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J. Isaac Miller

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.

Working papers

  1. William A. Brock & J. Isaac Miller, 2023. "Polar Amplification in a Moist Energy Balance Model: A Structural Econometric Approach to Estimation and Testing," Working Papers 2304, Department of Economics, University of Missouri.

    Cited by:

    1. Francis X. Diebold & Glenn D. Rudebusch & Maximilian Goebel & Philippe Goulet Coulombe & Boyuan Zhang, 2022. "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," Papers 2203.04040, arXiv.org, revised May 2023.

  2. J. Isaac Miller & Kyungsik Nam, 2019. "Dating Hiatuses: A Statistical Model of the Recent Slowdown in Global Warming – and the Next One," Working Papers 1903, Department of Economics, University of Missouri.

    Cited by:

    1. Kyungsik Nam, 2021. "Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing," Econometrics, MDPI, vol. 9(1), pages 1-25, February.

  3. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2019. "Forecasting Regional Long-Run Energy Demand: A Functional Coefficient Panel Approach," Working Papers 1915, Department of Economics, University of Missouri.

    Cited by:

    1. Liddle, Brantley, 2023. "Is timing everything? Assessing the evidence on whether energy/electricity demand elasticities are time-varying," Energy Economics, Elsevier, vol. 124(C).
    2. Grzegorz Ślusarz & Dariusz Twaróg & Barbara Gołębiewska & Marek Cierpiał-Wolan & Jarosław Gołębiewski & Philipp Plutecki, 2023. "The Role of Biogas Potential in Building the Energy Independence of the Three Seas Initiative Countries," Energies, MDPI, vol. 16(3), pages 1-23, January.
    3. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    4. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," CAMA Working Papers 2024-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Brantley Liddle, 2022. "What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence," Energies, MDPI, vol. 15(10), pages 1-12, May.
    6. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    7. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    8. Wang, You & Gong, Xu, 2022. "Analyzing the difference evolution of provincial energy consumption in China using the functional data analysis method," Energy Economics, Elsevier, vol. 105(C).
    9. Zhao, Jing & Miller, J. Isaac & Binfield, Julian & Thompson, Wyatt, 2022. "Modeling and Forecasting Agricultural Commodity Support in the Developing Countries," Commissioned Papers 321785, International Agricultural Trade Research Consortium.

  4. J. Isaac Miller, 2018. "Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data," Working Papers 1809, Department of Economics, University of Missouri.

    Cited by:

    1. Burak Alparslan Eroğlu & J. Isaac Miller & Taner Yiğit, 2022. "Time-varying cointegration and the Kalman filter," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 1-21, January.

  5. J. Isaac Miller, 2017. "Local Climate Sensitivity: A Statistical Approach for a Spatially Heterogeneous Planet," Working Papers 1702, Department of Economics, University of Missouri.

    Cited by:

    1. Kyungsik Nam, 2021. "Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing," Econometrics, MDPI, vol. 9(1), pages 1-25, February.

  6. Yoosoon Chang & Robert K. Kaufmann & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2016. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Working Papers 1622, Department of Economics, University of Missouri, revised 17 Sep 2018.

    Cited by:

    1. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
    2. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    3. Chen, Liang & Dolado, Juan José & Ramos Ramirez, Andrey David & Gonzalo, Jesús, 2023. "Heterogeneous Predictive Association of CO2 with Global Warming," UC3M Working papers. Economics 36451, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Hee Soo (test record) Kim & Christian Matthes & Toan Phan, 2011. "Extreme Weather and the Macroeconomy," Working Paper 21-14, Federal Reserve Bank of Richmond.
    5. Li Chen & Jiti Gao & Farshid Vahid, 2019. "Global Temperatures and Greenhouse Gases: A Common Features Approach," Monash Econometrics and Business Statistics Working Papers 23/19, Monash University, Department of Econometrics and Business Statistics.
    6. Morana, Claudio & Sbrana, Giacomo, 2019. "Climate change implications for the catastrophe bonds market: An empirical analysis," Economic Modelling, Elsevier, vol. 81(C), pages 274-294.
    7. Marc Gronwald, 2023. "Explosive Temperatures," CESifo Working Paper Series 10680, CESifo.
    8. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series," Boston University - Department of Economics - Working Papers Series WP2020-009, Boston University - Department of Economics.
    9. Kyungsik Nam, 2021. "Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing," Econometrics, MDPI, vol. 9(1), pages 1-25, February.
    10. Yoonseok Lee & Donggyu Sul, 2023. "Depth-weighted Forecast Combination: Application to COVID-19 Cases," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 235-260, Emerald Group Publishing Limited.
    11. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," CAMA Working Papers 2024-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. In Choi, 2023. "Does climate change affect economic data?," Empirical Economics, Springer, vol. 64(6), pages 2939-2956, June.
    13. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    14. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Manveer Kaur Mangat & Erhard Reschenhofer, 2020. "Frequency-Domain Evidence for Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-15, July.
    16. Pretis, Felix, 2021. "Exogeneity in climate econometrics," Energy Economics, Elsevier, vol. 96(C).

  7. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2015. "A New Approach to Modeling the Effects of Temperature Fluctuations on Monthly Electricity Demand," Working Papers 1512, Department of Economics, University of Missouri.

    Cited by:

    1. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    2. Xu, Chang & Katchova, Ani, 2018. "Predicting Soybean Yield with NDVI using a Flexible Fourier Transform Model," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266693, Southern Agricultural Economics Association.
    3. Tian, Chuyin & Huang, Guohe & Piwowar, Joseph M. & Yeh, Shin-Cheng & Lu, Chen & Duan, Ruixin & Ren, Jiayan, 2022. "Stochastic RCM-driven cooling and heating energy demand analysis for residential building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    4. Hocheol Jeon, 2019. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data," Energies, MDPI, vol. 12(23), pages 1-15, November.
    5. Harish, Santosh & Singh, Nishmeet & Tongia, Rahul, 2020. "Impact of temperature on electricity demand: Evidence from Delhi and Indian states," Energy Policy, Elsevier, vol. 140(C).
    6. Alimohammadisagvand, Behrang & Jokisalo, Juha & Sirén, Kai, 2018. "Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building," Applied Energy, Elsevier, vol. 209(C), pages 167-179.
    7. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    8. Gautam, Tej K. & Paudel, Krishna P., 2018. "Estimating sectoral demands for electricity using the pooled mean group method," Applied Energy, Elsevier, vol. 231(C), pages 54-67.
    9. Manner, Hans & Alavi Fard, Farzad & Pourkhanali, Armin & Tafakori, Laleh, 2019. "Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae," Energy Economics, Elsevier, vol. 78(C), pages 143-164.
    10. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
    11. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego F., 2018. "Effect of stopping hydroelectric power generation on the dynamics of electricity prices: An event study approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 456-467.
    12. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    13. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    14. Stéphane AURAY & Vincent CAPONI, 2020. "A Vector Autoregressive Model of Forecast Electricity Consumption in France," Working Papers 2020-06, Center for Research in Economics and Statistics.
    15. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.

  8. Yoosoon Chang & Robert K. Kaufmann & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2015. "Time Series Analysis of Global Temperature Distributions: Identifying and Estimating Persistent Features in Temperature Anomalies," Working Papers 1513, Department of Economics, University of Missouri, revised 25 Jul 2016.

    Cited by:

    1. J. Isaac Miller, 2017. "Local Climate Sensitivity: A Statistical Approach for a Spatially Heterogeneous Planet," Working Papers 1702, Department of Economics, University of Missouri.
    2. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2017. "Trends in distributional characteristics : Existence of global warming," UC3M Working papers. Economics 24121, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Claudio Morana & Giacomo Sbrana, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," Working Papers 2017.09, Fondazione Eni Enrico Mattei.
    4. Claudio Morana & Giacomo Sbrana, 2018. "Some financial implications of global warming: An empirical assessment," Working Paper series 18-09, Rimini Centre for Economic Analysis.

