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Hisashi Tanizaki

Personal Details

First Name:Hisashi
Middle Name:
Last Name:Tanizaki
Suffix:
RePEc Short-ID:pta865
[This author has chosen not to make the email address public]
http://www2.econ.osaka-u.ac.jp/~tanizaki/

Affiliation

Graduate School of Economics
Osaka University

Osaka, Japan
http://www.econ.osaka-u.ac.jp/
RePEc:edi:feosujp (more details at EDIRC)

Research output

as
Jump to: Articles

Articles

  1. Donglian Ma & Hisashi Tanizaki, 2019. "On the day-of-the-week effects of Bitcoin markets: international evidence," China Finance Review International, Emerald Group Publishing Limited, vol. 9(4), pages 455-478, July.
  2. Ma, Donglian & Tanizaki, Hisashi, 2019. "The day-of-the-week effect on Bitcoin return and volatility," Research in International Business and Finance, Elsevier, vol. 49(C), pages 127-136.
  3. Ken-ichi Mizobuchi & Hisashi Tanizaki, 2014. "On estimation of almost ideal demand system using moving blocks bootstrap and pairs bootstrap methods," Empirical Economics, Springer, vol. 47(4), pages 1221-1250, December.
  4. Hisashi Tanizaki & Shigeyuki Hamori, 2009. "Volatility transmission between Japan, UK and USA in daily stock returns," Empirical Economics, Springer, vol. 36(1), pages 27-54, February.
  5. Hisashi Tanizaki, 2008. "A Simple Gamma Random Number Generator for Arbitrary Shape Parameters," Economics Bulletin, AccessEcon, vol. 3(7), pages 1-10.
  6. Hisashi Tanizaki & Shigeyuki Hamori & Yoichi Matsubayashi, 2006. "On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods," Statistical Papers, Springer, vol. 47(1), pages 109-124, January.
  7. Geweke, John & Tanizaki, Hisashi, 2001. "Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 151-170, August.
  8. Hisashi Tanizaki, 2001. "Nonlinear and Non-Gaussian State Space Modeling Using Sampling Techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 63-81, March.
  9. Tanizaki, Hisashi, 2000. "Bias correction of OLSE in the regression model with lagged dependent variables," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 495-511, October.
  10. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290.
  11. Tanizaki, Hisashi, 1997. "Nonlinear and nonnormal filters using Monte Carlo methods," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 417-439, September.
  12. Hisashi Tanizaki, 1997. "Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(5), pages 603-632.
  13. Tanizaki, Hisashi & Mariano, Roberto S, 1994. "Prediction, Filtering and Smoothing in Non-linear and Non-normal Cases Using Monte Carlo Integration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(2), pages 163-179, April-Jun.
  14. Tanizaki, Hisashi, 1993. "Kalman Filter Model with Qualitative Dependent Variables," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 747-752, November.
  15. Tanizaki, Hisashi, 1989. "The Kalman filter model under the assumption of the first-order autoregressive process in the disturbance terms," Economics Letters, Elsevier, vol. 31(2), pages 145-149, December.

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Tanizaki, Hisashi & Mariano, Roberto S, 1994. "Prediction, Filtering and Smoothing in Non-linear and Non-normal Cases Using Monte Carlo Integration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(2), pages 163-179, April-Jun.

    Mentioned in:

    1. Prediction, filtering and smoothing in non-linear and non-normal cases using Monte Carlo integration (Journal of Applied Econometrics 1994) in ReplicationWiki ()

Articles

  1. Donglian Ma & Hisashi Tanizaki, 2019. "On the day-of-the-week effects of Bitcoin markets: international evidence," China Finance Review International, Emerald Group Publishing Limited, vol. 9(4), pages 455-478, July.

    Cited by:

    1. Li, Yi & Urquhart, Andrew & Wang, Pengfei & Zhang, Wei, 2021. "MAX momentum in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 77(C).
    2. Weige Huang & Xiang Gao, 2023. "Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies," SAGE Open, , vol. 13(1), pages 21582440231, January.
    3. Zhang, Wei & Li, Yi & Xiong, Xiong & Wang, Pengfei, 2021. "Downside risk and the cross-section of cryptocurrency returns," Journal of Banking & Finance, Elsevier, vol. 133(C).

