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)

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. Dukpa Kim & Tatsushi Oka & Francisco Estrada & Pierre Perron, 2017. "Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures," Boston University - Department of Economics - Working Papers Series WP2017-003, Boston University - Department of Economics.
   • 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.

   Cited by:

   1. Tatsushi Oka & Pierre Perron, 2016. "Testing for Common Breaks in a Multiple Equations System," Papers 1606.00092, arXiv.org, revised Jan 2018.
      • Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
      • 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.
      • 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.

2. 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.
   • 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.
   • Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.

   Cited by:

   1. 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.
   2. Stefan Birr & Stanislav Volgushev & Tobias Kley & Holger Dette & Marc Hallin, 2015. "Quantile Spectral Analysis for Locally Stationary Time Series," Working Papers ECARES ECARES 2015-27, ULB -- Universite Libre de Bruxelles.
      • 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.
      • Stefan Skowronek & Stanislav Volgushev & Tobias Kley & Holger Dette & Marc Hallin, 2014. "Quantile Spectral Analysis for Locally Stationary Time Series," Working Papers ECARES ecares 2014-24, ULB -- Universite Libre de Bruxelles.
   3. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
      • Lee, JiHyung, 2015. "Predictive quantile regression with persistent covariates: IVX-QR approach," MPRA Paper 65150, University Library of Munich, Germany.
   4. 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.
   5. 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.
      • Baumöhl, Eduard & Lyócsa, Štefan, 2017. "Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis," MPRA Paper 76915, University Library of Munich, Germany.
   6. Daniel Danau, 2018. "Prudence and preference for flexibility gain," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 2018-05, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
   7. Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle & Yarema Okhrin, 2017. "Tail event driven networks of SIFIs," SFB 649 Discussion Papers SFB649DP2017-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
   8. 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.
      • Helena Chuliá & Montserrat Guillén & Jorge M. Uribe, 2015. "“Spillovers From the United States to Latin American and G7 Stock Markets: a VAR Quantile Analysis”," IREA Working Papers 201525, University of Barcelona, Research Institute of Applied Economics, revised Oct 2015.
   9. Todorova, Neda, 2017. "The intraday directional predictability of large Australian stocks: A cross-quantilogram analysis," Economic Modelling, Elsevier, vol. 64(C), pages 221-230.
   10. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2018. "Oil Shocks and Volatility Jumps," Working Papers 201825, University of Pretoria, Department of Economics.
   11. 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.
   12. Bekiros, Stelios & Shahzad, Syed Jawad Hussain & Arreola-Hernandez, Jose & Ur Rehman, Mobeen, 2018. "Directional predictability and time-varying spillovers between stock markets and economic cycles," Economic Modelling, Elsevier, vol. 69(C), pages 301-312.
   13. 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.
   14. Shen, Yifan, 2018. "International risk transmission of stock market movements," Economic Modelling, Elsevier, vol. 69(C), pages 220-236.
   15. Heejoon Han, 2016. "Quantile Dependence between Stock Markets and its Application in Volatility Forecasting," Papers 1608.07193, arXiv.org.
   16. Tan Le & Franck Martin & Duc Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Working Papers hal-01806733, HAL.
   17. Montes-Rojas, Gabriel, 2017. "Reduced form vector directional quantiles," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 20-30.
   18. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
   19. Tan T. M. Le & Franck Martin & Duc K. Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 2018-04, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
   20. 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.

3. 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.
   • Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
   • Tatsushi Oka & Pierre Perron, 2016. "Testing for Common Breaks in a Multiple Equations System," Papers 1606.00092, arXiv.org, revised Jan 2018.
   • 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.

   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. 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.
   3. 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.
   4. 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.
      • Yunjong Eo & James Morley, 2015. "Likelihood‐ratio‐based confidence sets for the timing of structural breaks," Quantitative Economics, Econometric Society, vol. 6(2), pages 463-497, July.
   5. 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.
   6. Ye Li & Pierre Perron, 2012. "Inference on Locally Ordered Breaks in Multiple Regressions," Boston University - Department of Economics - Working Papers Series wp2015-013, Boston University - Department of Economics, revised 02 Feb 2015.
      • 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.
   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.

4. 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.
   • Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.

   Cited by:

   1. 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.
      • Christina Christou & Rangan Gupta & Wendy Nyakabawo & Mark E. Wohar, 2017. "Do House Prices Hedge Inflation in the US? A Quantile Cointegration Approach," Working Papers 201707, University of Pretoria, Department of Economics.
   2. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
   3. Tatsushi Oka & Pierre Perron, 2016. "Testing for Common Breaks in a Multiple Equations System," Papers 1606.00092, arXiv.org, revised Jan 2018.
      • Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
      • 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.
      • 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.
   4. 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.
   5. 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.
      • Tolga Omay & Rangan Gupta & Giovanni Bonaccolto, 2017. "The US real GNP is trend-stationary after all," Applied Economics Letters, Taylor & Francis Journals, vol. 24(8), pages 510-514, May.
   6. 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.
   7. 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).
      • Tillmann, Peter & Wolters, Maik H., 2014. "The changing dynamics of US inflation persistence: A quantile regression approach," Economics Working Papers 2014-09, Christian-Albrechts-University of Kiel, Department of Economics.
      • Wolters Maik H. & Tillmann Peter, 2015. "The changing dynamics of US inflation persistence: a quantile regression approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 161-182, April.
      • Tillmann, Peter & Wolters, Maik H., 2014. "The changing dynamics of US inflation persistence: A quantile regression approach," Kiel Working Papers 1951, Kiel Institute for the World Economy (IfW).
      • Peter Tillmann & Maik H. Wolters, 2012. "The changing dynamics of US inflation persistence: a quantile regression approach," MAGKS Papers on Economics 201206, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
   8. 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.
   9. 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.
   10. Wolters, Maik H., 2012. "Estimating monetary policy reaction functions using quantile regressions," Journal of Macroeconomics, Elsevier, vol. 34(2), pages 342-361.
       • Wolters, Maik Hendrik, 2010. "Estimating Monetary Policy Reaction Functions Using Quantile Regressions," MPRA Paper 23857, University Library of Munich, Germany.
   11. 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.
   12. 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.
   13. 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.
   14. 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.
       • Qu, Zhongjun & Yoon, Jungmo, 2015. "Nonparametric estimation and inference on conditional quantile processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 1-19.
   15. Yu, Ping, 2015. "Adaptive estimation of the threshold point in threshold regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 83-100.
   16. 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.
   17. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
   18. 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.
   19. 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.
   20. 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.
   21. 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.
   22. 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.
   23. 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.
   24. 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.
   25. 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.

5. Tatsushi Oka, 2004. "Juvenile Crime and Punishment: Evidence from Japan," Discussion Papers in Economics and Business 04-16, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
   • Tatsushi Oka, 2009. "Juvenile crime and punishment: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 41(24), pages 3103-3115.

   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.
      • Ignacio Munyo, 2014. "Code and data files for "The Juvenile Crime Dilemma"," Computer Codes 13-7, Review of Economic Dynamics.

Articles

1. 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.
   • 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.
   • Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.
   See citations under working paper version above.
2. 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.
      • Kate Ho & Adam Rosen, 2015. "Partial identification in applied research: benefits and challenges," CeMMAP working papers CWP64/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
      • Ho, Katherine & Rosen, Adam M., 2015. "Partial Identification in Applied Research: Benefits and Challenges," CEPR Discussion Papers 10883, C.E.P.R. Discussion Papers.
   2. Arie Beresteanu, 2016. "Quantile Regression with Interval Data," Working Paper 5991, Department of Economics, University of Pittsburgh.
   3. Kate Ho & Adam Rosen, 2016. "Partial identification in applied research: benefits and challenges," CeMMAP working papers CWP45/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

3. 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).
      • Gobillon, Laurent & Magnac, Thierry, 2013. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," IZA Discussion Papers 7493, Institute for the Study of Labor (IZA).
      • Laurent Gobillon & Thierry Magnac, 2014. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Control," CESifo Working Paper Series 5077, CESifo Group Munich.
      • Laurent Gobillon & Thierry Magnac, 2013. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," PSE - Labex "OSE-Ouvrir la Science Economique" halshs-00849071, HAL.
      • Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," Post-Print halshs-01509743, HAL.
      • Gobillon, Laurent & Magnac, Thierry, 2013. "Regional Policy Evaluation:Interactive Fixed Effects and Synthetic Controls," IDEI Working Papers 786, Institut d'Économie Industrielle (IDEI), Toulouse.
      • Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
      • Gobillon, Laurent & Magnac, Thierry, 2014. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," CEPR Discussion Papers 10253, C.E.P.R. Discussion Papers.
   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.
   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.
      • Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
      • Hyungsik Roger Moon & Martin Weidner, 2014. "Linear regression for panel with unknown number of factors as interactive fixed effects," CeMMAP working papers CWP35/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
   4. Evan Totty, 2014. "The Effect of Minimum Wages on Employment: A Factor Model Approach," Purdue University Economics Working Papers 1278, Purdue University, Department of Economics.
      • 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.
      • Evan Totty, 2017. "The Effect Of Minimum Wages On Employment: A Factor Model Approach," Economic Inquiry, Western Economic Association International, vol. 55(4), pages 1712-1737, October.
   5. 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 Apr 2018.
   6. 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.

4. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
   • 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.
   See citations under working paper version above.
5. Tatsushi Oka, 2009. "Juvenile crime and punishment: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 41(24), pages 3103-3115.
   • Tatsushi Oka, 2004. "Juvenile Crime and Punishment: Evidence from Japan," Discussion Papers in Economics and Business 04-16, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
   See citations under working paper version above.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 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 (3) 2014-03-30 2017-07-09 2018-02-05
2. NEP-ETS: Econometric Time Series (3) 2014-03-30 2017-07-09 2018-02-05
3. NEP-RMG: Risk Management (1) 2014-03-30
4. NEP-SEA: South East Asia (1) 2017-07-09

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