IDEAS home Printed from https://ideas.repec.org/f/pmo1135.html
   My authors  Follow this author

Kaiji Motegi

Personal Details

First Name:Kaiji
Middle Name:
Last Name:Motegi
Suffix:
RePEc Short-ID:pmo1135
[This author has chosen not to make the email address public]
http://www2.kobe-u.ac.jp/~motegi/

Affiliation

Faculty of Economics
Kobe University

Kobe, Japan
http://www.econ.kobe-u.ac.jp/
RePEc:edi:fekobjp (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.
  2. 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.

Articles

  1. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
  2. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.
  3. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
  4. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.

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. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.

    Cited by:

    1. Jiang, Qingshan & Xu, Li & Huang, Can, 2022. "Covariates distributions balancing for continuous treatment," Economics Letters, Elsevier, vol. 217(C).
    2. Chen, Xiaohong & Liu, Ying & Ma, Shujie & Zhang, Zheng, 2024. "Causal inference of general treatment effects using neural networks with a diverging number of confounders," Journal of Econometrics, Elsevier, vol. 238(1).
    3. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for Continuous Treatments," Papers 2402.02535, arXiv.org, revised Nov 2024.
    4. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    5. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    6. Yukitoshi Matsushita & Taisuke Otsu & Keisuke Takahata, 2022. "Estimating density ratio of marginals to joint: Applications to causal inference," STICERD - Econometrics Paper Series 619, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Lin Liu & Chang Li, 2023. "New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators," Papers 2302.08097, arXiv.org.

  2. 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.

    Cited by:

    1. Zhidong Bai & Yongchang Hui & Dandan Jiang & Zhihui Lv & Wing-Keung Wong & Shurong Zheng, 2018. "A new test of multivariate nonlinear causality," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-14, January.
    2. Martin Enilov & Yuan Wang, 2022. "Tourism and economic growth: Multi-country evidence from mixed-frequency Granger causality tests," Tourism Economics, , vol. 28(5), pages 1216-1239, August.
    3. Martin Enilov, 2024. "The predictive power of commodity prices for future economic growth: Evaluating the role of economic development," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3040-3062, July.
    4. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    5. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    6. Mehmet Balcilar & George Ike & Rangan Gupta, 2019. "The Role of Economic Policy Uncertainty in Predicting Output Growth in Emerging Markets: A Mixed-Frequency Granger Causality Approach," Working Papers 201975, University of Pretoria, Department of Economics.
    7. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," Working Papers hal-04141416, HAL.
    8. Saeed Solaymani, 2023. "Impacts of Environmental Variables on Rice Production in Malaysia," World, MDPI, vol. 4(3), pages 1-17, July.
    9. Peng Yue & Yaodong Fan & Jonathan A. Batten & Wei-Xing Zhou, 2020. "Information transfer between stock market sectors: A comparison between the USA and China," Papers 2004.07612, arXiv.org.
    10. Leila Hedhili Zaier & Khaled Mokni & Ahdi Noomen Ajmi, 2024. "Causality relationships between climate policy uncertainty, renewable energy stocks, and oil prices: a mixed-frequency causality analysis," Future Business Journal, Springer, vol. 10(1), pages 1-11, December.
    11. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    12. Nikola Gradojevic & Camillo Lento, 2012. "Multiscale Analysis of Foreign Exchange Order Flows and Technical Trading Profitability," Working Paper series 31_12, Rimini Centre for Economic Analysis.
    13. Yonglian Wang & Lijun Wang & Changchun Pan, 2022. "Tourism–Growth Nexus in the Presence of Instability," Sustainability, MDPI, vol. 14(4), pages 1-11, February.
    14. Xuan Lv & Menggang Li & Yingjie Zhang, 2022. "Financial Stability and Economic Activity in China: Based on Mixed-Frequency Spillover Method," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    15. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    16. Ashton de Silva & Maria Yanotti & Sarah Sinclair & Sveta Angelopoulos, 2023. "Place‐Based Policies and Nowcasting," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(3), pages 363-370, September.
    17. Feng-Li Lin & Mei-Chih Wang, 2019. "Does economic growth cause military expenditure to go up? Using MF-VAR model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(6), pages 3097-3117, November.
    18. Mikhail Stolbov & Maria Shchepeleva, 2023. "Sentiment-based indicators of real estate market stress and systemic risk: international evidence," Annals of Finance, Springer, vol. 19(3), pages 355-382, September.
    19. Simona Boffelli & Vasiliki D. Skintzi & Giovanni Urga, 2017. "High- and Low-Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 62-105.
    20. Martin Enilov & Giorgio Fazio & Atanu Ghoshray, 2023. "Global connectivity between commodity prices and national stock markets: A time‐varying MIDAS analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2607-2619, July.
    21. Burak Alparslan Eroğlu & Deniz İkizlerli & Numan Ülkü, 2024. "A mixed-frequency VAR application to studying joint dynamics of foreign investor trading and stock market returns," Empirical Economics, Springer, vol. 67(1), pages 47-73, July.
    22. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    23. Hong, Yanran & Xu, Pengfei & Wang, Lu & Pan, Zhigang, 2022. "Relationship between the news-based categorical economic policy uncertainty and US GDP: A mixed-frequency Granger-causality analysis," Finance Research Letters, Elsevier, vol. 48(C).
    24. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    25. Biao Gu & Liying Fu & Kehuan Yu, 2023. "On the dynamic effects of the cross‐section distribution of sectoral price changes in China," International Studies of Economics, John Wiley & Sons, vol. 18(4), pages 468-501, December.
    26. 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.
    27. Götz, T.B. & Hecq, A.W. & Smeekes, S., 2015. "Testing for Granger Causality in Large Mixed-Frequency VARs," Research Memorandum 036, Maastricht University, Graduate School of Business and Economics (GSBE).
    28. Hecq, Alain & Goetz, Thomas, 2018. "Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes," MPRA Paper 87746, University Library of Munich, Germany.
    29. Chang, Tsangyao & Hsu, Chen-Min & Chen, Sheng-Tung & Wang, Mei-Chih & Wu, Cheng-Feng, 2023. "Revisiting economic growth and CO2 emissions nexus in Taiwan using a mixed-frequency VAR model," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 319-342.
    30. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    31. Bevilacqua, Mattia & Morelli, David & Tunaru, Radu, 2019. "The determinants of the model-free positive and negative volatilities," Journal of International Money and Finance, Elsevier, vol. 92(C), pages 1-24.
    32. 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).
    33. Wang, Ping & Gu, Changgui & Yang, Huijie & Wang, Haiying, 2024. "Identify causality by multi-scale structural complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    34. Hong Shen & Qi Pan, 2022. "Risk Contagion between Commodity Markets and the Macro Economy during COVID-19: Evidence from China," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    35. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    36. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    37. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    38. Yi-Hui Liu & Wei-Shiun Chang & Wen-Yi Chen, 2019. "Health progress and economic growth in the United States: the mixed frequency VAR analyses," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1895-1911, July.
    39. Dunbar, Kwamie, 2022. "Impact of the COVID-19 event on U.S. banks’ financial soundness," Research in International Business and Finance, Elsevier, vol. 59(C).
    40. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    41. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    42. Holmes, Mark J. & Otero, Jesús, 2019. "Re-examining the movements of crude oil spot and futures prices over time," Energy Economics, Elsevier, vol. 82(C), pages 224-236.
    43. Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
    44. Hammoudeh, Shawkat & Uddin, Gazi Salah & Sousa, Ricardo M. & Wadström, Christoffer & Sharmi, Rubaiya Zaman, 2022. "Do pandemic, trade policy and world uncertainties affect oil price returns?," Resources Policy, Elsevier, vol. 77(C).
    45. Dergiades, Theologos & Milas, Costas & Panagiotidis, Theodore, 2020. "A mixed frequency approach for stock returns and valuation ratios," Economics Letters, Elsevier, vol. 187(C).
    46. Andrea Cipollini & Ieva Mikaliunaite, 2021. "Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR," Empirical Economics, Springer, vol. 61(2), pages 855-881, August.
    47. Han Liu & Ying Liu & Yonglian Wang, 2021. "Exploring the influence of economic policy uncertainty on the relationship between tourism and economic growth with an MF-VAR model," Tourism Economics, , vol. 27(5), pages 1081-1100, August.
    48. Hu, Jinyan & Wang, Kai-Hua & Su, Chi Wei & Umar, Muhammad, 2022. "Oil price, green innovation and institutional pressure: A China's perspective," Resources Policy, Elsevier, vol. 78(C).
    49. Wang, Kai-Hua & Su, Chi-Wei & Umar, Muhammad, 2021. "Geopolitical risk and crude oil security: A Chinese perspective," Energy, Elsevier, vol. 219(C).
    50. Haoke Ding & Yinghua Ren & Wanhai You, 2022. "Does uncertainty granger-causes visitor arrivals? evidence from the MF-VAR model," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4193-4215, December.
    51. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).