  9. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.

    Cited by:

    1. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    2. Yun Liu & Yeonwoo Rho, 2018. "On the Choice of Instruments in Mixed Frequency Specification Tests," Papers 1809.05503, arXiv.org.

  10. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.

    Cited by:

    1. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Jeyhun Mammadov, 2020. "Gasoline Demand Elasticities at the Backdrop of Lower Oil Prices: Fuel-Subsidizing Country Case," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    3. Asuamah Yeboah, Samuel, 2018. "Do government activities determine electricity consumption in Ghana? An empirical investigation," MPRA Paper 89408, University Library of Munich, Germany.
    4. Hocheol Jeon, 2019. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data," Energies, MDPI, vol. 12(23), pages 1-15, November.
    5. Julián Pérez-García & Julián Moral-Carcedo, 2017. "Why Electricity Demand Is Highly Income-Elastic in Spain: A Cross-Country Comparison Based on an Index-Decomposition Analysis," Energies, MDPI, vol. 10(3), pages 1-20, March.
    6. Yoosoon Chang & Yongok Choi & Chang Sik Kim & Joon Y. Park & J. Isaac Miller, 2013. "Disentangling Temporal Patterns in Elasticities: A Functional Coefficient Panel Analysis of Electricity Demand," Working Papers 1320, Department of Economics, University of Missouri.
    7. Hyo-Jin Kim & Gyeong-Sam Kim & Seung-Hoon Yoo, 2019. "Demand Function for Industrial Electricity: Evidence from South Korean Manufacturing Sector," Sustainability, MDPI, vol. 11(18), pages 1-11, September.
    8. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    9. Kyungsik Nam, 2021. "Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing," Econometrics, MDPI, vol. 9(1), pages 1-25, February.
    10. Daniel de Abreu Pereira Uhr & Júlia Gallego Ziero Uhr, André Luis Squarize Chagas, 2017. "Estimation of price and income elasticities for the Brazilian household electricity demand," Working Papers, Department of Economics 2017_12, University of São Paulo (FEA-USP).
    11. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
    12. Sharimakin, Akinsehinwa, 2021. "Modelling asymmetric price responses of industrial energy demand with a dynamic hierarchical model," Energy Economics, Elsevier, vol. 98(C).
    13. Kyungsik Nam & Sungro Lee & Hocheol Jeon, 2020. "Nonlinearity between CO 2 Emission and Economic Development: Evidence from a Functional Coefficient Panel Approach," Sustainability, MDPI, vol. 12(24), pages 1-10, December.
    14. Pereira Uhr, Daniel de Abreu & Squarize Chagas, André Luis & Ziero Uhr, Júlia Gallego, 2019. "Estimation of elasticities for electricity demand in Brazilian households and policy implications," Energy Policy, Elsevier, vol. 129(C), pages 69-79.
    15. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    16. Yasunobu Wakashiro, 2019. "Estimating price elasticity of demand for electricity: the case of Japanese manufacturing industry," International Journal of Economic Policy Studies, Springer, vol. 13(1), pages 173-191, January.
    17. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
    18. Fukushige, Mototsugu & Yamawaki, Hiroshige, 2015. "The relationship between an electricity supply ceiling and economic growth: An application of disequilibrium modeling to Taiwan," Journal of Asian Economics, Elsevier, vol. 36(C), pages 14-23.
    19. Hyo-Jin Kim & Jae-Sung Paek & Seung-Hoon Yoo, 2019. "Price Elasticity of Heat Demand in South Korean Manufacturing Sector: An Empirical Investigation," Sustainability, MDPI, vol. 11(21), pages 1-10, November.
    20. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    21. Ribó-Pérez, David & Van der Weijde, Adriaan H. & Álvarez-Bel, Carlos, 2019. "Effects of self-generation in imperfectly competitive electricity markets: The case of Spain," Energy Policy, Elsevier, vol. 133(C).
    22. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    23. Soon, Byung Min & Thompson, Wyatt, 2017. "Testing for Persistent Japanese Beef Trade Impacts from BSE Using a Time-Varying Armington Model," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259150, Agricultural and Applied Economics Association.
    24. Martin Falk & Xiang Lin, 2018. "Income elasticity of overnight stays over seven decades," Tourism Economics, , vol. 24(8), pages 1015-1028, December.
    25. Agnolucci, Paolo & De Lipsis, Vincenzo & Arvanitopoulos, Theodoros, 2017. "Modelling UK sub-sector industrial energy demand," Energy Economics, Elsevier, vol. 67(C), pages 366-374.
    26. Keita Honjo & Hiroto Shiraki & Shuichi Ashina, 2018. "Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    27. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Carlo A. Bollino & Ceyhun Mahmudlu, 2017. "Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach," Energies, MDPI, vol. 10(11), pages 1-12, November.
    28. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    29. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    30. Daniel Morais de Souza & Rogerio Silva de Mattos & Alexandre Zanini, 2022. "Estimating Elasticities for the Residential Demand of Electricity in Brazil Using Cointegration Models," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 315-324, March.
    31. Ozturk, Ilhan & Arisoy, Ibrahim, 2016. "An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach," Energy Policy, Elsevier, vol. 99(C), pages 174-179.
    32. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Marzio Galeotti, 2018. "Decoupling of C02 Emissions and GDP: A Time-Varying Cointegration Approach," IEFE Working Papers 101, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    33. Nikos Sakkas & Sofia Yfanti & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2021. "Interpretable Forecasting of Energy Demand in the Residential Sector," Energies, MDPI, vol. 14(20), pages 1-17, October.
    34. Hasanov, Fakhri J. & Aliyev, Ruslan & Taskin, Dilvin & Suleymanov, Elchin, 2023. "Oil rents and non-oil economic growth in CIS oil exporters. The role of financial development," Resources Policy, Elsevier, vol. 82(C).
    35. Joo, Young C. & Park, Sung Y., 2017. "Oil prices and stock markets: Does the effect of uncertainty change over time?," Energy Economics, Elsevier, vol. 61(C), pages 42-51.
    36. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    37. Chang Sik Kim & Sunghyun Kim & Yunjong Wang, 2018. "RMB Bloc in East Asia: Too Early to Talk About It?," Asian Economic Papers, MIT Press, vol. 17(3), pages 31-48, Fall.
    38. Hortay, Olivér & Szőke, Tamás, 2019. "Keresleti árrugalmasság becslése a magyar villamosenergia-piacon [Estimating demand-price elasticity on the Hungarian electric energy market]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 788-804.
    39. Wang, Banban & Wei, Jie & Tan, Xiujie & Su, Bin, 2021. "The sectorally heterogeneous and time-varying price elasticities of energy demand in China," Energy Economics, Elsevier, vol. 102(C).

  11. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.

    Cited by:

    1. Doré, Natalia I. & Teixeira, Aurora A.C., 2023. "The role of human capital, structural change, and institutional quality on Brazil's economic growth over the last two hundred years (1822–2019)," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 1-12.
    2. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    3. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    4. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    5. J. Isaac Miller, 2018. "Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data," Working Papers 1809, Department of Economics, University of Missouri.
    6. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    7. Aurora A. C. Teixeira & Ana Sofia Loureiro, 2019. "FDI, income inequality and poverty: a time series analysis of Portugal, 1973–2016," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 18(3), pages 203-249, October.

