| Year |
Citation |
Score |
| 2020 |
Blasques F, Koopman SJ, Lucas A. Nonlinear autoregressive models with optimality properties Econometric Reviews. 39: 559-578. DOI: 10.1080/07474938.2019.1701807 |
0.4 |
|
| 2020 |
Blasques F, Gorgi P, Koopman SJ. Missing Observations in Observation-Driven Time Series Models Journal of Econometrics. 2018. DOI: 10.1016/J.Jeconom.2020.07.043 |
0.493 |
|
| 2020 |
Borowska A, Hoogerheide L, Koopman SJ, Dijk HKv. Partially censored posterior for robust and efficient risk evaluation Journal of Econometrics. 217: 335-355. DOI: 10.1016/J.Jeconom.2019.12.007 |
0.41 |
|
| 2020 |
Bräuning F, Koopman SJ. The dynamic factor network model with an application to international trade Journal of Econometrics. 216: 494-515. DOI: 10.1016/J.Jeconom.2019.10.007 |
0.455 |
|
| 2020 |
Li M, Koopman SJ, Lit R, Petrova D. Long-term forecasting of El Niño events via dynamic factor simulations Journal of Econometrics. 214: 46-66. DOI: 10.1016/J.Jeconom.2019.05.004 |
0.416 |
|
| 2019 |
Bennedsen M, Hillebrand E, Koopman SJ. Trend analysis of the airborne fraction and sink rate of anthropogenically released CO 2 Biogeosciences. 16: 3651-3663. DOI: 10.5194/Bg-16-3651-2019 |
0.371 |
|
| 2019 |
Koopman SJ, Lit R, Nguyen TM. Modified efficient importance sampling for partially non-Gaussian state space models Statistica Neerlandica. 73: 44-62. DOI: 10.1111/Stan.12128 |
0.444 |
|
| 2019 |
Gorgi P, Koopman SJ, Lit R. The Analysis and Forecasting of ATP Tennis Matches Using a High-Dimensional Dynamic Model Journal of the Royal Statistical Society Series a-Statistics in Society. 182: 1393-1409. DOI: 10.1111/Rssa.12464 |
0.497 |
|
| 2019 |
Hansen PR, Janus P, Koopman SJ. Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model Journal of Financial Econometrics. 17: 1-32. DOI: 10.1093/Jjfinec/Nby007 |
0.482 |
|
| 2019 |
Blasques F, Gorgi P, Koopman SJ. Accelerating score-driven time series models Journal of Econometrics. 212: 359-376. DOI: 10.1016/J.Jeconom.2019.03.005 |
0.504 |
|
| 2019 |
Gorgi P, Koopman SJ, Li M. Forecasting economic time series using score-driven dynamic models with mixed-data sampling International Journal of Forecasting. 35: 1735-1747. DOI: 10.1016/J.Ijforecast.2018.11.005 |
0.469 |
|
| 2019 |
Petrova D, Lowe R, Stewart-Ibarra A, Ballester J, Koopman SJ, Rodó X. Sensitivity of large dengue epidemics in Ecuador to long-lead predictions of El Niño Climate Services. 15: 100096. DOI: 10.1016/J.Cliser.2019.02.003 |
0.406 |
|
| 2018 |
Blasques F, Gorgi P, Koopman SJ, Wintenberger O. Feasible invertibility conditions and maximum likelihood estimation for observation-driven models Electronic Journal of Statistics. 12: 1019-1052. DOI: 10.1214/18-Ejs1416 |
0.459 |
|
| 2018 |
Barra I, Borowska A, Koopman SJ. Bayesian Dynamic Modeling of High-Frequency Integer Price Changes Journal of Financial Econometrics. 16: 384-424. DOI: 10.1093/Jjfinec/Nby010 |
0.52 |
|
| 2018 |
Blasques F, Koopman SJ, Lucas A. Amendments and Corrections‘Information-theoretic optimality of observation-driven time series models for continuous responses’ Biometrika. 105: 753-753. DOI: 10.1093/Biomet/Asy039 |
0.403 |
|
| 2018 |
Koopman SJ, Lit R, Lucas A, Opschoor A. Dynamic discrete copula models for high‐frequency stock price changes Journal of Applied Econometrics. 33: 966-985. DOI: 10.1002/Jae.2645 |
0.495 |
|
| 2017 |