  2. Ma, Donglian & Tanizaki, Hisashi, 2019. "The day-of-the-week effect on Bitcoin return and volatility," Research in International Business and Finance, Elsevier, vol. 49(C), pages 127-136.

    Cited by:

    1. Kazeem Abimbola Sanusi & Zandri Dickason-Koekemoer, 2022. "Cryptocurrency Returns, Cybercrime and Stock Market Volatility: GAS and Regime Switching Approaches," International Journal of Economics and Financial Issues, Econjournals, vol. 12(6), pages 52-64, November.
    2. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Nuray Tosunoğlu & Hilal Abacı & Gizem Ateş & Neslihan Saygılı Akkaya, 2023. "Artificial neural network analysis of the day of the week anomaly in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    4. Yutaka Kurihara & Akio Fukushima & Shinichiro Maeda, 2020. "Can Bitcoin’S Price Be A Predictor Of Stock Prices?," Noble International Journal of Economics and Financial Research, Noble Academic Publsiher, vol. 5(4), pages 50-55, April.
    5. Wang, Pengfei & Li, Xiao & Shen, Dehua & Zhang, Wei, 2020. "How does economic policy uncertainty affect the bitcoin market?," Research in International Business and Finance, Elsevier, vol. 53(C).
    6. Bergsli, Lykke Øverland & Lind, Andrea Falk & Molnár, Peter & Polasik, Michał, 2022. "Forecasting volatility of Bitcoin," Research in International Business and Finance, Elsevier, vol. 59(C).
    7. Guglielmo Maria Caporale & Alex Plastun & Viktor Oliinyk, 2019. "Bitcoin fluctuations and the frequency of price overreactions," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 109-131, June.
    8. López-Cabarcos, M. Ángeles & Pérez-Pico, Ada M. & Piñeiro-Chousa, Juan & Šević, Aleksandar, 2021. "Bitcoin volatility, stock market and investor sentiment. Are they connected?," Finance Research Letters, Elsevier, vol. 38(C).
    9. Kyriazis, Nikolaos & Papadamou, Stephanos & Corbet, Shaen, 2020. "A systematic review of the bubble dynamics of cryptocurrency prices," Research in International Business and Finance, Elsevier, vol. 54(C).
    10. Aslanidis, Nektarios & Fernández Bariviera, Aurelio & Savva, Christos S., 2020. "Weekly dynamic conditional correlations among cryptocurrencies and traditional assets," Working Papers 2072/417680, Universitat Rovira i Virgili, Department of Economics.
    11. Kinateder, Harald & Papavassiliou, Vassilios G., 2021. "Calendar effects in Bitcoin returns and volatility," Finance Research Letters, Elsevier, vol. 38(C).
    12. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    13. Fang, Tong & Su, Zhi & Yin, Libo, 2020. "Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility," International Review of Financial Analysis, Elsevier, vol. 71(C).
    14. Roberto Joaquín Santillán Salgado & Alejandro Fonseca Ramírez & Luis Nelson Romero, 2019. "The "day-of-the-week" effects in the exchange rate of Latin American currencies," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(PNEA), pages 485-507, Agosto 20.
    15. Li, Wenhui & Zhu, Qi & Wen, Fenghua & Nor, Normaziah Mohd, 2022. "The evolution of day-of-the-week and the implications in crude oil market," Energy Economics, Elsevier, vol. 106(C).
    16. Qadan, Mahmoud & Aharon, David Y. & Eichel, Ron, 2022. "Seasonal and Calendar Effects and the Price Efficiency of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 46(PA).
    17. Noriyuki Kunimoto & Kazuhiko Kakamu, 2021. "Is Bitcoin really a currency? A viewpoint of a stochastic volatility model," Papers 2111.15351, arXiv.org.
    18. Dulani Jayasuriya Daluwathumullagamage & Alexandra Sims, 2021. "Fantastic Beasts: Blockchain Based Banking," JRFM, MDPI, vol. 14(4), pages 1-43, April.

