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Tatsushi Oka

Personal Details

First Name:Tatsushi
Middle Name:
Last Name:Oka
Suffix:
RePEc Short-ID:pok37
[This author has chosen not to make the email address public]
https://sites.google.com/site/homepageoka/
Monash University, Caulfield Campus
Terminal Degree:2010 Department of Economics; Boston University (from RePEc Genealogy)

Affiliation

Department of Econometrics and Business Statistics
Monash Business School
Monash University

Melbourne, Australia
http://business.monash.edu/econometrics-and-business-statistics

: 03 990 52372
03 990 55474
Room 674, Menzies Building, Wellington Road, Clayton, Victoria, 3168
RePEc:edi:dxmonau (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Dukpa Kim & Tatsushi Oka & Francisco Estrada & Pierre Perron, 2018. "Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures," Papers 1805.09937, arXiv.org.
  2. David T. Frazier & Tatsushi Oka & Dan Zhu, 2017. "Indirect Inference with a Non-Smooth Criterion Function," Papers 1708.02365, arXiv.org, revised Aug 2018.
  3. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series," CeMMAP working papers CWP06/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Pierre Perron & Tatsushi Oka, 2011. "Testing for Common Breaks in a Multiple Equations System," Boston University - Department of Economics - Working Papers Series WP2011-057, Boston University - Department of Economics.
  5. Zhongjun Qu & Tatsushi Oka, 2010. "Estimating structural changes in regression quantiles," Boston University - Department of Economics - Working Papers Series WP2010-052, Boston University - Department of Economics.
  6. Tatsushi Oka, 2004. "Juvenile Crime and Punishment: Evidence from Japan," Discussion Papers in Economics and Business 04-16, Osaka University, Graduate School of Economics.

    repec:bos:wpaper:wp2017-003 is not listed on IDEAS

Articles

  1. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
  2. Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
  3. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
  4. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.
  5. Dukpa Kim & Tatsushi Oka, 2014. "Divorce Law Reforms And Divorce Rates In The Usa: An Interactive Fixed‐Effects Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 231-245, March.
  6. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
  7. Tatsushi Oka, 2009. "Juvenile crime and punishment: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 41(24), pages 3103-3115.

