Aart F. de Vos

Personal Details

First Name:	Aart
Middle Name:	F.
Last Name:	de Vos
Suffix:
RePEc Short-ID:	pde930
	[This author has chosen not to make the email address public]
	http://personal.vu.nl/a.f.de.vos/
Terminal Degree:	1975 Faculteit Economie en Bedrijfskunde; Universiteit van Amsterdam (from RePEc Genealogy)

Affiliation

Afdeling Econometrie and Operations Research
School of Business and Economics
Vrije Universiteit Amsterdam

Amsterdam, Netherlands
https://sbe.vu.nl/nl/afdelingen-en-instituten/econometrie-en-or-nieuw/
RePEc:edi:ectvunl (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.

Articles

1. Marc K. Francke & Siem Jan Koopman & Aart F. De Vos, 2010. "Likelihood functions for state space models with diffuse initial conditions," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 407-414, November.
   • Marc K. Francke & Siem Jan Koopman & Aart de Vos, 2008. "Likelihood Functions for State Space Models with Diffuse Initial Conditions," Tinbergen Institute Discussion Papers 08-040/4, Tinbergen Institute.

   Cited by:

   1. Victor Bystrov, 2018. "Measuring the Natural Rates of Interest in Germany and Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(4), pages 333-353, December.
      • Bystrov Victor, 2018. "Measuring the Natural Rates of Interest in Germany and Italy," Lodz Economics Working Papers 7/2018, University of Lodz, Faculty of Economics and Sociology.
   2. Helske, Jouni, 2017. "KFAS: Exponential Family State Space Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i10).
   3. Søren Johansen & Marco Riani & Anthony C. Atkinson, 2012. "The Selection of ARIMA Models with or without Regressors," CREATES Research Papers 2012-46, Department of Economics and Business Economics, Aarhus University.
      • Søren Johansen & Marco Riani & Anthony C. Atkinson, 2012. "The Selection of ARIMA Models with or without Regressors," Discussion Papers 12-17, University of Copenhagen. Department of Economics.
   4. Yue Zhao & Difang Wan, 2018. "Institutional high frequency trading and price discovery: Evidence from an emerging commodity futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 243-270, February.
   5. Webel, Karsten & Smyk, Anna, 2023. "Towards seasonal adjustment of infra-monthly time series with JDemetra+," Discussion Papers 24/2023, Deutsche Bundesbank.
   6. Tommaso Proietti & Diego J. Pedregal, 2021. "Seasonality in High Frequency Time Series," CEIS Research Paper 508, Tor Vergata University, CEIS, revised 11 Mar 2021.
      • Proietti, Tommaso & Pedregal, Diego J., 2023. "Seasonality in High Frequency Time Series," Econometrics and Statistics, Elsevier, vol. 27(C), pages 62-82.
   7. Nilsen, Øivind Anti & Raknerud, Arvid & Skjerpen, Terje, 2011. "Using the Helmert-Transformation to Reduce Dimensionality in a Mixed Model: Application to a Wage Equation with Worker and Firm Heterogeneity," IZA Discussion Papers 5847, Institute of Labor Economics (IZA).
      • Øivind A. Nilsen & Arvid Raknerud & Terje Skjerpen, 2011. "Using the Helmert-transformation to reduce dimensionality in a mixed model: An application to a wage equation with worker and firm heterogeneity," Discussion Papers 667, Statistics Norway, Research Department.
      • Nilsen, Øivind Anti & Raknerud, Arvid & Skjerpen, Terje, 2011. "Using the Helmert-transformation to reduce dimensionality in a mixed model: Application to a wage equation with worker and firm heterogeneity," Discussion Paper Series in Economics 11/2011, Norwegian School of Economics, Department of Economics, revised 04 Oct 2011.
   8. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
      • Luati, Alessandra & Proietti, Tommaso, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Working Papers 2012_02, University of Sydney Business School, Discipline of Business Analytics.
      • Tommaso, Proietti & Alessandra, Luati, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," MPRA Paper 39600, University Library of Munich, Germany.
   9. José Casals & Sonia Sotoca & Miguel Jerez, 2012. "Minimally Conditioned Likelihood for a Nonstationary State Space Model," Documentos de Trabajo del ICAE 2012-04, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
   10. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
       • Poncela Blanco, Maria Pilar & Ruiz Ortega, Esther & Miranda Gualdrón, Karen Alejandra, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de Estadística.
   11. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
   12. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2018. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 107-123.
       • Martyna Marczak & Tommaso Proietti & Stefano Grassi, 2016. "A Data–Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models," CEIS Research Paper 374, Tor Vergata University, CEIS, revised 31 Mar 2016.
       • Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2015. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Hohenheim Discussion Papers in Business, Economics and Social Sciences 13-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
   13. Victor Bystrov, 2020. "Identification and Estimation of Initial Conditions in Non-Minimal State-Space Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(4), pages 413-429, December.
   14. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
   15. Raïsa Basselier & David de Antonio Liedo & Jana Jonckheere & Geert Langenus, 2018. "Can inflation expectations in business or consumer surveys improve inflation forecasts?," Working Paper Research 348, National Bank of Belgium.
   16. Øivind A. Nilsen & Arvid Raknerud & Terje Skjerpen, 2017. "Estimation of a model for matched panel data with high-dimensional two-way unobserved heterogeneity," Empirical Economics, Springer, vol. 53(4), pages 1657-1680, December.