  3. Ken-ichi Mizobuchi & Hisashi Tanizaki, 2014. "On estimation of almost ideal demand system using moving blocks bootstrap and pairs bootstrap methods," Empirical Economics, Springer, vol. 47(4), pages 1221-1250, December.

    Cited by:

    1. Marilena FURNO & Francesco CARACCIOLO, 2017. "Beyond the mean: Estimating consumer demand systems in the tails," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(10), pages 449-460.

  4. Hisashi Tanizaki & Shigeyuki Hamori, 2009. "Volatility transmission between Japan, UK and USA in daily stock returns," Empirical Economics, Springer, vol. 36(1), pages 27-54, February.

    Cited by:

    1. Viorica CHIRILA & Ciprian CHIRILA, 2018. "Effects of US Monetary Policy on Eastern European Financial Markets," CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10(2), pages 149-166, August.
    2. Ekin Tokat & Hakkı Arda Tokat, 2010. "Shock and Volatility Transmission in the Futures and Spot Markets: Evidence from Turkish Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(4), pages 92-104, January.
    3. Borjigin, Sumuya & Gao, Ting & Sun, Yafei & An, Biao, 2020. "For evil news rides fast, while good news baits later?—A network based analysis in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    4. Kunlin Hsieh & Yuching Hsieh & Shigeyuki Hamori, 2010. "The Interdependence of Taiwanese and Japanese Stock Prices," Economics Bulletin, AccessEcon, vol. 30(1), pages 879-892.
    5. Athanasios Koulakiotis & Katerina Lyroudi & Nikos Thomaidis & Nicholas Papasyriopoulos, 2010. "The impact of cross‐listings on the UK and the German stock markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 27(1), pages 4-18, March.
    6. Koulakiotis, Athanasios & Kartalis, Nikos & Lyroudi, Katerina & Papasyriopoulos, Nicholas, 2012. "Asymmetric and threshold effects on comovements among Germanic cross-listed equities," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 327-342.
    7. Abdul Wahid & Muhammad Zubair Mumtaz, 2018. "The Paradigm Shift in the Pakistan Stock Exchange’s Financial Integration Post-FTA and CPEC," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 23(1), pages 21-50, Jan-June.
    8. Sinha, Pankaj & Sinha, Gyanesh, 2010. "Volatility Spillover in India, USA and Japan Investigation of Recession Effects," MPRA Paper 47190, University Library of Munich, Germany, revised 17 May 2013.
    9. Galin Todorov & Prasad Bidarkota, 2013. "On international financial spillovers to frontier markets," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 5(4), pages 433-452.
    10. Withanage, Yeshan & Jayasinghe, Prabhath, 2017. "Volatility Spillovers between South Asian Stock Markets: Evidence from Sri Lanka, India and Pakistan," MPRA Paper 82782, University Library of Munich, Germany, revised Nov 2017.
    11. Sun, Qingru & Gao, Xiangyun & An, Haizhong & Guo, Sui & Liu, Xueyong & Wang, Ze, 2021. "Which time-frequency domain dominates spillover in the Chinese energy stock market?," International Review of Financial Analysis, Elsevier, vol. 73(C).
    12. Liu, Xueyong & An, Haizhong & Li, Huajiao & Chen, Zhihua & Feng, Sida & Wen, Shaobo, 2017. "Features of spillover networks in international financial markets: Evidence from the G20 countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 265-278.
    13. Zhu, Hui-Ming & Li, ZhaoLai & You, WanHai & Zeng, Zhaofa, 2015. "Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 142-153.
    14. A. Khalifa & S. Hammoudeh & E. Otranto & S. Ramchander, 2012. "Volatility Transmission across Currency, Commodity and Equity Markets under Multi-Chain Regime Switching: Implications for Hedging and Portfolio Allocation," Working Paper CRENoS 201214, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

  5. Hisashi Tanizaki, 2008. "A Simple Gamma Random Number Generator for Arbitrary Shape Parameters," Economics Bulletin, AccessEcon, vol. 3(7), pages 1-10.