Koopman SJ, Mesters G. Empirical Bayes Methods for Dynamic Factor Models The Review of Economics and Statistics. 99: 486-498. DOI: 10.2139/Ssrn.2441183 |
0.433 |
|
| 2017 |
Bazzi M, Blasques F, Koopman SJ, Lucas A. Time Varying Transition Probabilities for Markov Regime Switching Models Journal of Time Series Analysis. 38: 458-478. DOI: 10.1111/Jtsa.12211 |
0.487 |
|
| 2017 |
Koopman SJ, Lit R, Lucas A. Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model Journal of the American Statistical Association. 112: 1490-1503. DOI: 10.1080/01621459.2017.1302878 |
0.482 |
|
| 2017 |
Petrova D, Koopman SJ, Ballester J, Rodó X. Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model Climate Dynamics. 48: 1249-1276. DOI: 10.1007/S00382-016-3139-Y |
0.523 |
|
| 2017 |
Barra I, Hoogerheide LF, Koopman SJ, Lucas A. Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models Journal of Applied Econometrics. 32: 1003-1026. DOI: 10.1002/Jae.2533 |
0.439 |
|
| 2016 |
Vujić S, Commandeur JJF, Koopman SJ. Intervention time series analysis of crime rates: The case of sentence reform in Virginia. Economic Modelling. 57: 311-323. PMID 32287827 DOI: 10.1016/J.Econmod.2016.02.017 |
0.329 |
|
| 2016 |
Koopman SJ, Lucas A, Scharth M. Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models The Review of Economics and Statistics. 98: 97-110. DOI: 10.2139/Ssrn.2016266 |
0.499 |
|
| 2016 |
Calvori F, Creal D, Koopman SJ, Lucas A. Testing for Parameter Instability across Different Modeling Frameworks Journal of Financial Econometrics. 15: 223-246. DOI: 10.1093/Jjfinec/Nbw008 |
0.341 |
|
| 2016 |
Mesters G, Koopman SJ, Ooms M. Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models Econometric Reviews. 35: 659-687. DOI: 10.1080/07474938.2015.1031014 |
0.412 |
|
| 2016 |
Blasques F, Koopman SJ, Lucas A, Schaumburg J. Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models Journal of Econometrics. 195: 211-223. DOI: 10.1016/J.Jeconom.2016.09.001 |
0.466 |
|
| 2016 |
Blasques F, Koopman SJ, Mallee M, Zhang Z. Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data Journal of Econometrics. 193: 405-417. DOI: 10.1016/J.Jeconom.2016.04.014 |
0.444 |
|
| 2016 |
Hindrayanto I, Koopman SJ, Winter Jd. Forecasting and nowcasting economic growth in the euro area using factor models International Journal of Forecasting. 32: 1284-1305. DOI: 10.1016/J.Ijforecast.2016.05.003 |
0.457 |
|
| 2016 |
Blasques F, Koopman SJ, Łasak K, Lucas A. Rejoinder to the discussion "In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation-Driven Models" International Journal of Forecasting. 32: 893-894. DOI: 10.1016/J.Ijforecast.2016.04.004 |
0.339 |
|
| 2016 |
Blasques F, Koopman SJ, Łasak K, Lucas A. In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models International Journal of Forecasting. DOI: 10.1016/J.Ijforecast.2015.11.018 |
0.402 |
|
| 2016 |
Galati G, Hindrayanto I, Koopman SJ, Vlekke M. Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area Economics Letters. 145: 83-87. DOI: 10.1016/J.Econlet.2016.05.034 |
0.413 |
|
| 2015 |
Koopman SJ, Lit R. A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League Journal of the Royal Statistical Society Series a-Statistics in Society. 178: 167-186. DOI: 10.1111/Rssa.12042 |
0.515 |
|
| 2015 |
Jungbacker B, Koopman SJ. Likelihood‐Based Dynamic Factor Analysis for Measurement and Forecasting Econometrics Journal. 18: 1-21. DOI: 10.1111/Ectj.12029 |
