Rutger-Jan Lange

Personal Details

First Name:	Rutger-Jan
Middle Name:
Last Name:	Lange
Suffix:
RePEc Short-ID:	pla919
	[This author has chosen not to make the email address public]

Affiliation

Econometrisch Instituut
Faculteit der Economische Wetenschappen
Erasmus Universiteit Rotterdam

Rotterdam, Netherlands
http://www.econometric-institute.org/
RePEc:edi:eieurnl (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. Rutger-Jan Lange & Bram van Os & Dick van Dijk, 2022. "Implicit score-driven filters for time-varying parameter models," Tinbergen Institute Discussion Papers 22-066/III, Tinbergen Institute, revised 21 Nov 2024.

   Cited by:

   1. Ramon de Punder & Timo Dimitriadis & Rutger-Jan Lange, 2024. "Kullback-Leibler-based characterizations of score-driven updates," Papers 2408.02391, arXiv.org, revised Sep 2024.
      • Ramon de Punder & Timo Dimitriadis & Rutger-Jan Lange, 2024. "Kullback-Leibler-based characterizations of score-driven updates," Tinbergen Institute Discussion Papers 24-051/III, Tinbergen Institute, revised 22 Oct 2024.

2. Teulings, Coen & Lange, Rutger-Jan, 2018. "The option value of vacant land and the optimal timing of city extensions," CEPR Discussion Papers 12847, C.E.P.R. Discussion Papers.
   • Rutger-Jan Lange & Coen Teulings, 2018. "The option value of vacant land and the optimal timing of city extensions," Tinbergen Institute Discussion Papers 18-033/III, Tinbergen Institute.

   Cited by:

   1. Murray, Cameron, 2020. "A housing supply absorption rate equation," OSF Preprints 7n8rj, Center for Open Science.
   2. Murray, Cameron, 2020. "Time is money: How landbanking constrains housing supply," OSF Preprints hym43, Center for Open Science.
      • Murray, Cameron K., 2020. "Time is money: How landbanking constrains housing supply," Journal of Housing Economics, Elsevier, vol. 49(C).
   3. Cameron K. Murray, 2022. "A Housing Supply Absorption Rate Equation," The Journal of Real Estate Finance and Economics, Springer, vol. 64(2), pages 228-246, February.

3. Michael Grubb & Jean-Francois Mercure & Pablo Salas & Rutger-Jan Lange & Ida Sognnaes, 2018. "Systems Innovation, Inertia and Pliability: A mathematical exploration with implications for climate change abatement," Working Papers EPRG 1808, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
   • Grubb, M. & Mercure, J. & Salas, P. & Lange, R., 2018. "Systems Innovation, Inertia and Pliability: A mathematical exploration with implications for climate change abatement," Cambridge Working Papers in Economics 1819, Faculty of Economics, University of Cambridge.

   Cited by:

4. Rutger-Jan Lange & Andre Lucas & Arjen H. Siegmann, 2016. "Score-Driven Systemic Risk Signaling for European Sovereign Bond Yields and CDS Spreads," Tinbergen Institute Discussion Papers 16-064/IV, Tinbergen Institute.

   Cited by:

   1. Bonga-Bonga, Lumengo & Manguzvane, Mathias Mandla, 2018. "Assessing the extent of contagion of sovereign credit risk among BRICS countries," MPRA Paper 89200, University Library of Munich, Germany.
      • Lumengo Bonga-Bonga & Mathias mandla Manguzvane, 2020. "Assessing the extent of contagion of sovereign credit risk among BRICS countries," Economics Bulletin, AccessEcon, vol. 40(2), pages 1017-1032.
   2. Hoang Nguyen & Audron.e Virbickait.e & M. Concepci'on Aus'in & Pedro Galeano, 2024. "Structured factor copulas for modeling the systemic risk of European and United States banks," Papers 2401.03443, arXiv.org.
   3. Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," International Journal of Forecasting, Elsevier, vol. 35(1), pages 25-44.
      • Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," Working Paper Series in Economics 123, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
   4. J. W. Muteba Mwamba & Mathias Manguzvane, 2020. "Contagion risk in african sovereign debt markets: A spatial econometrics approach," International Finance, Wiley Blackwell, vol. 23(3), pages 506-536, December.