  12. Yoosoon Chang & Yongok Choi & Chang Sik Kim & Joon Y. Park & J. Isaac Miller, 2013. "Disentangling Temporal Patterns in Elasticities: A Functional Coefficient Panel Analysis of Electricity Demand," Working Papers 1320, Department of Economics, University of Missouri.

    Cited by:

    1. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Jeyhun Mammadov, 2020. "Gasoline Demand Elasticities at the Backdrop of Lower Oil Prices: Fuel-Subsidizing Country Case," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. Liddle, Brantley & Parker, Steven & Hasanov, Fakhri, 2023. "Why has the OECD long-run GDP elasticity of economy-wide electricity demand declined? Because the electrification of energy services has saturated," Energy Economics, Elsevier, vol. 125(C).
    3. Liddle, Brantley, 2023. "Is timing everything? Assessing the evidence on whether energy/electricity demand elasticities are time-varying," Energy Economics, Elsevier, vol. 124(C).
    4. Meangbua, Onicha & Dhakal, Shobhakar & Kuwornu, John K.M., 2019. "Factors influencing energy requirements and CO2 emissions of households in Thailand: A panel data analysis," Energy Policy, Elsevier, vol. 129(C), pages 521-531.
    5. Julián Pérez-García & Julián Moral-Carcedo, 2017. "Why Electricity Demand Is Highly Income-Elastic in Spain: A Cross-Country Comparison Based on an Index-Decomposition Analysis," Energies, MDPI, vol. 10(3), pages 1-20, March.
    6. Gao, Jiti & Peng, Bin & Smyth, Russell, 2021. "On income and price elasticities for energy demand: A panel data study," Energy Economics, Elsevier, vol. 96(C).
    7. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    8. Brantley Liddle, 2022. "What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence," Energies, MDPI, vol. 15(10), pages 1-12, May.
    9. Kyungsik Nam & Sungro Lee & Hocheol Jeon, 2020. "Nonlinearity between CO 2 Emission and Economic Development: Evidence from a Functional Coefficient Panel Approach," Sustainability, MDPI, vol. 12(24), pages 1-10, December.
    10. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    11. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    12. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    13. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    14. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    15. Fakhri J. Hasanov & Lester C. Hunt & Ceyhun I. Mikayilov, 2016. "Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques," Energies, MDPI, vol. 9(12), pages 1-31, December.
    16. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Carlo A. Bollino & Ceyhun Mahmudlu, 2017. "Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach," Energies, MDPI, vol. 10(11), pages 1-12, November.
    17. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    18. Yongok Choi, 2020. "Impact of Longevity Risks on the Korean Government: Proposing a New Mortality Forecasting Model," Korean Economic Review, Korean Economic Association, vol. 36, pages 201-225.
    19. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.
    20. Mohammad Nure Alam, 2021. "Accessing the Effect of Renewables on the Wholesale Power Market," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 341-360.
    21. Jeyhun Mikayilov & Fred Joutz & Fakhri Hasanov, 2019. "Gasoline Demand in Saudi Arabia: Are the Price and Income Elasticities Constant?," Discussion Papers ks--2019-dp81, King Abdullah Petroleum Studies and Research Center.

  13. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.

    Cited by:

    1. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    2. Marçal, Emerson Fernandes & Zimmermann, Beatrice & de Prince, Diogo & Merlin, Giovanni, 2018. "Assessing interdependence among countries' fundamentals and its implications for exchange rate misalignment estimates: An empirical exercise based on GVAR," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 72(4), December.
    3. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    4. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
    5. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    6. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    7. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    8. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    9. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    10. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    11. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    12. J. Isaac Miller, 2018. "Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data," Working Papers 1809, Department of Economics, University of Missouri.
    13. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    14. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    15. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.
    16. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    17. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    18. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-frequency macro-financial spillovers," Working Papers 201704, Geary Institute, University College Dublin.
    19. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.

  14. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.

    Cited by:

    1. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    2. Maolin Cheng & Bin Liu, 2019. "Analysis on the Influence of China’s Energy Consumption on Economic Growth," Sustainability, MDPI, vol. 11(14), pages 1-25, July.
    3. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    4. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    5. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    6. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    7. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    8. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    9. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    10. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.
    11. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    12. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    13. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    14. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    15. Chi-Wei Su & Yuru Song & Hsu-Ling Chang & Weike Zhang & Meng Qin, 2023. "Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?," Sustainability, MDPI, vol. 15(9), pages 1-15, May.

  15. J. Isaac Miller, 2011. "Conditionally Efficient Estimation of Long-run Relationships Using Mixed-frequency Time Series," Working Papers 1103, Department of Economics, University of Missouri, revised 30 May 2012.

    Cited by:

    1. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    2. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    3. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    4. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    5. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    6. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    7. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    8. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    9. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    10. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    11. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.

  16. J. Isaac Miller, 2011. "Cointegrating MiDaS Regressions and a MiDaS Test," Working Papers 1104, Department of Economics, University of Missouri.

    Cited by:

    1. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    2. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).

  17. J. Isaac Miller, 2010. "A Nonlinear IV Likelihood-Based Rank Test for Multivariate Time Series and Long Panels," Working Papers 1001, Department of Economics, University of Missouri.

    Cited by:

    1. Uwe Hassler & Mehdi Hosseinkouchack, 2016. "Panel Cointegration Testing in the Presence of Linear Time Trends," Econometrics, MDPI, vol. 4(4), pages 1-16, November.
    2. Hanck, Christoph & Demetrescu, Matei & Tarcolea, Adina, 2012. "IV-Based Cointegration Testing in Dependent Panels with Time-Varying Variance," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62072, Verein für Socialpolitik / German Economic Association.
    3. Antonia Arsova & Deniz Dilan Karaman Örsal, 2018. "Likelihood-based panel cointegration test in the presence of a linear time trend and cross-sectional dependence," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1033-1050, November.

  18. J. Isaac Miller & Shawn Ni, 2010. "Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy," Working Papers 1012, Department of Economics, University of Missouri.

    Cited by:

    1. Turhan, M. Ibrahim & Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk, 2014. "A view to the long-run dynamic relationship between crude oil and the major asset classes," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 286-299.
    2. Ratti, Ronald A. & Vespignani, Joaquin L., 2013. "Liquidity and crude oil prices: China's influence over 1996–2011," Economic Modelling, Elsevier, vol. 33(C), pages 517-525.
    3. Zhang, Chuanguo & Chen, Xiaoqing, 2014. "The impact of global oil price shocks on China’s bulk commodity markets and fundamental industries," Energy Policy, Elsevier, vol. 66(C), pages 32-41.
    4. {L}ukasz Bielak & Aleksandra Grzesiek & Joanna Janczura & Agnieszka Wy{l}oma'nska, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Papers 2107.07142, arXiv.org.
    5. Claudio Morana, 2012. "The Oil price-Macroeconomy Relationship since the Mid- 1980s: A global perspective," Working Papers 2012.28, Fondazione Eni Enrico Mattei.
    6. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    7. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    8. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    9. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.
    10. Ratti, Ronald A & Vespignani, Joaquin L., 2012. "Crude Oil Prices: China’s Influence Over 1996-2011," Working Papers 15728, University of Tasmania, Tasmanian School of Business and Economics, revised 17 Dec 2012.
    11. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).

  19. J. Isaac Miller, 2008. "Testing the Bounds: Empirical Behavior of Target Zone Fundamentals," Working Papers 0803, Department of Economics, University of Missouri, revised 15 Apr 2009.

    Cited by:

    1. Kevin C. Chua, 2018. "A Model Of Inflation Transmission In An Exchange Rate Target Zone," Bulletin of Economic Research, Wiley Blackwell, vol. 70(3), pages 285-297, July.