    Cited by:

    1. Devroye, Luc, 2021. "Random variate generation for the truncated negative gamma distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 51-56.
    2. Chuanhai Liu & Ryan Martin & Nick Syring, 2017. "Efficient simulation from a gamma distribution with small shape parameter," Computational Statistics, Springer, vol. 32(4), pages 1767-1775, December.
    3. Joshi, Mark S. & Zhu, Dan, 2016. "An exact method for the sensitivity analysis of systems simulated by rejection techniques," European Journal of Operational Research, Elsevier, vol. 254(3), pages 875-888.

  6. Hisashi Tanizaki & Shigeyuki Hamori & Yoichi Matsubayashi, 2006. "On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods," Statistical Papers, Springer, vol. 47(1), pages 109-124, January.

    Cited by:

    1. Hevia, Constantino, 2012. "Using pooled information and bootstrap methods to assess debt sustainability in low income countries," Policy Research Working Paper Series 5978, The World Bank.
    2. Wang, Shaoping & Li, Yanglin & Wen, Kuangyu, 2021. "Recursive adjusted unit root tests under non-stationary volatility," Economics Letters, Elsevier, vol. 205(C).
    3. Christopher Withers & Saralees Nadarajah, 2013. "Calibration with low bias," Statistical Papers, Springer, vol. 54(2), pages 371-379, May.
    4. Sergio Alvarez-Andrade & Salim Bouzebda, 2014. "Asymptotic results for hybrids of empirical and partial sums processes," Statistical Papers, Springer, vol. 55(4), pages 1121-1143, November.
    5. Gonçalves Mazzeu, Joao Henrique & Ruiz Ortega, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.

  7. Geweke, John & Tanizaki, Hisashi, 2001. "Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 151-170, August.

    Cited by:

    1. Linnea Polgreen & Pedro Silos, 2005. "Capital-skill complementarity and inequality: a sensitivity analysis," FRB Atlanta Working Paper 2005-20, Federal Reserve Bank of Atlanta.
    2. Fatemeh Hassanzadeh, 2021. "A smoothing spline model for multimodal and skewed circular responses: Applications in meteorology and oceanography," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    3. Fernando Linardi & Cees Diks & Marco van der Leij & Iuri Lazier, 2018. "Dynamic Interbank Network Analysis Using Latent Space Models," Working Papers Series 487, Central Bank of Brazil, Research Department.
    4. Christian Hotz-Behofsits & Florian Huber & Thomas O. Zorner, 2018. "Predicting crypto-currencies using sparse non-Gaussian state space models," Papers 1801.06373, arXiv.org, revised Feb 2018.
    5. Wang, Guiming, 2007. "On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models," Ecological Modelling, Elsevier, vol. 200(3), pages 521-528.
    6. Fabio Canova & Fernando J. Pérez Forero, 2012. "Estimating Overidentified, Nonrecursive Time-Varying Coefficients Structural VARs," Working Papers 637, Barcelona School of Economics.
    7. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    8. Liu Xiangdong & Li Xianglong & Zheng Shaozhi & Qian Hangyong, 2020. "PMCMC for Term Structure of Interest Rates under Markov Regime Switching and Jumps," Journal of Systems Science and Information, De Gruyter, vol. 8(2), pages 159-169, April.
    9. Veyssiere, Luc Pierre, 2009. "A three essays dissertation on agricultural and environmental microeconomics," ISU General Staff Papers 200901010800001958, Iowa State University, Department of Economics.
    10. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient Matrix Approach for Classical Inference in State Space Models," EMF Research Papers 19, Economic Modelling and Forecasting Group.
    11. Almeida, Carlos & Czado, Claudia, 2012. "Efficient Bayesian inference for stochastic time-varying copula models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1511-1527.
    12. Helio Migon & Alexandra Schmidt & Romy Ravines & João Pereira, 2013. "An efficient sampling scheme for dynamic generalized models," Computational Statistics, Springer, vol. 28(5), pages 2267-2293, October.
    13. Na Xia & Qinan Zhi & Menghua He & Yunqing Hong & Huazheng Du, 2020. "A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    14. Gonzalez-Astudillo, Manuel, 2013. "Monetary-Fiscal Policy Interactions: Interdependent Policy Rule Coefficients," MPRA Paper 50040, University Library of Munich, Germany.
    15. Mengheng Li & Marcel Scharth, 2022. "Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 285-301, January.
    16. Moon Jung Choi & Geun-Young Kim & Joo Yong Lee, 2015. "An Analysis of Trade Patterns in East Asia and the Effects of the Real Exchange Rate Movements," Working Papers 2015-29, Economic Research Institute, Bank of Korea.
    17. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
    18. Su, Zhenming & Peterman, Randall M., 2012. "Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error," Ecological Modelling, Elsevier, vol. 224(1), pages 76-89.
    19. Koenker, Roger & Yoon, Jungmo, 2009. "Parametric links for binary choice models: A Fisherian-Bayesian colloquy," Journal of Econometrics, Elsevier, vol. 152(2), pages 120-130, October.
    20. Gao, Rui & Li, Yaqiong & Lin, Lisha, 2019. "Bayesian statistical inference for European options with stock liquidity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 312-322.
    21. Hasegawa, Takanori & Niida, Atsushi & Mori, Tomoya & Shimamura, Teppei & Yamaguchi, Rui & Miyano, Satoru & Akutsu, Tatsuya & Imoto, Seiya, 2016. "A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 63-74.