0.499 |
|
| 2015 |
Blasques F, Koopman SJ, Lucas A. Information-theoretic optimality of observation-driven time series models for continuous responses Biometrika. 102: 325-343. DOI: 10.1093/Biomet/Asu076 |
0.488 |
|
| 2015 |
Koopman SJ, Lucas A, Scharth M. Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models Journal of Business & Economic Statistics. 33: 114-127. DOI: 10.1080/07350015.2014.925807 |
0.453 |
|
| 2015 |
Nucera F, Schwaab B, Koopman SJ, Lucas A. The information in systemic risk rankings Journal of Empirical Finance. DOI: 10.1016/J.Jempfin.2016.01.002 |
0.341 |
|
| 2014 |
Blasques F, Koopman SJ, Lucas A. Information Theoretic Optimality of Observation Driven Time Series Models Biometrika. 102: 325-343. DOI: 10.2139/Ssrn.2423765 |
0.482 |
|
| 2014 |
Creal DD, Schwaab B, Koopman SJ, Lucas A. Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk The Review of Economics and Statistics. 96: 898-915. DOI: 10.2139/Ssrn.1765764 |
0.455 |
|
| 2014 |
Blasques F, Koopman SJ, Lucas A. Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes Electronic Journal of Statistics. 8: 1088-1112. DOI: 10.1214/14-Ejs924 |
0.41 |
|
| 2014 |
Janus P, Koopman SJ, Lucas A. Long Memory Dynamics for Multivariate Dependence Under Heavy Tails Journal of Empirical Finance. 29: 187-206. DOI: 10.1016/J.Jempfin.2014.09.007 |
0.423 |
|
| 2014 |
Mesters G, Koopman SJ. Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time Journal of Econometrics. 180: 127-140. DOI: 10.1016/J.Jeconom.2014.03.004 |
0.503 |
|
| 2014 |
Schwaab B, Koopman SJ, Lucas A. Nowcasting and forecasting global financial sector stress and credit market dislocation International Journal of Forecasting. 30: 741-758. DOI: 10.1016/J.Ijforecast.2013.10.004 |
0.392 |
|
| 2014 |
Bräuning F, Koopman SJ. Forecasting Macroeconomic Variables Using Collapsed Dynamic Factor Analysis International Journal of Forecasting. 30: 572-584. DOI: 10.1016/J.Ijforecast.2013.03.004 |
0.483 |
|
| 2014 |
Kontoghiorghes EJ, Van Dijk HK, Belsley DA, Bollerslev T, Diebold FX, Dufour JM, Engle R, Harvey A, Koopman SJ, Pesaran H, Phillips PCB, Smith RJ, West M, Yao Q, Amendola A, et al. CFEnetwork: The Annals of computational and financial econometrics: 2nd issue Computational Statistics and Data Analysis. 76: 1-3. DOI: 10.1016/J.Csda.2014.04.006 |
0.537 |
|
| 2014 |
Bos CS, Koopman SJ, Ooms M. Long memory with stochastic variance model Computational Statistics & Data Analysis. 76: 144-157. DOI: 10.1016/J.Csda.2012.11.019 |
0.449 |
|
| 2014 |
van Dijk D, Koopman SJ, van der Wel M, Wright JH. Forecasting interest rates with shifting endpoints Journal of Applied Econometrics. 29: 693-712. DOI: 10.1002/Jae.2358 |
0.366 |
|
| 2013 |
Koopman SJ, Scharth M. The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures Journal of Financial Econometrics. 11: 76-115. DOI: 10.1093/Jjfinec/Nbs016 |
0.479 |
|
| 2013 |
Hindrayanto I, Aston JAD, Koopman SJ, Ooms M. Modeling trigonometric seasonal components for monthly economic time series Applied Economics. 45: 3024-3034. DOI: 10.1080/00036846.2012.690937 |
0.519 |
|
| 2013 |
Koopman SJ, Wel Mvd. Forecasting the U.S. Term Structure of Interest Rates Using a Macroeconomic Smooth Dynamic Factor Model International Journal of Forecasting. 29: 676-694. DOI: 10.1016/J.Ijforecast.2012.12.004 |
0.446 |
|
| 2013 |