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. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series," CeMMAP working papers CWP06/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Bouri, Elie & Gupta, Rangan & Lau, Chi Keung Marco & Roubaud, David & Wang, Shixuan, 2018. "Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 297-307.
    2. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    3. Elie Bouri & Rangan Gupta & Chi Keung Marco Lau & David Roubaud & Shixuan Wang, 2017. "Bitcoin and Global Financial Stress: A Copula-Based Approach to Dependence and Causality-in-Quantiles," Working Papers 201750, University of Pretoria, Department of Economics.
    4. Baumöhl, Eduard & Lyócsa, Štefan, 2017. "Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis," Finance Research Letters, Elsevier, vol. 23(C), pages 152-164.
    5. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Spillovers from the United States to Latin American and G7 stock markets: A VAR quantile analysis," Emerging Markets Review, Elsevier, vol. 31(C), pages 32-46.
    6. Labidi, Chiaz & Rahman, Md Lutfur & Hedström, Axel & Uddin, Gazi Salah & Bekiros, Stelios, 2018. "Quantile dependence between developed and emerging stock markets aftermath of the global financial crisis," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 179-211.
    7. Daniel Danau, 2018. "Prudence and preference for flexibility gain," Working Papers hal-01806743, HAL.
    8. Stelios Bekiros & Syed Jawad Hussain Shahzad & Jose Arreola-Hernandez & Mobeen Ur Rehman, 2018. "Directional predictability and time-varying spillovers between stock markets and economic cycles," Post-Print hal-01996787, HAL.
    9. Shahzad, Syed Jawad Hussain & Naifar, Nader & Hammoudeh, Shawkat & Roubaud, David, 2017. "Directional predictability from oil market uncertainty to sovereign credit spreads of oil-exporting countries: Evidence from rolling windows and crossquantilogram analysis," Energy Economics, Elsevier, vol. 68(C), pages 327-339.
    10. Stefan Birr & Stanislav Volgushev & Tobias Kley & Holger Dette & Marc Hallin, 2017. "Quantile spectral analysis for locally stationary time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1619-1643, November.
    11. Gkillas, Konstantinos & Gupta, Rangan & Wohar, Mark E., 2018. "Volatility jumps: The role of geopolitical risks," Finance Research Letters, Elsevier, vol. 27(C), pages 247-258.
    12. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    13. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, Open Access Journal, vol. 6(3), pages 1-47, September.
    14. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2019. "Tail event driven networks of SIFIs," Journal of Econometrics, Elsevier, vol. 208(1), pages 282-298.
    15. Syed Jawad Hussain Shahzad & Jose Arreola-Hernandez & Stelios Bekiros & Mobeen Ur Rehman, 2018. "Risk transmitters and receivers in global currency markets," Post-Print hal-01814274, HAL.
    16. Bouri, Elie & Shahzad, Syed Jawad Hussain & Raza, Naveed & Roubaud, David, 2018. "Oil volatility and sovereign risk of BRICS," Energy Economics, Elsevier, vol. 70(C), pages 258-269.
    17. Uribe, Jorge M. & Guillen, Montserrat & Mosquera-López, Stephania, 2018. "Uncovering the nonlinear predictive causality between natural gas and electricity prices," Energy Economics, Elsevier, vol. 74(C), pages 904-916.
    18. Ando, Tomohiro & Bai, Jushan, 2018. "Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity," MPRA Paper 88765, University Library of Munich, Germany.
    19. Todorova, Neda, 2017. "The intraday directional predictability of large Australian stocks: A cross-quantilogram analysis," Economic Modelling, Elsevier, vol. 64(C), pages 221-230.
    20. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2018. "Oil Shocks and Volatility Jumps," Working Papers 201825, University of Pretoria, Department of Economics.
    21. Shen, Yifan & Shi, Xunpeng & Variam, Hari Malamakkavu Padinjare, 2018. "Risk transmission mechanism between energy markets: A VAR for VaR approach," Energy Economics, Elsevier, vol. 75(C), pages 377-388.
    22. Shahzad, Syed Jawad Hussain & Hernandez, Jose Areola & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Zakaria, Muhammad, 2018. "A global network topology of stock markets: Transmitters and receivers of spillover effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2136-2153.
    23. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    24. Shen, Yifan, 2018. "International risk transmission of stock market movements," Economic Modelling, Elsevier, vol. 69(C), pages 220-236.
    25. Bouri, Elie & Lien, Donald & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Directional predictability of implied volatility: From crude oil to developed and emerging stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 65-79.
    26. Heejoon Han, 2016. "Quantile Dependence between Stock Markets and its Application in Volatility Forecasting," Papers 1608.07193, arXiv.org.
    27. Tan Le & Franck Martin & Duc Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Working Papers hal-01806733, HAL.
    28. Montes-Rojas, Gabriel, 2017. "Reduced form vector directional quantiles," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 20-30.
    29. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    30. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.

  2. Pierre Perron & Tatsushi Oka, 2011. "Testing for Common Breaks in a Multiple Equations System," Boston University - Department of Economics - Working Papers Series WP2011-057, Boston University - Department of Economics.

    Cited by:

    1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    2. Manner, Hans & Blatt, Dominik & Candelon, Bertrand, 2014. "Detecting financial contagion in a multivariate system," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100411, Verein für Socialpolitik / German Economic Association.
    3. Eo, Yunjong & Morley, James, 2011. "Likelihood-Ratio-Based Confidence Sets for the Timing of Structural Breaks," Working Papers 2011-07, University of Sydney, School of Economics, revised Feb 2014.
    4. Ye Li & Pierre Perron, 2017. "Inference on locally ordered breaks in multiple regressions," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 289-353, March.
    5. Ye Li & Pierre Perron, 2013. "Inference Related to Locally Ordered and Common Breaks in a Multivariate System with Joined Segmented Trends," Boston University - Department of Economics - Working Papers Series 2013-010, Boston University - Department of Economics.
    6. Richard S. J. Tol & Francisco Estrada & Carlos Gay-García, 2012. "The persistence of shocks in GDP and the estimation of the potential economic costs of climate change," Working Paper Series 4312, Department of Economics, University of Sussex Business School.
    7. Blatt, Dominik & Candelon, Bertrand & Manner, Hans, 2015. "Detecting contagion in a multivariate time series system: An application to sovereign bond markets in Europe," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 1-13.