2. Francke, Marc K. & de Vos, Aart F., 2007. "Marginal likelihood and unit roots," Journal of Econometrics, Elsevier, vol. 137(2), pages 708-728, April.

   Cited by:

   1. Marc Francke, 2010. "Repeat Sales Index for Thin Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 41(1), pages 24-52, July.
   2. Marc K. Francke & Siem Jan Koopman & Aart F. De Vos, 2010. "Likelihood functions for state space models with diffuse initial conditions," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 407-414, November.
      • Marc K. Francke & Siem Jan Koopman & Aart de Vos, 2008. "Likelihood Functions for State Space Models with Diffuse Initial Conditions," Tinbergen Institute Discussion Papers 08-040/4, Tinbergen Institute.
   3. Bent Nielsen, 2003. "Power of tests for unit roots in the presence of a linear trend," Economics Papers 2003-W22, Economics Group, Nuffield College, University of Oxford.
      • Bent Nielsen, 2008. "Power of Tests for Unit Roots in the Presence of a Linear Trend," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 619-644, October.
   4. Willa W. Chen & Rohit S. Deo, 2009. "The restricted likelihood ratio test at the boundary in autoregressive series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 618-630, November.
   5. Patrick Marsh, "undated". "Saddlepoint Approximations for Optimal Unit Root Tests," Discussion Papers 09/31, Department of Economics, University of York.
   6. Patrick Marsh, 2019. "Properties of the power envelope for tests against both stationary and explosive alternatives: the effect of trends," Discussion Papers 19/03, University of Nottingham, Granger Centre for Time Series Econometrics.
   7. Sriananthakumar, Sivagowry, 2013. "Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach," Economic Modelling, Elsevier, vol. 33(C), pages 126-136.

3. Rob Luginbuhl & Aart de Vos, 2003. "Seasonality and Markov switching in an unobserved component time series model," Empirical Economics, Springer, vol. 28(2), pages 365-386, April.

   Cited by:

   1. Antonio Matas-Mir & Denise R. Osborn & Marco J. Lombardi, 2008. "The effect of seasonal adjustment on the properties of business cycle regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 257-278.
      • Antonio Matas-Mir & Denise R. Osborn & Marco Lombardi, 2005. "The Effect of Seasonal Adjustment on the Properties of Business Cycle Regimes," Econometrics Working Papers Archive wp2005_15, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
   2. Rob Luginbuhl, 2020. "Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model," CPB Discussion Paper 409, CPB Netherlands Bureau for Economic Policy Analysis.

4. Francke, M K & de Vos, A F, 2000. "Efficient Computation of Hierarchical Trends," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 51-57, January.

   Cited by:

   1. David Geltner & Anil Kumar & Alex M. Van de Minne, 2020. "Riskiness of Real Estate Development: A Perspective from Urban Economics and Option Value Theory," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 406-445, June.
   2. Hany Guirguis & Christos Giannikos & Randy Anderson, 2004. "The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients," The Journal of Real Estate Finance and Economics, Springer, vol. 30(1), pages 33-53, October.
   3. Marc Francke, 2010. "Repeat Sales Index for Thin Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 41(1), pages 24-52, July.
   4. David Albouy & Minchul Shin, 2022. "A Statistical Learning Approach to Land Valuation: Optimizing the Use of External Information," Working Papers 22-38, Federal Reserve Bank of Philadelphia.
      • Albouy, David & Shin, Minchul, 2022. "A statistical learning approach to land valuation: Optimizing the use of external information," Journal of Housing Economics, Elsevier, vol. 58(PA).
   5. Alex Minne & Marc Francke & David Geltner & Robert White, 2020. "Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 514-553, May.
   6. Melser, Daniel, 2017. "Disaggregated property price appreciation: The mixed repeat sales model," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 108-118.
   7. Marc K. Francke & Alex Minne, 2017. "The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 55(4), pages 511-532, November.
   8. Marc Francke & Alex Van de Minne, 2021. "Modeling unobserved heterogeneity in hedonic price models," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(4), pages 1315-1339, December.
   9. Bing Zhu & Dorinth van Dijk & Colin Lizieri, 2021. "Price diffusion across international private commercial real estate markets," Working Papers 732, DNB.
      • Zhu, Bing & van Dijk, Dorinth & Lizieri, Colin, 2024. "Price diffusion across international private commercial real estate markets," Journal of International Money and Finance, Elsevier, vol. 140(C).
   10. Alicia N. Rambaldi & Cameron S. Fletcher, 2014. "Hedonic Imputed Property Price Indexes: The Effects of Econometric Modeling Choices," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S2), pages 423-448, November.

5. Luginbuhl, Rob & de Vos, Aart, 1999. "Bayesian Analysis of an Unobserved-Component Time Series Model of GDP with Markov-Switching and Time-Varying Growths," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 456-465, October.

   Cited by:

   1. Chin Nam Low & Heather Anderson & Ralph Snyder, 2006. "Beverridge Nelson Decomposition With Markov Switching," CAMA Working Papers 2006-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
      • Chin Nam Low & Heather Anderson & Ralph Snyder, 2006. "Beveridge-Nelson Decomposition with Markov Switching," Melbourne Institute Working Paper Series wp2006n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
      • Chin Nam Low & Heather Anderson & Ralph D. Snyder, 2006. "Beveridge-Nelson Decomposition with Markov Switching," Monash Econometrics and Business Statistics Working Papers 17/06, Monash University, Department of Econometrics and Business Statistics.
   2. Eliana González & Luis F. Melo & Luis E. Rojas & Brayan Rojas, 2010. "Estimations of the natural rate of interest in Colombia," Borradores de Economia 626, Banco de la Republica de Colombia.
      • Eliana González & Luis F. Melo & Luis E. Rojas & Brayan Rojas, 2011. "Estimations of the Natural Rate of Interest in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 33-75, January-J.
      • Eliana González & Luis F. Melo & Luis E. Rojas & Brayan Rojas, 2010. "Estimations of the natural rate of interest in Colombia," Borradores de Economia 7667, Banco de la Republica.
   3. Siem Jan Koopman & Philip Hans Franses, 2002. "Constructing Seasonally Adjusted Data with Time‐varying Confidence Intervals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(5), pages 509-526, December.
      • Koopman, S.J. & Franses, Ph.H.B.F., 2001. "Constructing seasonally adjusted data with time-varying confidence intervals," Econometric Institute Research Papers EI 2001-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
   4. Graflund, Andreas, 2001. "Are the Nordic Stock Markets Mean Reverting?," Working Papers 2001:15, Lund University, Department of Economics.
   5. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
   6. Andreas Graflund, 2000. "A Bayes Inference Approach to Testing Mean Reversion in the Swedish Stock Market," Econometric Society World Congress 2000 Contributed Papers 1363, Econometric Society.
   7. Paap, R. & van Dijk, H.K., 2002. "Bayes estimates of Markov trends in possibly cointegrated series: an application to US consumption and income," Econometric Institute Research Papers EI 2002-42, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
      • Richard Paap & Herman K. van Dijk, 1999. "Bayes Estimates of Markov Trends in possibly Cointegrated Series: An Application to US Consumption and Income," Tinbergen Institute Discussion Papers 99-024/4, Tinbergen Institute.
      • Paap, Richard & van Dijk, Herman K, 2003. "Bayes Estimates of Markov Trends in Possibly Cointegrated Series: An Application to U.S. Consumption and Income," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 547-563, October.
   8. Beatriz C. Galvao, Ana, 2002. "Can non-linear time series models generate US business cycle asymmetric shape?," Economics Letters, Elsevier, vol. 77(2), pages 187-194, October.
   9. Shami, R.G. & Forbes, C.S., 2000. "A structural Time Series Model with Markov Switching," Monash Econometrics and Business Statistics Working Papers 10/00, Monash University, Department of Econometrics and Business Statistics.
   10. Sinclair Tara M, 2009. "Asymmetry in the Business Cycle: Friedman's Plucking Model with Correlated Innovations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(1), pages 1-31, December.
   11. 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.