Creal D, Koopman SJ, Lucas A. Generalized Autoregressive Score Models With Applications Journal of Applied Econometrics. 28: 777-795. DOI: 10.1002/Jae.1279 |
0.514 |
|
| 2012 |
Jungbacker B, Koopman SJ, Wel Mvd. Smooth Dynamic Factor Analysis with Application to the U.S. Term Structure of Interest Rates Journal of Applied Econometrics. 29: 65-90. DOI: 10.2139/Ssrn.1403105 |
0.415 |
|
| 2012 |
Vujić S, Koopman SJ, Commandeur J. Economic Trends and Cycles in Crime: A Study for England and Wales Journal of Economics and Statistics. 232: 652-677. DOI: 10.1515/Jbnst-2012-0607 |
0.527 |
|
| 2012 |
Bos CS, Janus P, Koopman SJ. Spot Variance Path Estimation and Its Application to High Frequency Jump Testing Journal of Financial Econometrics. 10: 354-389. DOI: 10.1093/Jjfinec/Nbr013 |
0.362 |
|
| 2012 |
Koopman SJ, Lucas A, Schwaab B. Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008 Journal of Business & Economic Statistics. 30: 521-532. DOI: 10.1080/07350015.2012.700859 |
0.385 |
|
| 2012 |
Belsley DA, Kontoghiorghes EJ, Van Dijk HK, Bauwens L, Koopman SJ, McAleer M, Amendola A, Billio M, Croux C, Chen CWS, Davidson R, Duchesne P, Foschi P, Francq C, Fuertes AM, et al. The Annals of Computational and Financial Econometrics, first issue Computational Statistics and Data Analysis. 56: 2991-2992. DOI: 10.1016/J.Csda.2012.04.004 |
0.323 |
|
| 2012 |
Dordonnat V, Koopman SJ, Ooms M. Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling Computational Statistics & Data Analysis. 56: 3134-3152. DOI: 10.1016/J.Csda.2011.04.002 |
0.522 |
|
| 2011 |
Commandeur JJF, Koopman SJ, Ooms M. Statistical Software for State Space Methods Journal of Statistical Software. 41: 1-18. DOI: 10.18637/Jss.V041.I01 |
0.506 |
|
| 2011 |
Creal DD, Koopman SJ, Lucas A. A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations Journal of Business & Economic Statistics. 29: 552-563. DOI: 10.1198/Jbes.2011.10070 |
0.498 |
|
| 2011 |
Jungbacker BMJP, Koopman SJ, Wel Mvd. Maximum Likelihood Estimation for Dynamic Factor Models with Missing Data Journal of Economic Dynamics and Control. 35: 1358-1368. DOI: 10.1016/J.Jedc.2011.03.009 |
0.475 |
|
| 2011 |
Koopman SJ, Lucas A, Schwaab B. Modeling frailty-correlated defaults using many macroeconomic covariates Journal of Econometrics. 162: 312-325. DOI: 10.1016/J.Jeconom.2011.02.003 |
0.472 |
|
| 2011 |
Koopman SJ, Wong SY. Kalman filtering and smoothing for model-based signal extraction that depend on time-varying spectra Journal of Forecasting. 30: 147-167. DOI: 10.1002/For.1203 |
0.505 |
|
| 2010 |
Koopman SJ, Dordonnat V, Ooms M. Intradaily smoothing splines for time-varying regression models of hourly electricity loads The Journal of Energy Markets. 3: 17-52. DOI: 10.21314/Jem.2010.039 |
0.386 |
|
| 2010 |
Koopman SJ, Mallee MIP, Wel MVd. Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters Journal of Business & Economic Statistics. 28: 329-343. DOI: 10.1198/Jbes.2009.07295 |
0.485 |
|
| 2010 |
Francke MK, Koopman SJ, Vos AFD. Likelihood Functions for State Space Models with Diffuse Initial Conditions Journal of Time Series Analysis. 31: 407-414. DOI: 10.1111/J.1467-9892.2010.00673.X |
0.737 |
|
| 2010 |
Bijleveld F, Commandeur J, Koopman SJ, Montfort Kv. Multivariate non-linear time series modelling of exposure and risk in road safety research Journal of the Royal Statistical Society Series C-Applied Statistics. 59: 145-161. DOI: 10.1111/J.1467-9876.2009.00690.X |