  20. J. Isaac Miller & Ronald Ratti, 2008. "Crude Oil and Stock Markets: Stability, Instability, and Bubbles," Working Papers 0810, Department of Economics, University of Missouri, revised 20 Jan 2009.

    Cited by:

    1. Esin Cakan & Rıza Demirer & Rangan Gupta & Hardik A. Marfatia, 2017. "Oil Speculation and Herding Behavior in Emerging Stock Markets," Working Papers 201749, University of Pretoria, Department of Economics.
    2. Cheema, Muhammad A. & Scrimgeour, Frank, 2019. "Oil prices and stock market anomalies," Energy Economics, Elsevier, vol. 83(C), pages 578-587.
    3. Benkraiem, Ramzi & Lahiani, Amine & Miloudi, Anthony & Shahbaz, Muhammad, 2018. "New insights into the US stock market reactions to energy price shocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 169-187.
    4. Moya-Martínez, Pablo & Ferrer-Lapeña, Román & Escribano-Sotos, Francisco, 2014. "Oil price risk in the Spanish stock market: An industry perspective," Economic Modelling, Elsevier, vol. 37(C), pages 280-290.
    5. Kamarudin, Eka Azrin & Masih, Mansur, 2015. "Islamic versus conventional stock market and its co-movement with crude oil: a wavelet analysis," MPRA Paper 65261, University Library of Munich, Germany.
    6. Moreno, Blanca & García-Álvarez, María Teresa & Fonseca, Ana Rosa, 2017. "Fuel prices impacts on stock market of metallurgical industry under the EU emissions trading system," Energy, Elsevier, vol. 125(C), pages 223-233.
    7. Mongi Arfaoui & Aymen Ben Rejeb, 2017. "Oil, gold, US dollar and stock market interdependencies: a global analytical insight," European Journal of Management and Business Economics, Emerald Group Publishing Limited, vol. 26(3), pages 278-293, October.
    8. Boldanov, Rustam & Degiannakis, Stavros & Filis, George, 2017. "Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries," MPRA Paper 80435, University Library of Munich, Germany.
    9. Afees Adebare Salisu & Idris A. Adediran, 2018. "The U.S. Shale Oil Revolution and the Behavior of Commodity Prices," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 3(1), pages 27-53, September.
    10. Mokni, Khaled & Youssef, Manel, 2019. "Measuring persistence of dependence between crude oil prices and GCC stock markets: A copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 14-33.
    11. Gupta, Kartick, 2016. "Oil price shocks, competition, and oil & gas stock returns — Global evidence," Energy Economics, Elsevier, vol. 57(C), pages 140-153.
    12. Anna Créti & Zied Ftiti & Khaleb Guesmi, 2013. "Oil price impact on financial markets: co-spectral analysis for exporting versus importing countries," Working Papers hal-00822070, HAL.
    13. Zhang, Chuanguo & Chen, Xiaoqing, 2011. "The impact of global oil price shocks on China’s stock returns: Evidence from the ARJI(-ht)-EGARCH model," Energy, Elsevier, vol. 36(11), pages 6627-6633.
    14. Degiannakis, Stavros & Filis, George & Kizys, Renatas, 2014. "The effects of oil price shocks on stock market volatility: Evidence from European data," MPRA Paper 96296, University Library of Munich, Germany.
    15. Mohamed Fakhfekh & Ahmed Ghorbel & Nadhem Selmi & Nejib Hachicha, 2017. "Dependence between oil price volatility, Islamic and conventional Dow Jones indexes: Implication for portfolio management and hedging effectiveness," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 29-48, January.
    16. Mohammad I. Elian & Khalid M. Kisswani, 2018. "Oil price changes and stock market returns: cointegration evidence from emerging market," Economic Change and Restructuring, Springer, vol. 51(4), pages 317-337, November.
    17. Ivanenko, Victor & Pasichnichenko, Illia, 2016. "Expected utility for nonstochastic risk," MPRA Paper 70433, University Library of Munich, Germany.
    18. Fernandes, Leonardo H.S. & Araújo, Fernando H.A., 2020. "Taxonomy of commodities assets via complexity-entropy causality plane," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    19. Zhang, Hongwei & Jin, Chen & Bouri, Elie & Gao, Wang & Xu, Yahua, 2023. "Realized higher-order moments spillovers between commodity and stock markets: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 30(C).
    20. Civcir, Irfan & Akkoc, Ugur, 2021. "Non-linear ARDL approach to the oil-stock nexus: Detailed sectoral analysis of the Turkish stock market," Resources Policy, Elsevier, vol. 74(C).
    21. Razak, Razman & Masih, Mansur, 2017. "The links between crude palm oil, conventional and Islamic stock markets: evidence from Malaysia based on continuous and discrete wavelet analysis," MPRA Paper 79717, University Library of Munich, Germany.
    22. Escribano, Ana & Koczar, Monika W. & Jareño, Francisco & Esparcia, Carlos, 2023. "Shock transmission between crude oil prices and stock markets," Resources Policy, Elsevier, vol. 83(C).
    23. Abid, Ilyes & Goutte, Stéphane & Guesmi, Khaled & Jamali, Ibrahim, 2019. "Transmission of shocks and contagion from U.S. to MENA equity markets: The role of oil and gas markets," Energy Policy, Elsevier, vol. 134(C).
    24. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2016. "Intraday volatility interaction between the crude oil and equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 1-13.
    25. Suliman Zakaria S. Abdalla, 2014. "The Impact of Oil Price Fluctuations on the Sudanese Stock Market Performance," Working Papers 887, Economic Research Forum, revised Dec 2014.
    26. Rıza Demirer & Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2017. "Time-Varying Rare Disaster Risks, Oil Returns and Volatility," Working Papers 201762, University of Pretoria, Department of Economics.
    27. Acaravci, Ali & Ozturk, Ilhan & Kandir, Serkan Yilmaz, 2012. "Natural gas prices and stock prices: Evidence from EU-15 countries," Economic Modelling, Elsevier, vol. 29(5), pages 1646-1654.
    28. Xu, Weiju & Ma, Feng & Chen, Wang & Zhang, Bing, 2019. "Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States," Energy Economics, Elsevier, vol. 80(C), pages 310-320.
    29. Urom, Christian & Onwuka, Kevin O. & Uma, Kalu E. & Yuni, Denis N., 2020. "Regime dependent effects and cyclical volatility spillover between crude oil price movements and stock returns," International Economics, Elsevier, vol. 161(C), pages 10-29.
    30. Julia Kielmann & Hans Manner & Aleksey Min, 2022. "Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models," Empirical Economics, Springer, vol. 62(4), pages 1543-1574, April.
    31. Nadal, Raquel & Szklo, Alexandre & Lucena, André, 2017. "Time-varying impacts of demand and supply oil shocks on correlations between crude oil prices and stock markets indices," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1011-1020.
    32. Nicholas Apergis & Alexandros Gabrielsen & Lee A. Smales, 2016. "(Unusual) weather and stock returns—I am not in the mood for mood: further evidence from international markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 30(1), pages 63-94, February.
    33. Cunado, Juncal & Perez de Gracia, Fernando, 2014. "Oil price shocks and stock market returns: Evidence for some European countries," Energy Economics, Elsevier, vol. 42(C), pages 365-377.
    34. Sakaki, Hamid, 2019. "Oil price shocks and the equity market: Evidence for the S&P 500 sectoral indices," Research in International Business and Finance, Elsevier, vol. 49(C), pages 137-155.
    35. Jiang, Wei & Liu, Yan, 2021. "The asymmetric effect of crude oil prices on stock prices in major international financial markets," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    36. Natanelov, Valeri & McKenzie, Andrew M. & Van Huylenbroeck, Guido, 2013. "Crude oil–corn–ethanol – nexus: A contextual approach," Energy Policy, Elsevier, vol. 63(C), pages 504-513.
    37. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    38. Rania Jammazi & Duc Khuong Nguyen, 2015. "Responses of international stock markets to oil price surges: a regime-switching perspective," Applied Economics, Taylor & Francis Journals, vol. 47(41), pages 4408-4422, September.
    39. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2012. "On the Macroeconomic Determinants of the Long-Term Oil-Stock Correlation," Working Papers 0525, University of Heidelberg, Department of Economics.
    40. Pal, Debdatta & Mitra, Subrata K., 2019. "Oil price and automobile stock return co-movement: A wavelet coherence analysis," Economic Modelling, Elsevier, vol. 76(C), pages 172-181.
    41. K.S., Sujit & Ray, Subhajyoti, 2023. "Linear and nonlinear asymmetric relationship in crude oil, gold, stock market and exchange rates: An evidence from the UAE," Resources Policy, Elsevier, vol. 83(C).
    42. Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2010. "Conditional Correlations and Volatility Spillovers Between Crude Oil and Stock Index Returns," CARF F-Series CARF-F-202, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    43. Fayyad, Abdallah & Daly, Kevin, 2011. "The impact of oil price shocks on stock market returns: Comparing GCC countries with the UK and USA," Emerging Markets Review, Elsevier, vol. 12(1), pages 61-78, March.
    44. Katarzyna Kuziak & Joanna Górka, 2023. "Dependence Analysis for the Energy Sector Based on Energy ETFs," Energies, MDPI, vol. 16(3), pages 1-30, January.
    45. Degiannakis, Stavros & Filis, George & Arora, Vipin, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," MPRA Paper 96270, University Library of Munich, Germany.
    46. Christos Kollias & Catherine Kyrtsou & Stephanos Papadamou, 2011. "The Effects of Terrorism and War on the Oil and Prices Stock Indices Relationship," Economics of Security Working Paper Series 57, DIW Berlin, German Institute for Economic Research.
    47. David C Broadstock & Rui Wang & Dayong Zhang, 2014. "The direct and indirect effects of oil shocks on energy related stocks," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 146, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    48. Guo, Ranran & Ye, Wuyi, 2021. "A model of dynamic tail dependence between crude oil prices and exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    49. Dogah, Kingsley E. & Premaratne, Gamini, 2018. "Sectoral exposure of financial markets to oil risk factors in BRICS countries," Energy Economics, Elsevier, vol. 76(C), pages 228-256.
    50. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    51. Yue-Jun Zhang & Yi-Ming Wei, 2011. "The dynamic influence of advanced stock market risk on international crude oil returns: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 967-978.