  3. Zhongjun Qu & Tatsushi Oka, 2010. "Estimating structural changes in regression quantiles," Boston University - Department of Economics - Working Papers Series WP2010-052, Boston University - Department of Economics.

    Cited by:

    1. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    2. Tolga Omay & Rangan Gupta & Giovanni Bonaccolto, 2015. "The US Real GNP is Trend-Stationary After All," Working Papers 201581, University of Pretoria, Department of Economics.
    3. Tillmann, Peter & Wolters, Maik Hendrik, 2012. "The changing dynamics of US inflation persistence: A quantile regression approach," IMFS Working Paper Series 60, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    4. Zhanfeng Wang & Wenxin Liu & Yuanyuan Lin, 2015. "A change-point problem in relative error-based regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 835-856, December.
    5. Wolters, Maik Hendrik, 2010. "Estimating Monetary Policy Reaction Functions Using Quantile Regressions," MPRA Paper 23857, University Library of Munich, Germany.
    6. Christian Bauer & Sebastian Weber, 2016. "The Efficiency of Monetary Policy when Guiding Inflation Expectations," Research Papers in Economics 2016-14, University of Trier, Department of Economics.
    7. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    8. Weichi Wu & Zhou Zhou, 2017. "Nonparametric Inference for Time-Varying Coefficient Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 98-109, January.
    9. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2017. "South Africa’s inflation persistence: a quantile regression framework," Economic Change and Restructuring, Springer, vol. 50(4), pages 367-386, November.
    10. Tatsushi Oka & Pierre Perron, 2018. "Testing for common breaks in a multiple equations system," Monash Econometrics and Business Statistics Working Papers 3/18, Monash University, Department of Econometrics and Business Statistics.
    11. Marilena Furno, 2012. "Tests for structural break in quantile regressions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 493-515, October.
    12. Sebastiano Manzan & Dawit Zerom, 2015. "Asymmetric Quantile Persistence and Predictability: the Case of US Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 297-318, April.
    13. Alexander Aue & Rex C. Y. Cheung & Thomas C. M. Lee & Ming Zhong, 2014. "Segmented Model Selection in Quantile Regression Using the Minimum Description Length Principle," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1241-1256, September.
    14. Yan-Yu Chiou & Mei-Yuan Chen & Jau-er Chen, 2017. "Nonparametric Regression with Multiple Thresholds: Estimation and Inference," Papers 1705.09418, arXiv.org, revised Feb 2018.
    15. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    16. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    17. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    18. Kuriyama Nina, 2016. "Testing cointegration in quantile regressions with an application to the term structure of interest rates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 107-121, April.
    19. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    20. Sahbi FARHANI, 2012. "Tests of Parameters Instability: Theoretical Study and Empirical Analysis on Two Types of Models (ARMA Model and Market Model)," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 246-266.
    21. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2015. "The Changing Dynamics of South Africa's Inflation Persistence: Evidence from a Quantile Regression Framework," Working Papers 201563, University of Pretoria, Department of Economics.
    22. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.
    23. Yu, Ping, 2015. "Adaptive estimation of the threshold point in threshold regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 83-100.
    24. Zhou, Mi & Wang, Huixia Judy & Tang, Yanlin, 2015. "Sequential change point detection in linear quantile regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 98-103.
    25. Uribe, Jorge M. & Chuliá, Helena & Guillén, Montserrat, 2017. "Uncertainty, systemic shocks and the global banking sector: Has the crisis modified their relationship?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 52-68.
    26. Jean-Paul Chavas & Salvatore Falco, 2017. "Resilience, Weather and Dynamic Adjustments in Agroecosystems: The Case of Wheat Yield in England," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(2), pages 297-320, June.
    27. Wen-Yi Chen & Tsangyao Chang & Yu-Hui Lin, 2018. "Investigating the Persistence of Suicide in the United States: Evidence from the Quantile Unit Root Test," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 813-833, January.

