IDEAS home Printed from https://ideas.repec.org/r/cam/camdae/0648.html
   My bibliography  Save this item

Testing Dependence Among Serially Correlated Multi-category Variables

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ben Jacobsen & Ben R. Marshall & Nuttawat Visaltanachoti, 2019. "Stock Market Predictability and Industrial Metal Returns," Management Science, INFORMS, vol. 65(7), pages 3026-3042, July.
  2. Pönkä, Harri, 2016. "Real oil prices and the international sign predictability of stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 79-87.
  3. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
  4. Christian Seiler, 2012. "The Data Sets of the LMU-ifo Economics & Business Data Center – A Guide for Researchers," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 132(4), pages 609-618.
  5. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
  6. Etienne, Xiaoli L., 2015. "Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205124, Agricultural and Applied Economics Association.
  7. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
  8. Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
  9. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 1-46, December.
  10. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
  11. Tsuchiya, Yoichi, 2013. "Do corporate executives have accurate predictions for the economy? A directional analysis," Economic Modelling, Elsevier, vol. 30(C), pages 167-174.
  12. Hashmat Khan & Santosh Upadhayaya, 2020. "Does business confidence matter for investment?," Empirical Economics, Springer, vol. 59(4), pages 1633-1665, October.
  13. Heijdra, Ben J. & Ligthart, Jenny E., 2007. "Fiscal policy, monopolistic competition, and finite lives," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 325-359, January.
  14. Steven Trypsteen, 2014. "The Importance of a Time-Varying Variance and Cross-Country Interactions in Forecast Models," Discussion Papers 2014/15, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
  15. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
  16. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
  17. Jixiang, Zhang & Feng, Ma, 2024. "Video apps user engagement and stock market volatility: Evidence from China," Finance Research Letters, Elsevier, vol. 64(C).
  18. Cem Cakmakli & Richard Paap & Dick J.C. van Dijk, 2011. "Modeling and Estimation of Synchronization in Multistate Markov-Switching Models," Tinbergen Institute Discussion Papers 11-002/4, Tinbergen Institute.
  19. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
  20. Christian Seiler, 2014. "The determinants of unit non-response in the Ifo Business Survey," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 161-177, September.
  21. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2017. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 275-295, March.
  22. Anthony Garratt & Shaun P. Vahey & Yunyi Zhang, 2019. "Real‐time forecast combinations for the oil price," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 456-462, April.
  23. Kitazawa, Yoshitsugu, 2022. "Transformations and moment conditions for dynamic fixed effects logit models," Journal of Econometrics, Elsevier, vol. 229(2), pages 350-362.
  24. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
  25. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2017. "Exponential class of dynamic binary choice panel data models with fixed effects," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 898-927, October.
  26. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
  27. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 869-889, August.
  28. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
  29. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
  30. Kilian, Lutz & Baumeister, Christiane, 2014. "A General Approach to Recovering Market Expectations from Futures Prices With an Application to Crude Oil," CEPR Discussion Papers 10162, C.E.P.R. Discussion Papers.
  31. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
  32. Dawud Ansari & Mariza Montes de Oca Leon & Helen Schlüter, 2021. "What Drives Saudi Airstrikes in Yemen? An Empirical Analysis of the Dynamics of Coalition Airstrikes, Houthi Attacks, and the Oil Market," Discussion Papers of DIW Berlin 1959, DIW Berlin, German Institute for Economic Research.
  33. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.
  34. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
  35. Kajal Lahiri & Liu Yang, 2018. "Confidence Bands for ROC Curves With Serially Dependent Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 115-130, January.
  36. Helmut Elsinger, 2020. "Serial Correlation in Contingency Tables (Helmut Elsinger)," Working Papers 228, Oesterreichische Nationalbank (Austrian Central Bank).
  37. Roberto Duncan & Enrique Martínez García, 2015. "Forecasting local inflation in Open Economies: What Can a NOEM Model Do?," Globalization Institute Working Papers 235, Federal Reserve Bank of Dallas, revised 21 Dec 2022.
  38. Tsuchiya, Yoichi, 2016. "Do production managers predict turning points? A directional analysis," Economic Modelling, Elsevier, vol. 58(C), pages 1-8.
  39. Sebastian Link, 2019. "The Price and Employment Response of Firms to the Introduction of Minimum Wages," CESifo Working Paper Series 7575, CESifo.
  40. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
  41. Tsuchiya, Yoichi, 2016. "Directional analysis of fiscal sustainability: Revisiting Domar's debt sustainability condition," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 189-201.
  42. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & Yelou, Clement, 2018. "Oil Price Forecasts For The Long Term: Expert Outlooks, Models, Or Both?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 581-599, April.
  43. Christiane Baumeister & Lutz Kilian & Xiaoqing Zhou, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," Staff Working Papers 13-25, Bank of Canada.
  44. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
  45. Gu, Wentao & Peng, Yiqing, 2019. "Forecasting the market return direction based on a time-varying probability density model," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
  46. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
  47. Edward S. Knotek & Saeed Zaman, 2017. "Nowcasting U.S. Headline and Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 931-968, August.