    52. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2014. "The Impact of Oil Price Shocks on the Stock Market Return and Volatility Relationship," CAMA Working Papers 2014-71, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    53. Isah Wada, 2019. "Dynamic Effects of Crude Oil Price Movements: a Sectoral Examination," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 22(71), pages 17-28, March.
    54. Ben Cheikh, Nidhaleddine & Ben Naceur, Sami & Kanaan, Oussama & Rault, Christophe, 2020. "Investigating the Asymmetric Impact of Oil Prices on GCC Stock Markets," IZA Discussion Papers 13853, Institute of Labor Economics (IZA).
    55. Qin Zhang & Jin Boon Wong, 2023. "The influence of oil price uncertainty on stock liquidity," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(2), pages 141-167, February.
    56. Junttila, Juha & Pesonen, Juho & Raatikainen, Juhani, 2018. "Commodity market based hedging against stock market risk in times of financial crisis: The case of crude oil and gold," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 255-280.
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    299. Tim Friedhoff & Cam-Duc Au & Philippe Krahnhof, 2023. "Analysis of the Impact of Orthogonalized Brent Oil Price Shocks on the Returns of Dependent Industries in Times of the Russian War," MUNI ECON Working Papers 2023-04, Masaryk University.
    300. Degiannakis, Stavros & Filis, George & Floros, Christos, 2013. "Oil and stock returns: Evidence from European industrial sector indices in a time-varying environment," MPRA Paper 96298, University Library of Munich, Germany.
    301. Balli, Faruk & O Balli, Hatice & Nguyen, Thi Thu Ha, 2023. "Dynamic connectedness between crude oil and equity markets: What about the effects of firm's solvency and profitability positions?," Journal of Commodity Markets, Elsevier, vol. 31(C).
    302. Bouri, Elie, 2015. "A broadened causality in variance approach to assess the risk dynamics between crude oil prices and the Jordanian stock market," Energy Policy, Elsevier, vol. 85(C), pages 271-279.
    303. Stavros Degiannakis & George Filis & Renatas Kizys, 2013. "Oil price shocks and stock market volatility: evidence from European data," Working Papers 161, Bank of Greece.
    304. Yushu Li & Hyunjoo Kim Karlsson, 2023. "Investigating the Asymmetric Behavior of Oil Price Volatility Using Support Vector Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1765-1790, April.
    305. Joo, Young C. & Park, Sung Y., 2017. "Oil prices and stock markets: Does the effect of uncertainty change over time?," Energy Economics, Elsevier, vol. 61(C), pages 42-51.
    306. Jammazi, Rania & Reboredo, Juan C., 2016. "Dependence and risk management in oil and stock markets. A wavelet-copula analysis," Energy, Elsevier, vol. 107(C), pages 866-888.
    307. Ftiti, Zied & Guesmi, Khaled & Abid, Ilyes, 2016. "Oil price and stock market co-movement: What can we learn from time-scale approaches?," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 266-280.
    308. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Spillovers between oil and stock markets at times of geopolitical unrest and economic turbulence," MPRA Paper 59760, University Library of Munich, Germany.
    309. Guhathakurta, Kousik & Dash, Saumya Ranjan & Maitra, Debasish, 2020. "Period specific volatility spillover based connectedness between oil and other commodity prices and their portfolio implications," Energy Economics, Elsevier, vol. 85(C).
    310. Zhu, Hui-Ming & Li, Su-Fang & Yu, Keming, 2011. "Crude oil shocks and stock markets: A panel threshold cointegration approach," Energy Economics, Elsevier, vol. 33(5), pages 987-994, September.
    311. Manel Youssef & Khaled Mokni, 2019. "Do Crude Oil Prices Drive the Relationship between Stock Markets of Oil-Importing and Oil-Exporting Countries?," Economies, MDPI, vol. 7(3), pages 1-22, July.
    312. Go You-How & Chin Lai-Kwan & Kuah Yoke-Chin & Wei Chooi-Yi, 2018. "Information Spillover Between Crude Oil and Stock Markets: Evidence from Subsidy Cut for RON95 Fuel Price in Malaysia," Global Business Review, International Management Institute, vol. 19(4), pages 889-901, August.
    313. Begüm Yurteri Kösedağlı & Gül Huyugüzel Kışla & A. Nazif Çatık, 2021. "The time-varying effects of oil prices on oil–gas stock returns of the fragile five countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-22, December.
    314. Suleman Sarwar & Rida Waheed & Mehnoor Amir & Muqaddas Khalid, 2018. "Role of Energy on Economy The Case of Micro to Macro Level Analysis," Economics Bulletin, AccessEcon, vol. 38(4), pages 1905-1926.
    315. Balakumar, Suganya & Dash, Saumya Ranjan & Maitra, Debasish & Kang, Sang Hoon, 2022. "Do oil price shocks have any implications for stock return momentum?," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 637-663.
    316. Chou, Kuo-Wei & Tseng, Yi-Heng, 2016. "Oil prices, exchange rate, and the price asymmetry in the Taiwanese retail gasoline market," Economic Modelling, Elsevier, vol. 52(PB), pages 733-741.
    317. Ntantamis, Christos & Zhou, Jun, 2015. "Bull and bear markets in commodity prices and commodity stocks: Is there a relation?," Resources Policy, Elsevier, vol. 43(C), pages 61-81.
    318. Haykir, Ozkan & Yagli, Ibrahim & Aktekin Gok, Emine Dilara & Budak, Hilal, 2022. "Oil price explosivity and stock return: Do sector and firm size matter?," Resources Policy, Elsevier, vol. 78(C).
    319. Li, Jiang-Cheng & Leng, Na & Zhong, Guang-Yan & Wei, Yu & Peng, Jia-Sheng, 2020. "Safe marginal time of crude oil price via escape problem of econophysics," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    320. Jihed Majdoub & Walid Mansour & Islem Arrak, 2018. "Volatility Spillover among Equity Indices and Crude Oil Prices: Evidence from Islamic Markets امتداد التقلب بين مؤشرات الأسهم وأسعار النفط الخام: شواهد من الأسواق الإسلامية," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 31(1), pages 27-45, January.
    321. Sukcharoen, Kunlapath & Zohrabyan, Tatevik & Leatham, David & Wu, Ximing, 2014. "Interdependence of oil prices and stock market indices: A copula approach," Energy Economics, Elsevier, vol. 44(C), pages 331-339.
    322. Aktolkin Abubakirova & Aziza Syzdykova & Assan Dosmakhanbet & Lyazzat Kudabayeva & Gulnar Abdulina, 2021. "Relationship between Oil Prices and Stock Prices in BRICS-T Countries: Symmetric and Asymmetric Causality Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 140-148.
    323. Shegorika Rajwani & Dilip Kumar, 2016. "Asymmetric Dynamic Conditional Correlation Approach to Financial Contagion: A Study of Asian Markets," Global Business Review, International Management Institute, vol. 17(6), pages 1339-1356, December.
    324. de Jesus, Diego Pitta & Lenin Souza Bezerra, Bruno Felipe & da Nóbrega Besarria, Cássio, 2020. "The non-linear relationship between oil prices and stock prices: Evidence from oil-importing and oil-exporting countries," Research in International Business and Finance, Elsevier, vol. 54(C).
    325. Filippidis, Michail & Filis, George & Kizys, Renatas, 2020. "Oil price shocks and EMU sovereign yield spreads," Energy Economics, Elsevier, vol. 86(C).
    326. Caporale, Guglielmo Maria & Çatık, Abdurrahman Nazif & Huyuguzel Kısla, Gul Serife & Helmi, Mohamad Husam & Akdeniz, Coşkun, 2022. "Oil prices and sectoral stock returns in the BRICS-T countries: A time-varying approach," Resources Policy, Elsevier, vol. 79(C).
    327. Julia Kielmann & Hans Manner & Aleksey Min, 2021. "Stock Market Returns and Oil Price Shocks: A CoVaR Analysis based on Dynamic Vine Copula Models," Graz Economics Papers 2021-01, University of Graz, Department of Economics.
    328. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Karikari, Nana Kwasi & Hammoudeh, Shawkat, 2022. "Time-varying dependence dynamics between international commodity prices and Australian industry stock returns: a Perspective for portfolio diversification," Energy Economics, Elsevier, vol. 108(C).
    329. 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).
    330. Kumar, Pawan & Singh, Vipul Kumar, 2022. "Systemic spillover dynamics of crude oil with Indian Financial indicators in post WPI revision and COVID era," Resources Policy, Elsevier, vol. 77(C).
    331. Imran Yousaf & Shoaib Ali & Muhammad Naveed & Ifraz Adeel, 2021. "Risk and Return Transmissions From Crude Oil to Latin American Stock Markets During the Crisis: Portfolio Implications," SAGE Open, , vol. 11(2), pages 21582440211, April.
    332. Bahram Adrangi & Arjun Chatrath & Joseph Macri & Kambiz Raffiee, 2021. "Dynamics of crude oil price shocks and major Latin American Equity Markets: A study in time and frequency domains," Bulletin of Economic Research, Wiley Blackwell, vol. 73(3), pages 432-455, July.
    333. Chatterjee, Susmita & Bagchi, Bhaskar & Dandapat, Dhruba Ranjan, 2016. "Oil Price Shock and Effects on Stock Markets of Emerging Economies," MPRA Paper 75883, University Library of Munich, Germany.
    334. Bhatia, Vaneet & Basu, Sankarshan, 2021. "Causality-in-quantiles between crude oil and stock markets: Evidence from emerging economies," Finance Research Letters, Elsevier, vol. 40(C).
    335. Mensi, Walid & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Vo, Xuan Vinh, 2023. "Frequency dependence between oil futures and international stock markets and the role of gold, bonds, and uncertainty indices: Evidence from partial and multivariate wavelet approaches," Resources Policy, Elsevier, vol. 80(C).
    336. Degiannakis, Stavros & Filis, George & Floros, Christos, 2013. "Oil and stock price returns: Evidence from European industrial sector indices in a time-varying environment," MPRA Paper 80495, University Library of Munich, Germany.
    337. Bajo-Rubio, Oscar & Berke, Burcu & McMillan, David, 2017. "The behaviour of asset return and volatility spillovers in Turkey: A tale of two crises," Research in International Business and Finance, Elsevier, vol. 41(C), pages 577-589.
    338. Dinesh Gajurel & Akhila Chawla, 2022. "The oil price crisis and contagion effects on the Canadian economy," Applied Economics, Taylor & Francis Journals, vol. 54(13), pages 1527-1543, March.
    339. Zhu, Huiming & Huang, Hui & Peng, Cheng & Yang, Yan, 2016. "Extreme dependence between crude oil and stock markets in Asia-Pacific regions: Evidence from quantile regression," Economics Discussion Papers 2016-46, Kiel Institute for the World Economy (IfW Kiel).