  8. Hisashi Tanizaki, 2001. "Nonlinear and Non-Gaussian State Space Modeling Using Sampling Techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 63-81, March.

    Cited by:

    1. Yasuhiro Omori & Toshiaki Watanabe, 2007. "Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-space Model with Correlated Errors," CIRJE F-Series CIRJE-F-508, CIRJE, Faculty of Economics, University of Tokyo.
    2. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss," CARF F-Series CARF-F-094, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

  9. Tanizaki, Hisashi, 2000. "Bias correction of OLSE in the regression model with lagged dependent variables," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 495-511, October.

    Cited by:

    1. Steve Lawford & Michalis P. Stamatogiannis, 2008. "The Finite-Sample E ects of VAR Dimensions on OLS Bias, OLS Variance, and Minimum MSE Estimators," Working Paper series 13_08, Rimini Centre for Economic Analysis.
    2. Broda, Simon & Paolella, Marc S. & Carstensen, Kai, 2007. "Bias-adjusted estimation in the ARX(1) model," Munich Reprints in Economics 19992, University of Munich, Department of Economics.
    3. Hisashi Tanizaki & Shigeyuki Hamori & Yoichi Matsubayashi, 2006. "On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods," Statistical Papers, Springer, vol. 47(1), pages 109-124, January.
    4. Narangajavana, Yeamduan & Garrigos-Simon, Fernando J. & García, Javier Sanchez & Forgas-Coll, Santiago, 2014. "Prices, prices and prices: A study in the airline sector," Tourism Management, Elsevier, vol. 41(C), pages 28-42.
    5. Kiviet, J.F. & Phillips, G.D.A., 1999. "Higher-Order Asymptotic Expansions of the Least-Squares Estimation Bias in First-Order Dynamic Regression Models," Discussion Papers 9903, University of Exeter, Department of Economics.
    6. Octavio Fernández-Amador & Doris A. Oberdabernig & Patrick Tomberger, 2019. "Testing for Convergence in Carbon Dioxide Emissions Using a Bayesian Robust Structural Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1265-1286, August.
    7. Michael D. Bauer & Glenn D. Rudebusch & Jing Cynthia Wu, 2011. "Unbiased estimate of dynamic term structure models," Working Paper Series 2011-12, Federal Reserve Bank of San Francisco.

  10. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290.