Articles

  1. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.

    Cited by:

    1. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2020. "Copula-based regression models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    2. Boulin, Alexis & Di Bernardino, Elena & Laloë, Thomas & Toulemonde, Gwladys, 2022. "Non-parametric estimator of a multivariate madogram for missing-data and extreme value framework," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    3. Jierui Du & Xia Cui, 2024. "Semiparametric estimation in generalized additive partial linear models with nonignorable nonresponse data," Statistical Papers, Springer, vol. 65(5), pages 3235-3259, July.

  2. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.

    Cited by:

    1. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    2. Bucci, Andrea & Ciciretti, Vito, 2022. "Market regime detection via realized covariances," Economic Modelling, Elsevier, vol. 111(C).
    3. Chlebus Marcin & Dyczko Michał & Woźniak Michał, 2021. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem," Central European Economic Journal, Sciendo, vol. 8(55), pages 44-62, January.
    4. Motegi, Kaiji & Iitsuka, Yoshitaka, 2023. "Inter-regional dependence of J-REIT stock prices: A heteroscedasticity-robust time series approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    5. Pandey, Dharen Kumar & Lucey, Brian M. & Kumar, Satish, 2023. "Border disputes, conflicts, war, and financial markets research: A systematic review," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    7. Li, Muyi & Zhang, Yanfen, 2022. "Bootstrapping multivariate portmanteau tests for vector autoregressive models with weak assumptions on errors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).

  3. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.

    Cited by:

    1. Petre Caraiani & Rangan Gupta & Chi Keung Marco Lau & Hardik A. Marfatia, 2022. "Effects of Conventional and Unconventional Monetary Policy Shocks on Housing Prices in the United States: The Role of Sentiment," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(3), pages 241-261, July.
    2. Feng-Li Lin & Mei-Chih Wang, 2019. "Does economic growth cause military expenditure to go up? Using MF-VAR model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(6), pages 3097-3117, November.
    3. 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.
    4. Campos-González, Jorge & Balcombe, Kelvin, 2024. "The race between education and technology in Chile and its impact on the skill premium," Economic Modelling, Elsevier, vol. 131(C).
    5. Chang, Tsangyao & Hsu, Chen-Min & Chen, Sheng-Tung & Wang, Mei-Chih & Wu, Cheng-Feng, 2023. "Revisiting economic growth and CO2 emissions nexus in Taiwan using a mixed-frequency VAR model," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 319-342.
    6. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    7. Han Liu & Ying Liu & Yonglian Wang, 2021. "Exploring the influence of economic policy uncertainty on the relationship between tourism and economic growth with an MF-VAR model," Tourism Economics, , vol. 27(5), pages 1081-1100, August.
    8. Hu, Jinyan & Wang, Kai-Hua & Su, Chi Wei & Umar, Muhammad, 2022. "Oil price, green innovation and institutional pressure: A China's perspective," Resources Policy, Elsevier, vol. 78(C).
    9. Meng Yan & Kai Shi, 2024. "Revisiting the Impact of US Uncertainty Shocks: New Evidence from China’s Investment Dynamics," Open Economies Review, Springer, vol. 35(3), pages 457-495, July.
    10. Wang, Kai-Hua & Su, Chi-Wei & Umar, Muhammad, 2021. "Geopolitical risk and crude oil security: A Chinese perspective," Energy, Elsevier, vol. 219(C).

  4. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (2) 2014-06-02 2018-09-10
  2. NEP-DCM: Discrete Choice Models (1) 2020-01-06
  3. NEP-ETS: Econometric Time Series (1) 2014-06-02

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Kaiji Motegi should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.