  21. J. Isaac Miller, 2007. "Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error," Working Papers 0722, Department of Economics, University of Missouri, revised 15 Apr 2009.

    Cited by:

    1. Mishra, Vinod & Smyth, Russell, 2010. "Female labor force participation and total fertility rates in the OECD: New evidence from panel cointegration and Granger causality testing," Journal of Economics and Business, Elsevier, vol. 62(1), pages 48-64, January.
    2. Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.

  22. Chang, Yoosoon & Miller, J. Isaac & Park, Joon Y., 2005. "Extracting a Common Stochastic Trend: Theories with Some Applications," Working Papers 2005-06, Rice University, Department of Economics.

    Cited by:

    1. Tara Sinclair & Sinchan Mitra, 2008. "Output Fluctuations in the G-7: An Unobserved Components Approach," Working Papers 2008-04, The George Washington University, Institute for International Economic Policy.
    2. Herwartz, Helmut & Weber, Henning, 2010. "The euro's trade effect under cross-sectional heterogeneity and stochastic resistance," Kiel Working Papers 1631, Kiel Institute for the World Economy (IfW Kiel).
    3. Herwartz, Helmut & Weber, Henning, 2013. "The role of cross-sectional heterogeneity for magnitude and timing of the euro's trade effect," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 48-74.

  23. Miller, J. Isaac & Park, Joon Y., 2005. "How They Interact to Generate Persistency in Memory," Working Papers 2005-01, Rice University, Department of Economics.

    Cited by:

    1. Han, Heejoon & Park, Joon Y., 2006. "Time series properties of ARCH processes with persistent covariates," MPRA Paper 5199, University Library of Munich, Germany.
    2. Chung, Heetaik & Park, Joon Y., 2007. "Nonstationary nonlinear heteroskedasticity in regression," Journal of Econometrics, Elsevier, vol. 137(1), pages 230-259, March.