5. Andrew Harvey & Rutger-Jan Lange, 2015. "Modeling the Interactions between Volatility and Returns," Cambridge Working Papers in Economics 1518, Faculty of Economics, University of Cambridge.

   Cited by:

   1. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
      • Andrew Harvey & Rutger-Jan Lange, 2015. "Volatility Modeling with a Generalized t-distribution," Cambridge Working Papers in Economics 1517, Faculty of Economics, University of Cambridge.
   2. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Maximum Likelihood Estimation for Score-Driven Models," Tinbergen Institute Discussion Papers 14-029/III, Tinbergen Institute, revised 23 Oct 2017.
      • Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
   3. Liu, Dehong & Gu, Hongmei & Lung, Peter, 2016. "The equity mispricing: Evidence from China's stock market," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 211-223.

6. Andrew Harvey & Rutger-Jan Lange, 2015. "Volatility Modeling with a Generalized t-distribution," Cambridge Working Papers in Economics 1517, Faculty of Economics, University of Cambridge.
   • Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.

   Cited by:

   1. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
      • Harvey, Andrew & Palumbo, Dario, 2023. "Score-driven models for realized volatility," Journal of Econometrics, Elsevier, vol. 237(2).
   2. Ayala, Astrid & Blazsek, Szabolcs, 2019. "Score-driven time series models with dynamic shape : an application to the Standard & Poor's 500 index," UC3M Working papers. Economics 28133, Universidad Carlos III de Madrid. Departamento de Economía.
   3. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Maximum Likelihood Estimation for Score-Driven Models," Tinbergen Institute Discussion Papers 14-029/III, Tinbergen Institute, revised 23 Oct 2017.
      • Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
   4. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
   5. Ayala, Astrid & Blazsek, Szabolcs, 2017. "Dynamic conditional score models with time-varying location, scale and shape parameters," UC3M Working papers. Economics 25043, Universidad Carlos III de Madrid. Departamento de Economía.
   6. Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Working Paper Series 2022-02, University of Trier, Research Group Quantitative Finance and Risk Analysis.
      • Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Research Papers in Economics 2022-02, University of Trier, Department of Economics.
      • Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2023. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," CESifo Working Paper Series 10366, CESifo.
   7. Umlandt, Dennis, 2023. "Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance," Journal of Econometrics, Elsevier, vol. 237(2).
   8. Harvey, Andrew & Ito, Ryoko, 2020. "Modeling time series when some observations are zero," Journal of Econometrics, Elsevier, vol. 214(1), pages 33-45.
   9. Karol Kielak & Robert Ślepaczuk, 2020. "Value-at-risk — the comparison of state-of-the-art models on various assets," Working Papers 2020-28, Faculty of Economic Sciences, University of Warsaw.
   10. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
   11. Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
   12. Giuseppe Buccheri & Stefano Grassi & Giorgio Vocalelli, 2021. "Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility," CEIS Research Paper 506, Tor Vergata University, CEIS, revised 08 Nov 2021.
   13. Yinhao Wu & Ping He, 2024. "The continuous-time limit of quasi score-driven volatility models," Papers 2409.14734, arXiv.org.
   14. Andrew Harvey & Rutger‐Jan Lange, 2018. "Modeling the Interactions between Volatility and Returns using EGARCH‐M," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 909-919, November.
   15. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
   16. Bram van Os, 2023. "Information-Theoretic Time-Varying Density Modeling," Tinbergen Institute Discussion Papers 23-037/III, Tinbergen Institute.
   17. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
   18. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
   19. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
   20. Higbee, Joshua D. & McDonald, James B., 2024. "A comparison of the GB2 and skewed generalized log-t distributions with an application in finance," Journal of Econometrics, Elsevier, vol. 240(2).
   21. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
   22. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
   23. Van Tran, Quang & Kukal, Jaromir, 2024. "Renyi entropy based design of heavy tailed distribution for return of financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
   24. Astrid Ayala & Szabolcs Blazsek, 2019. "Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(1), pages 65-92, March.
   25. Hafner, Christian M. & Wang, Linqi, 2023. "A dynamic conditional score model for the log correlation matrix," Journal of Econometrics, Elsevier, vol. 237(2).
   26. Astrid Ayala & Szabolcs Blazsek & Adrian Licht, 2022. "Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020," Empirical Economics, Springer, vol. 62(5), pages 2179-2203, May.
