. NEP-MAC: Macroeconomics (3) 2015-08-25 2016-07-30 2017-01-22. Author is listed
  5. NEP-ENE: Energy Economics (1) 2015-06-13
  6. NEP-FDG: Financial Development & Growth (1) 2015-08-25
  7. NEP-SOG: Sociology of Economics (1) 2014-02-15

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