  24. Joon Y. Park & J. Isaac Miller, 2004. "Nonlinearity, Nonstationarity, and Thick Tails: How They Interact to Generate Persistency in Memory," Econometric Society 2004 North American Summer Meetings 597, Econometric Society.

    Cited by:

    1. Guillaume Chevillon & Alain Hecq & Sébastien Laurent, 2018. "Generating Univariate Fractional Integration within a Large VAR(1)," AMSE Working Papers 1844, Aix-Marseille School of Economics, France.
    2. Ioannis Kasparis & Peter C.B. Phillips & Tassos Magdalinos, 2012. "Non-linearity Induced Weak Instrumentation," University of Cyprus Working Papers in Economics 02-2012, University of Cyprus Department of Economics.
    3. Han, Heejoon & Park, Joon Y., 2006. "Time series properties of ARCH processes with persistent covariates," MPRA Paper 5199, University Library of Munich, Germany.
    4. Chevillon, Guillaume & Mavroeidis, Sophocles, 2011. "Learning generates Long Memory," ESSEC Working Papers WP1113, ESSEC Research Center, ESSEC Business School.
    5. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    6. Chevillon, G. & Hecq, A.W. & Laurent, S.F.J.A., 2015. "Long memory through marginalization of large systems and hidden cross-section dependence," Research Memorandum 014, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Chung, Heetaik & Park, Joon Y., 2007. "Nonstationary nonlinear heteroskedasticity in regression," Journal of Econometrics, Elsevier, vol. 137(1), pages 230-259, March.
    9. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    10. Miller, J. Isaac, 2011. "Testing the bounds: Empirical behavior of target zone fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 1782-1792, July.
    11. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. Chang, Yoosoon & Miller, J. Isaac & Park, Joon Y., 2005. "Extracting a Common Stochastic Trend: Theories with Some Applications," Working Papers 2005-06, Rice University, Department of Economics.
    13. Chevillon, Guillaume & Mavroeidis, Sophocles, 2017. "Learning can generate long memory," Journal of Econometrics, Elsevier, vol. 198(1), pages 1-9.

Articles

  1. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    See citations under working paper version above.
  2. Chang, Yoosoon & Kaufmann, Robert K. & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2020. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Journal of Econometrics, Elsevier, vol. 214(1), pages 274-294.
    See citations under working paper version above.
  3. J. Isaac Miller, 2019. "Testing Cointegrating Relationships Using Irregular and Non‐Contemporaneous Series with an Application to Paleoclimate Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 936-950, November.
    See citations under working paper version above.
  4. Miller, J. Isaac, 2018. "Simple robust tests for the specification of high-frequency predictors of a low-frequency series," Econometrics and Statistics, Elsevier, vol. 5(C), pages 45-66.
    See citations under working paper version above.
  5. Jing Zhao & J. Isaac Miller & Wyatt Thompson, 2018. "Modeling and Extrapolating Wheat Producer Support Using Income and Other Factors," Journal of Agricultural Economics, Wiley Blackwell, vol. 69(2), pages 338-350, June.

    Cited by:

    1. Zhao, Jing & Miller, J. Isaac & Binfield, Julian & Thompson, Wyatt, 2022. "Modeling and Forecasting Agricultural Commodity Support in the Developing Countries," Commissioned Papers 321785, International Agricultural Trade Research Consortium.

  6. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2016. "Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 232-243.
    See citations under working paper version above.
  7. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    See citations under working paper version above.
  8. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
    See citations under working paper version above.
  9. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.

    Cited by:

    1. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    2. J. Isaac Miller, 2018. "Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data," Working Papers 1809, Department of Economics, University of Missouri.
    3. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.

  10. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    See citations under working paper version above.
  11. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.

    Cited by:

    1. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Jeyhun Mammadov, 2020. "Gasoline Demand Elasticities at the Backdrop of Lower Oil Prices: Fuel-Subsidizing Country Case," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    3. Liddle, Brantley, 2023. "Is timing everything? Assessing the evidence on whether energy/electricity demand elasticities are time-varying," Energy Economics, Elsevier, vol. 124(C).
    4. Asuamah Yeboah, Samuel, 2018. "Do government activities determine electricity consumption in Ghana? An empirical investigation," MPRA Paper 89408, University Library of Munich, Germany.
    5. Hocheol Jeon, 2019. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data," Energies, MDPI, vol. 12(23), pages 1-15, November.
    6. Julián Pérez-García & Julián Moral-Carcedo, 2017. "Why Electricity Demand Is Highly Income-Elastic in Spain: A Cross-Country Comparison Based on an Index-Decomposition Analysis," Energies, MDPI, vol. 10(3), pages 1-20, March.
    7. Yoosoon Chang & Yongok Choi & Chang Sik Kim & Joon Y. Park & J. Isaac Miller, 2013. "Disentangling Temporal Patterns in Elasticities: A Functional Coefficient Panel Analysis of Electricity Demand," Working Papers 1320, Department of Economics, University of Missouri.
    8. Hyo-Jin Kim & Gyeong-Sam Kim & Seung-Hoon Yoo, 2019. "Demand Function for Industrial Electricity: Evidence from South Korean Manufacturing Sector," Sustainability, MDPI, vol. 11(18), pages 1-11, September.
    9. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    10. Kyungsik Nam, 2021. "Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing," Econometrics, MDPI, vol. 9(1), pages 1-25, February.
    11. Daniel de Abreu Pereira Uhr & Júlia Gallego Ziero Uhr, André Luis Squarize Chagas, 2017. "Estimation of price and income elasticities for the Brazilian household electricity demand," Working Papers, Department of Economics 2017_12, University of São Paulo (FEA-USP).
    12. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
    13. Sharimakin, Akinsehinwa, 2021. "Modelling asymmetric price responses of industrial energy demand with a dynamic hierarchical model," Energy Economics, Elsevier, vol. 98(C).
    14. Kyungsik Nam & Sungro Lee & Hocheol Jeon, 2020. "Nonlinearity between CO 2 Emission and Economic Development: Evidence from a Functional Coefficient Panel Approach," Sustainability, MDPI, vol. 12(24), pages 1-10, December.
    15. Pereira Uhr, Daniel de Abreu & Squarize Chagas, André Luis & Ziero Uhr, Júlia Gallego, 2019. "Estimation of elasticities for electricity demand in Brazilian households and policy implications," Energy Policy, Elsevier, vol. 129(C), pages 69-79.
    16. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    17. Yasunobu Wakashiro, 2019. "Estimating price elasticity of demand for electricity: the case of Japanese manufacturing industry," International Journal of Economic Policy Studies, Springer, vol. 13(1), pages 173-191, January.
    18. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
    19. Fukushige, Mototsugu & Yamawaki, Hiroshige, 2015. "The relationship between an electricity supply ceiling and economic growth: An application of disequilibrium modeling to Taiwan," Journal of Asian Economics, Elsevier, vol. 36(C), pages 14-23.
    20. Hyo-Jin Kim & Jae-Sung Paek & Seung-Hoon Yoo, 2019. "Price Elasticity of Heat Demand in South Korean Manufacturing Sector: An Empirical Investigation," Sustainability, MDPI, vol. 11(21), pages 1-10, November.
    21. Khan, Muhammad Arshad & Abbas, Faisal, 2016. "The dynamics of electricity demand in Pakistan: A panel cointegration analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1159-1178.
    22. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    23. Ribó-Pérez, David & Van der Weijde, Adriaan H. & Álvarez-Bel, Carlos, 2019. "Effects of self-generation in imperfectly competitive electricity markets: The case of Spain," Energy Policy, Elsevier, vol. 133(C).
    24. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    25. Soon, Byung Min & Thompson, Wyatt, 2017. "Testing for Persistent Japanese Beef Trade Impacts from BSE Using a Time-Varying Armington Model," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259150, Agricultural and Applied Economics Association.
    26. Martin Falk & Xiang Lin, 2018. "Income elasticity of overnight stays over seven decades," Tourism Economics, , vol. 24(8), pages 1015-1028, December.
    27. Agnolucci, Paolo & De Lipsis, Vincenzo & Arvanitopoulos, Theodoros, 2017. "Modelling UK sub-sector industrial energy demand," Energy Economics, Elsevier, vol. 67(C), pages 366-374.
    28. Keita Honjo & Hiroto Shiraki & Shuichi Ashina, 2018. "Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    29. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Carlo A. Bollino & Ceyhun Mahmudlu, 2017. "Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach," Energies, MDPI, vol. 10(11), pages 1-12, November.
    30. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    31. Daniel Morais de Souza & Rogerio Silva de Mattos & Alexandre Zanini, 2022. "Estimating Elasticities for the Residential Demand of Electricity in Brazil Using Cointegration Models," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 315-324, March.
    32. Ozturk, Ilhan & Arisoy, Ibrahim, 2016. "An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach," Energy Policy, Elsevier, vol. 99(C), pages 174-179.
    33. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Marzio Galeotti, 2018. "Decoupling of C02 Emissions and GDP: A Time-Varying Cointegration Approach," IEFE Working Papers 101, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    34. Nikos Sakkas & Sofia Yfanti & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2021. "Interpretable Forecasting of Energy Demand in the Residential Sector," Energies, MDPI, vol. 14(20), pages 1-17, October.
    35. Joo, Young C. & Park, Sung Y., 2017. "Oil prices and stock markets: Does the effect of uncertainty change over time?," Energy Economics, Elsevier, vol. 61(C), pages 42-51.
    36. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    37. Tan, Xiujie & Wang, Banban & Wei, Jie & Taghizadeh-Hesary, Farhad, 2023. "The role of carbon pricing in achieving energy transition in the Post-COP26 era: Evidence from China's industrial energy conservation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    38. Chang Sik Kim & Sunghyun Kim & Yunjong Wang, 2018. "RMB Bloc in East Asia: Too Early to Talk About It?," Asian Economic Papers, MIT Press, vol. 17(3), pages 31-48, Fall.
    39. Hortay, Olivér & Szőke, Tamás, 2019. "Keresleti árrugalmasság becslése a magyar villamosenergia-piacon [Estimating demand-price elasticity on the Hungarian electric energy market]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 788-804.
    40. Wang, Banban & Wei, Jie & Tan, Xiujie & Su, Bin, 2021. "The sectorally heterogeneous and time-varying price elasticities of energy demand in China," Energy Economics, Elsevier, vol. 102(C).
    41. Aslam, Misbah & Ahmad, Eatzaz, 2023. "Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan," Energy, Elsevier, vol. 282(C).
    42. Jeyhun Mikayilov & Fred Joutz & Fakhri Hasanov, 2019. "Gasoline Demand in Saudi Arabia: Are the Price and Income Elasticities Constant?," Discussion Papers ks--2019-dp81, King Abdullah Petroleum Studies and Research Center.