   27. Harvey, A. & Liao, Y., 2019. "Dynamic Tobit models," Cambridge Working Papers in Economics 1913, Faculty of Economics, University of Cambridge.
   28. Victor Korolev, 2023. "Analytic and Asymptotic Properties of the Generalized Student and Generalized Lomax Distributions," Mathematics, MDPI, vol. 11(13), pages 1-27, June.
   29. Stephen Thiele, 2020. "Modeling the conditional distribution of financial returns with asymmetric tails," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 46-60, January.
   30. Mazur Błażej & Pipień Mateusz, 2018. "Time-varying asymmetry and tail thickness in long series of daily financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-21, December.
   31. Harvey, A. & Palumbo, D., 2021. "Regime switching models for directional and linear observations," Cambridge Working Papers in Economics 2123, Faculty of Economics, University of Cambridge.
   32. Astrid Ayala & Szabolcs Blazsek, 2018. "Equity market neutral hedge funds and the stock market: an application of score-driven copula models," Applied Economics, Taylor & Francis Journals, vol. 50(37), pages 4005-4023, August.
   33. Ruijie Guan & Xu Zhao & Weihu Cheng & Yaohua Rong, 2021. "A New Generalized t Distribution Based on a Distribution Construction Method," Mathematics, MDPI, vol. 9(19), pages 1-36, September.
   34. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
   35. Dennis Umlandt, 2020. "Likelihood-based Dynamic Asset Pricing: Learning Time-varying Risk Premia from Cross-Sectional Models," Working Paper Series 2020-06, University of Trier, Research Group Quantitative Finance and Risk Analysis.
   36. Harvey, Andew & Liao, Yin, 2023. "Dynamic Tobit models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 72-83.
   37. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
   38. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
   39. Rutger-Jan Lange & Bram van Os & Dick van Dijk, 2022. "Implicit score-driven filters for time-varying parameter models," Tinbergen Institute Discussion Papers 22-066/III, Tinbergen Institute, revised 21 Nov 2024.
   40. Ayala Astrid & Blazsek Szabolcs & Escribano Alvaro, 2023. "Anticipating extreme losses using score-driven shape filters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 449-484, September.
   41. Ayala, Astrid & Blazsek, Szabolcs, 2019. "Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk," UC3M Working papers. Economics 28638, Universidad Carlos III de Madrid. Departamento de Economía.

7. Robin Niesert & Jochem Oorschot & Chris Veldhuisen & Kester Brons & Rutger-Jan Lange, "undated". "Can Google Search Data Help Predict Macroeconomic Series?," Tinbergen Institute Discussion Papers 19-021/III, Tinbergen Institute.
   • Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.

   Cited by:

   1. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
   2. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
   3. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
   4. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
   5. Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers 2020-11, University of Paris Nanterre, EconomiX.
      • Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
      • Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
      • Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
      • Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers 2007.00273, arXiv.org, revised Sep 2022.
      • Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
      • Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
   6. VAN DER WIELEN Wouter & BARRIOS Salvador, 2020. "Fear and Employment During the COVID Pandemic: Evidence from Search Behaviour in the EU," JRC Working Papers on Taxation & Structural Reforms 2020-08, Joint Research Centre.
      • van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
   7. Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
   8. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
      • Bhattacharjee, Arnab & Kohns, David, 2022. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," National Institute of Economic and Social Research (NIESR) Discussion Papers 538, National Institute of Economic and Social Research.
   9. Adrian Fernandez-Perez & Ana-Maria Fuertes & Marcos Gonzalez-Fernandez & Joelle Miffre, 2020. "Fear of Hazards in Commodity Futures Markets," Post-Print hal-02931680, HAL.
      • Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Gonzalez-Fernandez, Marcos & Miffre, Joelle, 2019. "Fear of Hazards in Commodity Futures Markets," MPRA Paper 100528, University Library of Munich, Germany, revised 06 May 2020.
      • Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Gonzalez-Fernandez, Marcos & Miffre, Joelle, 2020. "Fear of hazards in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 119(C).
   10. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia & Brzeszczyński, Janusz, 2024. "Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
   11. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
   12. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
   13. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
   14. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
   15. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
   16. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