  4. Tatsushi Oka, 2004. "Juvenile Crime and Punishment: Evidence from Japan," Discussion Papers in Economics and Business 04-16, Osaka University, Graduate School of Economics.

    Cited by:

    1. Ignacio Munyo, 2015. "The Juvenile Crime Dilemma," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(2), pages 201-211, April.
    2. Cristiano M. Costa & Luciana D. Costa & Renata C. Gomes, 2015. "Family member incarceration and delinquent behaviour in the classroom: evidence from Brazil," Applied Economics Letters, Taylor & Francis Journals, vol. 22(5), pages 411-415, March.

Articles

  1. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.

    Cited by:

    1. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    2. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    3. Brantly Callaway & Tong Li, 2017. "Quantile Treatment Effects in Difference in Differences Models with Panel Data," DETU Working Papers 1701, Department of Economics, Temple University.

  2. Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
    See citations under working paper version above.
  3. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    See citations under working paper version above.
  4. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.

    Cited by:

    1. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    2. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    3. Arie Beresteanu, 2016. "Quantile Regression with Interval Data," Working Paper 5991, Department of Economics, University of Pittsburgh.

  5. Dukpa Kim & Tatsushi Oka, 2014. "Divorce Law Reforms And Divorce Rates In The Usa: An Interactive Fixed‐Effects Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 231-245, March.

    Cited by:

    1. Gobillon, Laurent & Magnac, Thierry, 2013. "Regional Policy Evaluation:Interactive Fixed Effects and Synthetic Controls," TSE Working Papers 13-419, Toulouse School of Economics (TSE).
    2. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2017. "Matrix Completion Methods for Causal Panel Data Models," Papers 1710.10251, arXiv.org, revised Sep 2018.
    3. Hyungsik Roger Moon & Martin Weidner, 2013. "Linear regression for panel with unknown number of factors as interactive fixed effects," CeMMAP working papers CWP49/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Shi, Wei & Lee, Lung-fei, 2018. "A spatial panel data model with time varying endogenous weights matrices and common factors," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 6-34.
    5. Totty, Evan, 2015. "The Effect of Minimum Wages on Employment: A Factor Model Approach," Institute for Research on Labor and Employment, Working Paper Series qt2b26n60q, Institute of Industrial Relations, UC Berkeley.
    6. Chigavazira, Abraham & Fisher, Hayley & Robinson, Tim & Zhu, Anna, 2019. "The Consequences of Extending Equitable Property Division Divorce Laws to Cohabitants," Working Papers 2019-02, University of Sydney, School of Economics.
    7. Wei Shi & Lung-fei Lee, 2018. "The effects of gun control on crimes: a spatial interactive fixed effects approach," Empirical Economics, Springer, vol. 55(1), pages 233-263, August.
    8. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2017. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Papers 1712.09089, arXiv.org, revised Dec 2018.
    9. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "Inference on average treatment effects in aggregate panel data settings," Papers 1812.10820, arXiv.org.
    10. Bai, Jushan & Liao, Yuan, 2017. "Inferences in panel data with interactive effects using large covariance matrices," Journal of Econometrics, Elsevier, vol. 200(1), pages 59-78.

  6. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
    See citations under working paper version above.
  7. Tatsushi Oka, 2009. "Juvenile crime and punishment: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 41(24), pages 3103-3115.
    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 6 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 (4) 2014-03-30 2017-07-09 2017-08-13 2018-02-05
  2. NEP-ETS: Econometric Time Series (4) 2014-03-30 2017-07-09 2018-02-05 2018-02-26
  3. NEP-CMP: Computational Economics (1) 2017-08-13
  4. NEP-ENV: Environmental Economics (1) 2018-06-11
  5. NEP-RMG: Risk Management (1) 2014-03-30
  6. NEP-SEA: South East Asia (1) 2017-07-09

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