  12. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    See citations under working paper version above.
  13. Miller, J. Isaac & Ni, Shawn, 2011. "Long-Term Oil Price Forecasts: A New Perspective On Oil And The Macroeconomy," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 396-415, November.
    See citations under working paper version above.
  14. Miller, J. Isaac, 2011. "Testing the bounds: Empirical behavior of target zone fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 1782-1792, July.
    See citations under working paper version above.
  15. Miller, J. Isaac & Park, Joon Y., 2010. "Nonlinearity, nonstationarity, and thick tails: How they interact to generate persistence in memory," Journal of Econometrics, Elsevier, vol. 155(1), pages 83-89, March.
    See citations under working paper version above.
  16. Miller J. Isaac, 2010. "A Nonlinear IV Likelihood-Based Rank Test for Multivariate Time Series and Long Panels," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-38, September.
    See citations under working paper version above.
  17. J. Isaac Miller, 2010. "Cointegrating regressions with messy regressors and an application to mixed‐frequency series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 255-277, July.

    Cited by:

    1. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    2. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    3. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    4. J. Isaac Miller, 2018. "Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data," Working Papers 1809, Department of Economics, University of Missouri.
    5. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    6. Francois, John Nana & Ahmad, Nazneen & Keinsley, Andrew & Nti-Addae, Akwasi, 2022. "Heterogeneity in the long-run remittance-output relationship: Theory and new evidence," Economic Modelling, Elsevier, vol. 110(C).
    7. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    8. J. Isaac Miller & Kyungsik Nam, 2019. "Dating Hiatuses: A Statistical Model of the Recent Slowdown in Global Warming – and the Next One," Working Papers 1903, Department of Economics, University of Missouri.

  18. Chang, Yoosoon & Isaac Miller, J. & Park, Joon Y., 2009. "Extracting a common stochastic trend: Theory with some applications," Journal of Econometrics, Elsevier, vol. 150(2), pages 231-247, June.

    Cited by:

    1. Yong Li & Jun Yu, 2011. "Bayesian Hypothesis Testing in Latent Variable Models," Working Papers 11-2011, Singapore Management University, School of Economics.
    2. Tobias Hartl & Roland Weigand, 2018. "Multivariate Fractional Components Analysis," Papers 1812.09149, arXiv.org, revised Jan 2019.
    3. Bretó, Carles, 2014. "On idiosyncratic stochasticity of financial leverage effects," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 20-26.
    4. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    5. Durdyev, Ruslan & Peresetsky, Anatoly, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 39-58.
    6. Berger, Tino & Everaert, Gerdie, 2010. "Labour taxes and unemployment evidence from a panel unobserved component model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 354-364, March.
    7. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    8. Miller, J. Isaac, 2011. "Testing the bounds: Empirical behavior of target zone fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 1782-1792, July.
    9. Miller, J. Isaac & Park, Joon Y., 2010. "Nonlinearity, nonstationarity, and thick tails: How they interact to generate persistence in memory," Journal of Econometrics, Elsevier, vol. 155(1), pages 83-89, March.
    10. Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2015. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," MPRA Paper 64503, University Library of Munich, Germany.
    11. Manevich, Vyacheslav & Peresetsky, Anatoly & Pogorelova, Polina, 2022. "Stock market and cryptocurrency market volatility," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 65-76.
    12. Herwartz, Helmut & Weber, Henning, 2010. "The euro's trade effect under cross-sectional heterogeneity and stochastic resistance," Kiel Working Papers 1631, Kiel Institute for the World Economy (IfW Kiel).
    13. Shuo Cao & Hongyi Chen, 2017. "Exchange Rate Movements and Fundamentals: Impact of Oil Prices and China¡¯s Growth," Working Papers 042017, Hong Kong Institute for Monetary Research.
    14. Herwartz, Helmut & Weber, Henning, 2013. "The role of cross-sectional heterogeneity for magnitude and timing of the euro's trade effect," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 48-74.
    15. Adland, Roar & Benth, Fred Espen & Koekebakker, Steen, 2018. "Multivariate modeling and analysis of regional ocean freight rates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 113(C), pages 194-221.
    16. Chen, Xiaoshan & MacDonald, Ronald, 2015. "Measuring the dollar–euro permanent equilibrium exchange rate using the unobserved components model," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 20-35.

  19. Miller, J. Isaac & Ratti, Ronald A., 2009. "Crude oil and stock markets: Stability, instability, and bubbles," Energy Economics, Elsevier, vol. 31(4), pages 559-568, July.
    See citations under working paper version above.

Chapters

  1. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 14, pages 93-122, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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