Articles

1. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
   • Robin Niesert & Jochem Oorschot & Chris Veldhuisen & Kester Brons & Rutger-Jan Lange, "undated". "Can Google Search Data Help Predict Macroeconomic Series?," Tinbergen Institute Discussion Papers 19-021/III, Tinbergen Institute.
   See citations under working paper version above.
2. Lange, Rutger-Jan & Ralph, Daniel & Støre, Kristian, 2020. "Real-Option Valuation in Multiple Dimensions Using Poisson Optional Stopping Times," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(2), pages 653-677, March.

   Cited by:

   1. Dammann, Felix & Ferrari, Giorgio, 2021. "On an Irreversible Investment Problem with Two-Factor Uncertainty," Center for Mathematical Economics Working Papers 646, Center for Mathematical Economics, Bielefeld University.
   2. Teulings, Coen & Lange, Rutger-Jan, 2021. "The option value of vacant land: Don't build when demand for housing is booming," CEPR Discussion Papers 16023, C.E.P.R. Discussion Papers.
      • Rutger-Jan Lange & Coen N. Teulings, 2021. "The option value of vacant land: Don't build when demand for housing is booming," Tinbergen Institute Discussion Papers 21-022/IV, Tinbergen Institute.
   3. Nunes, Cláudia & Oliveira, Carlos & Pimentel, Rita, 2021. "Quasi-analytical solution of an investment problem with decreasing investment cost due to technological innovations," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
   4. Lange, Rutger-Jan & Teulings, Coen N., 2024. "Irreversible investment under predictable growth: Why land stays vacant when housing demand is booming," Journal of Economic Theory, Elsevier, vol. 215(C).
   5. Compernolle, T. & Huisman, Kuno & Kort, Peter M. & Lavrutich, Maria & Nunes, Claudia & Thijssen, J.J.J., 2018. "Investment Decisions with Two-Factor Uncertainty," Discussion Paper 2018-003, Tilburg University, Center for Economic Research.
      • Compernolle, T. & Huisman, Kuno & Kort, Peter M. & Lavrutich, Maria & Nunes, Claudia & Thijssen, J.J.J., 2018. "Investment Decisions with Two-Factor Uncertainty," Other publications TiSEM 9e05d125-86f6-4549-9d49-9, Tilburg University, School of Economics and Management.
      • Tine Compernolle & Kuno J. M. Huisman & Peter M. Kort & Maria Lavrutich & Cláudia Nunes & Jacco J. J. Thijssen, 2021. "Investment Decisions with Two-Factor Uncertainty," JRFM, MDPI, vol. 14(11), pages 1-17, November.
   6. Felix Dammann & Giorgio Ferrari, 2021. "On an Irreversible Investment Problem with Two-Factor Uncertainty," Papers 2103.08258, arXiv.org, revised Jul 2021.
   7. Alvarez E., Luis H.R. & Lempa, Jukka & Saarinen, Harto & Sillanpää, Wiljami, 2024. "Solutions for Poissonian stopping problems of linear diffusions via extremal processes," Stochastic Processes and their Applications, Elsevier, vol. 172(C).
   8. Takuji Arai & Masahiko Takenaka, 2022. "Constrained optimal stopping under a regime-switching model," Papers 2204.07914, arXiv.org.
   9. Zhang, Xinhua & Hueng, C. James & Lemke, Robert J., 2023. "Using a price floor on carbon allowances to achieve emission reductions under uncertainty," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1096-1110.
   10. Hobson, David, 2021. "The shape of the value function under Poisson optimal stopping," Stochastic Processes and their Applications, Elsevier, vol. 133(C), pages 229-246.
   11. Balter, Anne G. & Huisman, Kuno J.M. & Kort, Peter M., 2022. "New insights in capacity investment under uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).