0.506 |
|
| 2010 |
Koopman SJ, Ooms M. Exponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments International Journal of Forecasting. 26: 647-651. DOI: 10.1016/J.Ijforecast.2010.05.013 |
0.346 |
|
| 2010 |
Hindrayanto I, Koopman SJ, Ooms M. Exact maximum likelihood estimation for non-stationary periodic time series models Computational Statistics & Data Analysis. 54: 2641-2654. DOI: 10.1016/J.Csda.2010.04.010 |
0.559 |
|
| 2010 |
Creal D, Koopman SJ, Zivot E. Extracting a Robust U.S. Business Cycle Using a Time-Varying Multivariate Model-Based Bandpass Filter Journal of Applied Econometrics. 25: 695-719. DOI: 10.1002/Jae.1185 |
0.474 |
|
| 2009 |
Koopman SJ, Ooms M, Hindrayanto I. Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment Oxford Bulletin of Economics and Statistics. 71: 683-713. DOI: 10.1111/J.1468-0084.2009.00557.X |
0.535 |
|
| 2009 |
Koopman SJ, Lee KM. Seasonality with Trend and Cycle Interactions in Unobserved Components Models Journal of the Royal Statistical Society Series C-Applied Statistics. 58: 427-448. DOI: 10.1111/J.1467-9876.2009.00661.X |
0.544 |
|
| 2009 |
Harvey A, Koopman S. Unobserved components models in economics and finance Ieee Control Systems Magazine. 29: 71-81. DOI: 10.1109/Mcs.2009.934465 |
0.704 |
|
| 2009 |
Koopman SJ, Kräussl R, Lucas A, Monteiro AA. Credit Cycles and Macro Fundamentals Journal of Empirical Finance. 16: 42-54. DOI: 10.1016/J.Jempfin.2008.07.002 |
0.382 |
|
| 2009 |
Koopman SJ, Shephard N, Creal D. Testing the assumptions behind importance sampling Journal of Econometrics. 149: 2-11. DOI: 10.1016/J.Jeconom.2008.10.002 |
0.397 |
|
| 2008 |
Koopman SJ, Lucas A, Daniels RJ. A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk Journal of Business & Economic Statistics. 26: 510-525. DOI: 10.1198/073500108000000051 |
0.46 |
|
| 2008 |
Bijleveld F, Commandeur J, Gould PG, Koopman SJ. Model-based Measurement of Latent Risk in Time Series with Applications Journal of the Royal Statistical Society Series a-Statistics in Society. 171: 265-277. DOI: 10.1111/J.1467-985X.2007.00496.X |
0.338 |
|
| 2008 |
Koopman SJ, Ooms M, Lucas A, Montfort Kv, Geest VVD. Estimating Systematic Continuous-Time Trends in Recidivism Using a Non-Gaussian Panel Data Model Statistica Neerlandica. 62: 104-130. DOI: 10.1111/J.1467-9574.2007.00375.X |
0.55 |
|
| 2008 |
Koopman SJ, Lucas A, Monteiro AA. The Multi-State Latent Factor Intensity Model for Credit Rating Transitions Journal of Econometrics. 142: 399-424. DOI: 10.1016/J.Jeconom.2007.07.001 |
0.473 |
|
| 2008 |
Dordonnat V, Koopman SJ, Ooms M, Dessertaine A, Collet J. An Hourly Periodic State Space Model for Modelling French National Electricity Load International Journal of Forecasting. 24: 566-587. DOI: 10.1016/J.Ijforecast.2008.08.010 |
0.496 |
|
| 2007 |
Menkveld AJ, Koopman SJ, Lucas A. Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks using State Space Methods Journal of Business & Economic Statistics. 25: 213-225. DOI: 10.1198/073500106000000594 |
0.355 |
|
| 2007 |
Koopman SJ, Ooms M, Carnero MA. Periodic seasonal reg-ARFIMA-GARCH models for daily electricity spot prices Journal of the American Statistical Association. 102: 16-27. DOI: 10.1198/016214506000001022 |
0.43 |
|
| 2007 |