6. Merkus, H R & Pollock, D S G & de Vos, A F, 1993. "A Synopsis of the Smoothing Formulae Associated with the Kalman Filter," Computational Economics, Springer;Society for Computational Economics, vol. 6(3-4), pages 177-200, November.
   • Merkus, H.R. & Pollock, D.S.G. & Vos, A.F., 1991. "A synopsis of the smoothing formulae associated with the Kalman Filter," Serie Research Memoranda 0079, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

   Cited by:

   1. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.
   2. González, P. & Idais, H. & Pasadas, M. & Yasin, M., 2019. "3D fuzzy data approximation by fuzzy smoothing bicubic splines," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 164(C), pages 94-102.

7. Jacob A. Bikker & Aart F. De Vos, 1992. "An international trade flow model with zero observations: an extension of the Tobit model," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 135, pages 379-404.

   Cited by:

   1. J.A. Bikker, 2009. "An extended gravity model with substitution applied to international trade," Working Papers 09-17, Utrecht School of Economics.
   2. Zahoor Haq & Karl Meilke, 2009. "The Role of Income in Trading‐Differentiated Agri‐Food Products: The Case of Canada, the United States, and Selected EU Countries," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(3), pages 343-363, September.
   3. Gert-Jan M. Linders & Henri L.F. de Groot, 2006. "Estimation of the Gravity Equation in the Presence of Zero Flows," Tinbergen Institute Discussion Papers 06-072/3, Tinbergen Institute.
   4. Estrella Gómez Herrera, 2010. "Comparing alternative methods to estimate gravity models of bilateral trade," ThE Papers 10/05, Department of Economic Theory and Economic History of the University of Granada..
      • Estrella Gómez-Herrera, 2013. "Comparing alternative methods to estimate gravity models of bilateral trade," Empirical Economics, Springer, vol. 44(3), pages 1087-1111, June.
   5. Mogilat, A. & Salnikov, V., 2015. "Trade Effects Estimation for the Case of Eurasian Economic Space Countries: Application of Regional Gravity Model," Journal of the New Economic Association, New Economic Association, vol. 27(3), pages 80-108.
   6. G.J.M. Linders, 2006. "Estimation of the Gravity Equation of Bilateral Trade in the Presence of Zero Flows," ERSA conference papers ersa06p746, European Regional Science Association.
   7. Martijn Burger & Frank van Oort & Gert-Jan Linders, 2009. "On the Specification of the Gravity Model of Trade: Zeros, Excess Zeros and Zero-inflated Estimation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(2), pages 167-190.
      • Burger, M.J. & van Oort, F.G. & Linders, G.J.M., 2009. "On the Specification of the Gravity Model of Trade: Zeros, Excess Zeros and Zero-Inflated Estimation," ERIM Report Series Research in Management ERS-2009-003-ORG, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
   8. Martin Grančay & Nóra Grančay & Jana Drutarovská & Ladislav Mura, 2015. "Gravitačný model zahraničného obchodu českej a slovenskej republiky 1995-2012: ako sa zmenili determinanty obchodu? [Gravity Model of Trade of the Czech and Slovak Republics 1995-2012: How Have Det," Politická ekonomie, Prague University of Economics and Business, vol. 2015(6), pages 759-777.
   9. Natalia Drzewoszewska, 2014. "Searching for the Appropriate Measure of Multilateral Trade-Resistance Terms in the Gravity Model of Bilateral Trade Flows," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 14, pages 29-49.
   10. Gert-Jan Linders, 2004. "The Effect of Domestic Institutions on International Trade Flows: A sectoral analysis," ERSA conference papers ersa04p357, European Regional Science Association.
   11. Jeffrey B. Nugent & Jiaxuan Lu, 2020. "Does the All-China Federation of Industry and Commerce Align Private Firms with the Goals of the People's Republic of China's Belt and Road Initiative?," Asian Development Review, MIT Press, vol. 37(2), pages 45-76, September.

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