    Cited by:

    1. Florian Heiss, 2008. "Sequential numerical integration in nonlinear state space models for microeconometric panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 373-389.
    2. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    3. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
    4. Motta, Anderson C. O. & Hotta, Luiz K., 2003. "Exact Maximum Likelihood and Bayesian Estimation of the Stochastic Volatility Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(2), November.
    5. Cui, Qiurong & Xu, Yuqing & Zhang, Zhengjun & Chan, Vincent, 2021. "Max-linear regression models with regularization," Journal of Econometrics, Elsevier, vol. 222(1), pages 579-600.
    6. Levent Ozbek & Umit Ozlale & Fikri Ozturk, 2003. "Employing Extended Kalman Filter in a Simple Macroeconomic Model," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 3(1), pages 53-65.
    7. Tokovenko, Oleksiy & Gunter, Lewell F., 2008. "Quarterly Storage Model of U.S. Cotton Market: Estimation of the Basis under Rational Expectations," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6435, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. J. Huston McCulloch & Prasad V. Bidarkota, 2003. "Signal Extraction can Generate Volatility Clusters," Computing in Economics and Finance 2003 59, Society for Computational Economics.
    9. J. Huston McCulloch & Prasad V. Bidarkota, 2002. "Signal Extraction Can Generate Volatility Clusters From IID Shocks," Working Papers 02-04, Ohio State University, Department of Economics.
    10. Yasuhiro Omori & Toshiaki Watanabe, 2007. "Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-space Model with Correlated Errors," CIRJE F-Series CIRJE-F-508, CIRJE, Faculty of Economics, University of Tokyo.
    11. Pablo Marshall, 2000. "Difusion De Internet En Chile," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 3(2), pages 143-163.
    12. Cagnone, Silvia & Bartolucci, Francesco, 2013. "Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data," MPRA Paper 51037, University Library of Munich, Germany.
    13. Tomohiro Ando, 2008. "Measuring the baseline sales and the promotion effect for incense products: a Bayesian state-space modeling approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 763-780, December.
    14. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss," CARF F-Series CARF-F-094, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    15. Geweke, John & Tanizaki, Hisashi, 2001. "Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 151-170, August.
    16. F Cadini & D Avram & E Zio, 2010. "System state estimation by particle filtering for fault diagnosis and prognosis," Journal of Risk and Reliability, , vol. 224(3), pages 149-158, September.
    17. Cadini, F. & Zio, E. & Avram, D., 2009. "Model-based Monte Carlo state estimation for condition-based component replacement," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 752-758.
    18. Miller, J. Isaac & Park, Joon Y., 2005. "How They Interact to Generate Persistency in Memory," Working Papers 2005-01, Rice University, Department of Economics.
    19. Chiachío, Juan & Chiachío, Manuel & Sankararaman, Shankar & Saxena, Abhinav & Goebel, Kai, 2015. "Condition-based prediction of time-dependent reliability in composites," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 134-147.
    20. Siem Jan Koopman & Kai Ming Lee, 2005. "Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series," Tinbergen Institute Discussion Papers 05-081/4, Tinbergen Institute.
    21. Timothy Cogley, 2005. "Changing Beliefs and the Term Structure of Interest Rates: Cross-Equation Restrictions with Drifting Parameters," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 420-451, April.

  11. Tanizaki, Hisashi, 1997. "Nonlinear and nonnormal filters using Monte Carlo methods," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 417-439, September.

    Cited by:

    1. Cadini, F. & Zio, E. & Avram, D., 2009. "Model-based Monte Carlo state estimation for condition-based component replacement," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 752-758.