Koopman SJ, Azevedo JVE. Measuring Synchronization and Convergence of Business Cycles for the Euro area, UK and US Oxford Bulletin of Economics and Statistics. 70: 23-51. DOI: 10.1111/J.1468-0084.2007.00489.X |
0.427 |
|
| 2007 |
Jungbacker B, Koopman SJ. Monte Carlo estimation for nonlinear non-Gaussian state space models Biometrika. 94: 827-839. DOI: 10.1093/Biomet/Asm074 |
0.406 |
|
| 2006 |
Azevedo JVe, Koopman SJ, Rua A. Tracking the Business Cycle of the Euro Area: A Multivariate Model-Based Bandpass Filter Journal of Business & Economic Statistics. 24: 278-290. DOI: 10.1198/073500105000000261 |
0.446 |
|
| 2006 |
Jungbacker B, Koopman SJ. Monte Carlo likelihood estimation for three multivariate stochastic volatility models Econometric Reviews. 25: 385-408. DOI: 10.1080/07474930600712848 |
0.432 |
|
| 2006 |
Koopman SJ, Lee KM, Wong SY. Trend-Cycle Decomposition Models with Smooth-Transition Parameters: Evidence from U.S. Economic Time Series Contributions to Economic Analysis. 276: 199-219. DOI: 10.1016/S0573-8555(05)76008-9 |
0.478 |
|
| 2006 |
Amendola A, Francq C, Koopman SJ. Special Issue on Nonlinear Modelling and Financial Econometrics Computational Statistics & Data Analysis. 51: 2115-2117. DOI: 10.1016/J.Csda.2006.09.022 |
0.355 |
|
| 2006 |
Koopman SJ, Ooms M. Forecasting daily time series using periodic unobserved components time series models Computational Statistics & Data Analysis. 51: 885-903. DOI: 10.1016/J.Csda.2005.09.009 |
0.516 |
|
| 2006 |
Aston JAD, Koopman SJ. A non-Gaussian generalization of the Airline model for robust seasonal adjustment Journal of Forecasting. 25: 325-349. DOI: 10.1002/For.991 |
0.572 |
|
| 2005 |
Koopman SJ, Jungbacker B, Hol E. Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements Journal of Empirical Finance. 12: 445-475. DOI: 10.1016/J.Jempfin.2004.04.009 |
0.499 |
|
| 2005 |
Koopman SJ, Lucas A, Klaassen P. Empirical credit cycles and capital buffer formation Journal of Banking and Finance. 29: 3159-3179. DOI: 10.1016/J.Jbankfin.2005.01.003 |
0.424 |
|
| 2005 |
Koopman SJ, Lucas Aé. Business and default cycles for credit risk Journal of Applied Econometrics. 20: 311-323. DOI: 10.1002/Jae.833 |
0.313 |
|
| 2004 |
Koopman SJ, Lee KM. Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers Studies in Nonlinear Dynamics and Econometrics. 8: 1-17. DOI: 10.2202/1558-3708.1210 |
0.436 |
|
| 2004 |
Koopman SJ, Bos CS. State Space Models With a Common Stochastic Variance Journal of Business & Economic Statistics. 22: 346-357. DOI: 10.1198/073500104000000190 |
0.501 |
|
| 2004 |
Luginbuhl R, Koopman SJ. Convergence in European GDP series: a multivariate common converging trend–cycle decomposition Journal of Applied Econometrics. 19: 611-636. DOI: 10.1002/Jae.785 |
0.485 |
|
| 2003 |
Luginbuhl R, Koopman SJ. Convergence in European GDP Series Journal of Applied Econometrics. 19: 611-636. DOI: 10.2139/Ssrn.395340 |
0.702 |
|
| 2003 |
Koopman SJ, Durbin J. Filtering and smoothing of state vector for diffuse state-space models Journal of Time Series Analysis. 24: 85-98. DOI: 10.1111/1467-9892.00294 |
0.379 |
|
| 2003 |
Koopman SJ, Ooms M. Time Series Modelling of Daily Tax Revenues Statistica Neerlandica. 57: 439-469. DOI: 10.1111/1467-9574.00239 |
0.528 |
|
| 2003 |
Koopman SJ, Harvey A. Computing Observation Weights for Signal Extraction and Filtering Journal of Economic Dynamics and Control. 27: 1317-1333. DOI: 10.1016/S0165-1889(02)00061-1 |
0.655 |
|
| 2002 |