3. Andrew Harvey & Rutger‐Jan Lange, 2018. "Modeling the Interactions between Volatility and Returns using EGARCH‐M," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 909-919, November.

   Cited by:

   1. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
      • Harvey, Andrew & Palumbo, Dario, 2023. "Score-driven models for realized volatility," Journal of Econometrics, Elsevier, vol. 237(2).
   2. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
   3. Fei, Tianlun & Liu, Xiaoquan, 2021. "Herding and market volatility," International Review of Financial Analysis, Elsevier, vol. 78(C).
   4. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
   5. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
   6. Astrid Ayala & Szabolcs Blazsek & Adrian Licht, 2022. "Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020," Empirical Economics, Springer, vol. 62(5), pages 2179-2203, May.
   7. Harvey, A. & Liao, Y., 2019. "Dynamic Tobit models," Cambridge Working Papers in Economics 1913, Faculty of Economics, University of Cambridge.
   8. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
   9. Harvey, Andew & Liao, Yin, 2023. "Dynamic Tobit models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 72-83.
   10. Elisa Navarra, 2022. "Stock Market Response to Firms’ Misconduct," Working Papers ECARES 2022-40, ULB -- Universite Libre de Bruxelles.
   11. Ayala Astrid & Blazsek Szabolcs & Escribano Alvaro, 2023. "Anticipating extreme losses using score-driven shape filters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 449-484, September.
   12. Ciarreta, Aitor & Pizarro-Irizar, Cristina & Zarraga, Ainhoa, 2020. "Renewable energy regulation and structural breaks: An empirical analysis of Spanish electricity price volatility," Energy Economics, Elsevier, vol. 88(C).
   13. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.

4. Michael Atkinson & Moshe Kress & Rutger-Jan Lange, 2016. "When Is Information Sufficient for Action? Search with Unreliable yet Informative Intelligence," Operations Research, INFORMS, vol. 64(2), pages 315-328, April.

   Cited by:

   1. Ben Hermans & Herbert Hamers & Roel Leus & Roy Lindelauf, 2019. "Timely exposure of a secret project: Which activities to monitor?," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(6), pages 451-468, September.
   2. Baycik, N. Orkun & Sharkey, Thomas C. & Rainwater, Chase E., 2020. "A Markov Decision Process approach for balancing intelligence and interdiction operations in city-level drug trafficking enforcement," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).

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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 14 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) 2015-06-20 2020-09-14 2022-10-17 2024-09-09. Author is listed
2. NEP-ETS: Econometric Time Series (3) 2015-06-20 2020-09-14 2024-09-30
3. NEP-RMG: Risk Management (3) 2015-06-20 2015-07-11 2016-09-04
4. NEP-URE: Urban and Real Estate Economics (3) 2018-04-16 2018-04-30 2021-03-08
5. NEP-ENE: Energy Economics (2) 2019-03-25 2021-01-04
6. NEP-ENV: Environmental Economics (2) 2019-03-25 2021-01-04
7. NEP-MAC: Macroeconomics (2) 2018-04-16 2018-04-30
8. NEP-AGR: Agricultural Economics (1) 2021-01-04
9. NEP-BIG: Big Data (1) 2019-04-22
10. NEP-CBA: Central Banking (1) 2016-09-04
11. NEP-EEC: European Economics (1) 2016-09-04
12. NEP-FMK: Financial Markets (1) 2015-07-11
13. NEP-FOR: Forecasting (1) 2019-04-22
14. NEP-HIS: Business, Economic and Financial History (1) 2022-02-14
15. NEP-HME: Heterodox Microeconomics (1) 2019-03-25
16. NEP-ORE: Operations Research (1) 2020-09-14
17. NEP-RES: Resource Economics (1) 2021-01-04

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