  48. Markos Farag, Stephen Snudden, Greg Upton, 2024. "Can Futures Prices Predict the Real Price of Primary Commodities?," LCERPA Working Papers jc0145, Laurier Centre for Economic Research and Policy Analysis, revised 2024.
  49. Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," Papers 2402.04828, arXiv.org, revised Feb 2024.
  50. Morita, Hiroshi & 森田, 裕史, 2019. "Forecasting Public Investment Using Daily Stock Returns," Discussion paper series HIAS-E-88, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
  51. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
  52. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2023. "Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts," Energy Economics, Elsevier, vol. 121(C).
  53. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
  54. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
  55. Liu, Li & Pan, Zhiyuan, 2020. "Forecasting stock market volatility: The role of technical variables," Economic Modelling, Elsevier, vol. 84(C), pages 55-65.
  56. Kevin Lee & Kian Ong & Kalvinder K. Shields, 2020. "Making Fiscal Adjustments Using Event Probability Forecasts in OECD Countries," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 294-313, September.
  57. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
  58. Harchaoui, Tarek M. & Janssen, Robert V., 2018. "How can big data enhance the timeliness of official statistics?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 225-234.
  59. Tsuchiya, Yoichi, 2013. "Are government and IMF forecasts useful? An application of a new market-timing test," Economics Letters, Elsevier, vol. 118(1), pages 118-120.
  60. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
  61. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
  62. Malte Knüppel & Guido Schultefrankenfeld, 2012. "How Informative Are Central Bank Assessments of Macroeconomic Risks?," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 87-139, September.
  63. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
  64. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
  65. Lahiri, Kajal & Yang, Liu, 2016. "Asymptotic variance of Brier (skill) score in the presence of serial correlation," Economics Letters, Elsevier, vol. 141(C), pages 125-129.
  66. Knüppel, Malte & Schultefrankenfeld, Guido, 2008. "How informative are macroeconomic risk forecasts? An examination of the Bank of England's inflation forecasts," Discussion Paper Series 1: Economic Studies 2008,14, Deutsche Bundesbank.
  67. Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
  68. Link Sebastian, 2020. "Harmonization of the ifo Business Survey’s Micro Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(4), pages 543-555, August.
  69. Garratt, Anthony & Lee, Kevin & Shields, Kalvinder, 2016. "Forecasting global recessions in a GVAR model of actual and expected output," International Journal of Forecasting, Elsevier, vol. 32(2), pages 374-390.
  70. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
  71. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
  72. Christiane Baumeister & Lutz Kilian, 2014. "Do oil price increases cause higher food prices? [Biofuels, binding constraints, and agricultural commodity price volatility]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 29(80), pages 691-747.
  73. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
  74. Lei, Xiying & Yang, Yao & Alharthi, Majed & Rasul, Farhat & Faraz Raza, Syed Muhammad, 2022. "Immense reliance on natural resources and environmental challenges in G-20 economies through the lens of COP-26 targets," Resources Policy, Elsevier, vol. 79(C).
  75. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
  76. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
  77. Leiva-Leon, Danilo, 2013. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," MPRA Paper 54452, University Library of Munich, Germany.
  78. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
  79. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
  80. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
  81. Amor Aniss Benmoussa & Reinhard Ellwanger & Stephen Snudden, 2020. "The New Benchmark for Forecasts of the Real Price of Crude Oil," Staff Working Papers 20-39, Bank of Canada.
  82. Shiu-Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 949-967, August.
  83. Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2024. "Forecasting the price of oil: A cautionary note," Journal of Commodity Markets, Elsevier, vol. 33(C).
  84. Harri Pönkä & Markku Stenborg, 2020. "Forecasting the state of the Finnish business cycle," Finnish Economic Papers, Finnish Economic Association, vol. 29(1), pages 81-99, Spring.
  85. Xiaoyi Mu and Haichun Ye, 2015. "Small Trends and Big Cycles in Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
  86. Erum, Naila & Hussain, Shahzad, 2019. "Corruption, natural resources and economic growth: Evidence from OIC countries," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  87. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
  88. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
  89. James Mitchell & Richard J. Smith & Martin R. Weale, 2013. "Efficient Aggregation Of Panel Qualitative Survey Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 580-603, June.
  90. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
  91. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
  92. Cortazar, Gonzalo & Ortega, Hector & Valencia, Consuelo, 2021. "How good are analyst forecasts of oil prices?," Energy Economics, Elsevier, vol. 102(C).
  93. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
  94. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
  95. Y. Tsuchiya, 2014. "A directional evaluation of corporate executives' exchange rate forecasts," Applied Economics, Taylor & Francis Journals, vol. 46(1), pages 95-101, January.
  96. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  97. Chrystalleni Aristidou & Kevin Lee & Kalvinder Shields, 2015. "Real-Time Data should be used in Forecasting Output Growth and Recessionary Events in the US," Discussion Papers 2015/13, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
  98. Pönkä, Harri & Zheng, Yi, 2019. "The role of oil prices on the Russian business cycle," Research in International Business and Finance, Elsevier, vol. 50(C), pages 70-78.