  12. Hisashi Tanizaki, 1997. "Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(5), pages 603-632.

    Cited by:

    1. Krüger, Jens & Chlaß, Nadine, 2007. "Small Sample Properties of the Wilcoxon Signed Rank Test with Discontinuous and Dependent Observations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 34399, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Citlali Calderon & Lorena Carrete & Jorge Vera-Martínez & María Esther Gloria-Quintero & María del Socorro Romero-Figueroa, 2021. "A Social Marketing Intervention to Improve Treatment Adherence in Patients with Type 1 Diabetes," IJERPH, MDPI, vol. 18(7), pages 1-14, March.
    3. Markus Neuh�user, 2015. "Combining the t test and Wilcoxon's rank-sum test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2769-2775, December.
    4. Serguei Rouzinov & André Berchtold, 2022. "Regression-Based Approach to Test Missing Data Mechanisms," Data, MDPI, vol. 7(2), pages 1-28, January.
    5. Kateřina Macháčová & Hana Vaňková & Iva Holmerová & Inna Čábelková & Ladislav Volicer, 2018. "Ratings of activities of daily living in nursing home residents: comparison of self- and proxy ratings with actual performance and the impact of cognitive status," European Journal of Ageing, Springer, vol. 15(4), pages 349-358, December.
    6. Kai Wang & Manikandan Narayanan & Hua Zhong & Martin Tompa & Eric E Schadt & Jun Zhu, 2009. "Meta-analysis of Inter-species Liver Co-expression Networks Elucidates Traits Associated with Common Human Diseases," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-16, December.

  13. Tanizaki, Hisashi & Mariano, Roberto S, 1994. "Prediction, Filtering and Smoothing in Non-linear and Non-normal Cases Using Monte Carlo Integration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(2), pages 163-179, April-Jun.

    Cited by:

    1. Florian Heiss, 2008. "Sequential numerical integration in nonlinear state space models for microeconometric panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 373-389.
    2. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(4), pages 1059-1087.
    3. Tanizaki, Hisashi, 1997. "Nonlinear and nonnormal filters using Monte Carlo methods," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 417-439, September.
    4. Motta, Anderson C. O. & Hotta, Luiz K., 2003. "Exact Maximum Likelihood and Bayesian Estimation of the Stochastic Volatility Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(2), November.
    5. Florian Heiss, 2006. "Nonlinear State-Space Models for Microeconometric Panel Data," Computing in Economics and Finance 2006 285, Society for Computational Economics.
    6. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290.
    7. Hermann Singer, 2003. "Simulated Maximum Likelihood in Nonlinear Continuous-Discrete State Space Models: Importance Sampling by Approximate Smoothing," Computational Statistics, Springer, vol. 18(1), pages 79-106, March.
    8. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    9. S. Boragan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 319-340, March.
    10. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2004. "Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach," PIER Working Paper Archive 04-001, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  14. Tanizaki, Hisashi, 1993. "Kalman Filter Model with Qualitative Dependent Variables," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 747-752, November.

    Cited by:

    1. Du, Rex Yuxing & Kamakura, Wagner A., 2015. "Improving the statistical performance of tracking studies based on repeated cross-sections with primary dynamic factor analysis," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 94-112.
    2. Tanizaki, Hisashi, 1997. "Nonlinear and nonnormal filters using Monte Carlo methods," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 417-439, September.
    3. Alvaro Montenegro, 2005. "Introducción al filtro Kalman," Documentos de Economía 2920, Universidad Javeriana - Bogotá.
    4. Naik, P. & Piersma, N., 2002. "Understanding the role of marketing communications in direct marketing," Econometric Institute Research Papers EI 2002-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Fukasawa, T. & Basawa, I. V., 2002. "Estimation for a class of generalized state-space time series models," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 459-473, December.
    6. Chung‐Hua Shen & David R. Hakes & Kenneth Brown, 1999. "Time‐Varying Response of Monetary Policy to Macroeconomic Conditions," Southern Economic Journal, John Wiley & Sons, vol. 65(3), pages 584-593, January.

  15. Tanizaki, Hisashi, 1989. "The Kalman filter model under the assumption of the first-order autoregressive process in the disturbance terms," Economics Letters, Elsevier, vol. 31(2), pages 145-149, December.

    Cited by:

    1. Olayungbo, D.O., 2019. "Effects of oil export revenue on economic growth in Nigeria: A time varying analysis of resource curse," Resources Policy, Elsevier, vol. 64(C).

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