Koopman SJ, Franses PH. Constructing Seasonally Adjusted Data with Time-Varying Confidence Intervals Oxford Bulletin of Economics and Statistics. 64: 509-526. DOI: 10.1111/1468-0084.00275 |
0.481 |
|
| 2002 |
Durbin J, Koopman SJ. A simple and efficient simulation smoother for state space time series analysis Biometrika. 89: 603-615. DOI: 10.1093/Biomet/89.3.603 |
0.413 |
|
| 2002 |
Koopman SJ, Uspensky EH. The stochastic volatility in mean model: empirical evidence from international stock markets Journal of Applied Econometrics. 17: 667-689. DOI: 10.1002/Jae.652 |
0.521 |
|
| 2001 |
Butter FAGd, Koopman SJ. Interaction between structural and cyclical shocks in production and employment Review of World Economics. 137: 273-296. DOI: 10.1007/Bf02707266 |
0.347 |
|
| 2000 |
Koopman SJ, Durbin J. Fast filtering and smoothing for multivariate state space models Journal of Time Series Analysis. 21: 281-296. DOI: 10.1111/1467-9892.00186 |
0.413 |
|
| 2000 |
Durbin J, Koopman SJ. Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives Journal of the Royal Statistical Society Series B-Statistical Methodology. 62: 3-56. DOI: 10.1111/1467-9868.00218 |
0.476 |
|
| 2000 |
Harvey A, Koopman SJ. Signal extraction and the formulation of unobserved components models Econometrics Journal. 3: 84-107. DOI: 10.1111/1368-423X.00040 |
0.627 |
|
| 1999 |
Koopman SJ, Shephard N, Doornik JA. Statistical algorithms for models in state space using SsfPack 2.2 Econometrics Journal. 2: 107-160. DOI: 10.1111/1368-423X.00023 |
0.502 |
|
| 1998 |
Sandmann G, Koopman SJ. Estimation of stochastic volatility models via Monte Carlo maximum likelihood Journal of Econometrics. 87: 271-301. DOI: 10.1016/S0304-4076(98)00016-5 |
0.496 |
|
| 1997 |
Harvey A, Koopman SJ, Riani M. The modeling and seasonal adjustment of weekly observations Journal of Business & Economic Statistics. 15: 354-368. DOI: 10.1080/07350015.1997.10524713 |
0.647 |
|
| 1997 |
Koopman SJ. Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models Journal of the American Statistical Association. 92: 1630-1638. DOI: 10.1080/01621459.1997.10473685 |
0.431 |
|
| 1997 |
Atkinson AC, Koopman SJ, Shephard N. Detecting shocks: Outliers and breaks in time series Journal of Econometrics. 80: 387-422. DOI: 10.1016/S0304-4076(97)00050-X |
0.465 |
|
| 1996 |
Harvey A, Koopman SJ. Structural time series models in medicine. Statistical Methods in Medical Research. 5: 23-49. PMID 8743077 DOI: 10.1177/096228029600500103 |
0.677 |
|
| 1996 |
Koopman SJ. Stamp 5.0 : structural time series analyser, modeller and predictor The Economic Journal. 106: 1106. DOI: 10.2307/2235399 |
0.441 |
|
| 1993 |
Koopman SJ. Disturbance smoother for state space models Biometrika. 80: 117-126. DOI: 10.1093/Biomet/80.1.117 |
0.472 |
|
| 1993 |
Harvey A, Koopman SJ. Forecasting Hourly Electricity Demand Using Time-Varying Splines Journal of the American Statistical Association. 88: 1228-1236. DOI: 10.1080/01621459.1993.10476402 |
0.665 |
|
| 1992 |
Koopman SJ, Shephard N. Exact score for time series models in state space form Biometrika. 79: 823-826. DOI: 10.1093/Biomet/79.4.823 |
0.416 |
|
| 1992 |
Harvey AC, Koopman SJ. Diagnostic checking of unobserved- components time series models Journal of Business and Economic Statistics. 10: 377-389. DOI: 10.1080/07350015.1992.10509913 |
0.672 |
|
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