  99. Wang, Jiqian & Ma, Feng & Wang, Tianyang & Wu, Lan, 2023. "International stock volatility predictability: New evidence from uncertainties," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
  100. Anthony Garratt & Kevin Lee & Kalvinder Shields, 2014. "Forecasting Global Recessions in a GVAR Model of Actual and Expected Output in the G7," Discussion Papers 2014/06, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
  101. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
  102. Tsuchiya, Yoichi, 2014. "Purchasing and supply managers provide early clues on the direction of the US economy: An application of a new market-timing test," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 599-618.
  103. Li, Dakai, 2024. "Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 115-122.
  104. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
  105. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
  106. Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen, 2023. "Jump forecasting in foreign exchange markets: A high‐frequency analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 578-624, April.
  107. Kimberly Berg & Pierre Guérin & Yuko Imura, 2016. "Predictive Ability of Commodity Prices for the Canadian Dollar," Staff Analytical Notes 16-2, Bank of Canada.
  108. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
  109. IIZUKA Nobuo, 2013. "Predicting Business Cycle Phases by Professional Forecasters- Are They Useful ?," ESRI Discussion paper series 305, Economic and Social Research Institute (ESRI).
  110. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
  111. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
  112. Y. Tsuchiya, 2014. "Are consumer sentiments useful in Japan? An application of a new market-timing test," Applied Economics Letters, Taylor & Francis Journals, vol. 21(5), pages 356-359, March.
  113. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
  114. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
  115. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
  116. Sebastian Link, 2018. "Harmonization and Interpretation of the ifo Business Survey's Micro Data," CESifo Working Paper Series 7427, CESifo.
  117. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2022. "Geopolitical risk trends and crude oil price predictability," Energy, Elsevier, vol. 258(C).
  118. Lei, Xiying & Alharthi, Majed & Ahmad, Ishtiaq & Aziz, Babar & Abdin, Zain ul, 2022. "Importance of international relations for the promotion of renewable energy, preservation of natural resources and environment: Empirics from SEA nations," Renewable Energy, Elsevier, vol. 196(C), pages 1250-1257.
  119. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
  120. Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
  121. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
  122. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2024. "Machine-learning stock market volatility: Predictability, drivers, and economic value," International Review of Financial Analysis, Elsevier, vol. 94(C).
  123. Panagiotis Delis & Stavros Degiannakis & George Filis, 2022. "What matters when developing oil price volatility forecasting frameworks?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 361-382, March.
  124. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
  125. Yoshitsugu Kitazawa, 2013. "Exploration of dynamic fixed effects logit models from a traditional angle," Discussion Papers 60, Kyushu Sangyo University, Faculty of Economics.
  126. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
  127. Francisco Javier Eransus & Alfonso Novales Cinca, 2014. "Parameter Estimation Error in Tests of Predictive Performance under Discrete Loss Functions," Documentos de Trabajo del ICAE 2014-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  128. Snudden, Stephen, 2018. "Targeted growth rates for long-horizon crude oil price forecasts," International Journal of Forecasting, Elsevier, vol. 34(1), pages 1-16.
  129. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
  130. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
  131. Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019. "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, vol. 81(C), pages 639-649.
  132. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
  133. Amor Aniss Benmoussa, Reinhard Ellwanger, Stephen Snudden, 2023. "Carpe Diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?," LCERPA Working Papers bm0141, Laurier Centre for Economic Research and Policy Analysis.
  134. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
  135. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2022. "Asymmetry and Interdependence when Evaluating U.S. Energy Information Agency Forecasts," National Institute of Economic and Social Research (NIESR) Discussion Papers 541, National Institute of Economic and Social Research.
  136. Zhang, Lili & Zhong, Juandan, 2024. "Transportation sector and Chinese stock volatility forecasting: Evidence from freight and passenger traffic," Finance Research Letters, Elsevier, vol. 60(C).
  137. Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
  138. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
  139. Li Liu & Yudong Wang, 2021. "Forecasting aggregate market volatility: The role of good and bad uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 40-61, January.
  140. G. Kontogeorgos & K. Lambrias, 2022. "Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 213-229, March.
  141. Yaojie Zhang & Mengxi He & Yuqi Zhao & Xianfeng Hao, 2023. "Predicting stock realized variance based on an asymmetric robust regression approach," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1022-1047, October.
  142. Young Bin Ahn & Yoichi Tsuchiya, 2016. "Directional analysis of consumers’ forecasts of inflation in a small open economy: evidence from South Korea," Applied Economics, Taylor & Francis Journals, vol. 48(10), pages 854-864, February.
  143. Yuqing Feng & Yaojie Zhang & Yudong Wang, 2024. "Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 567-582, April.
  144. Constantin Bürgi & Dorine Boumans, 2020. "Categorical Forecasts and Non-Categorical Loss Functions," CESifo Working Paper Series 8266, CESifo.
  145. Knüppel, Malte & Schultefrankenfeld, Guido, 2011. "Evaluating macroeconomic risk forecasts," Discussion Paper Series 1: Economic Studies 2011,14, Deutsche Bundesbank.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.