| Univariate and multivariate stochastic volatility models: estimation and diagnostics R Liesenfeld, JF Richard Journal of empirical finance 10 (4), 505-531, 2003 | 332 | 2003 |
| Time series of count data: modeling, estimation and diagnostics RC Jung, M Kukuk, R Liesenfeld Computational Statistics & Data Analysis 51 (4), 2350-2364, 2006 | 224 | 2006 |
| Stochastic volatility models: conditional normality versus heavy‐tailed distributions R Liesenfeld, RC Jung Journal of applied Econometrics 15 (2), 137-160, 2000 | 217 | 2000 |
| The conditional autoregressive Wishart model for multivariate stock market volatility V Golosnoy, B Gribisch, R Liesenfeld Journal of Econometrics 167 (1), 211-223, 2012 | 200 | 2012 |
| A generalized bivariate mixture model for stock price volatility and trading volume R Liesenfeld Journal of econometrics 104 (1), 141-178, 2001 | 171 | 2001 |
| Dynamic bivarsate mixture models: Modeling the behavior of prices and trading volume R Liesenfeld Journal of Business & Economic Statistics 16 (1), 101-109, 1998 | 125 | 1998 |
| Classical and Bayesian analysis of univariate and multivariate stochastic volatility models R Liesenfeld, JF Richard Econometric Reviews 25 (2-3), 335-360, 2006 | 98 | 2006 |
| Modelling financial transaction price movements: a dynamic integer count data model R Liesenfeld, I Nolte, W Pohlmeier Empirical Economics 30, 795-825, 2006 | 97 | 2006 |
| Dynamic factor models for multivariate count data: An application to stock-market trading activity RC Jung, R Liesenfeld, JF Richard Journal of Business & Economic Statistics 29 (1), 73-85, 2011 | 71 | 2011 |
| Intra-daily volatility spillovers in international stock markets V Golosnoy, B Gribisch, R Liesenfeld Journal of International Money and Finance 53, 95-114, 2015 | 68 | 2015 |
| Efficient likelihood evaluation of state-space representations DN DeJong, R Liesenfeld, GV Moura, JF Richard, H Dharmarajan Review of Economic Studies 80 (2), 538-567, 2013 | 57 | 2013 |
| Improving MCMC, using efficient importance sampling R Liesenfeld, JF Richard Computational statistics & data analysis 53 (2), 272-288, 2008 | 45 | 2008 |
| Estimating time series models for count data using efficient importance sampling RC Jung, R Liesenfeld AStA Advances in Statistical Analysis 4 (85), 387-407, 2001 | 43 | 2001 |
| Likelihood evaluation of high-dimensional spatial latent Gaussian models with non-Gaussian response variables R Liesenfeld, JF Richard, J Vogler Spatial Econometrics: Qualitative and Limited Dependent Variables 37, 35-77, 2016 | 29* | 2016 |
| A nonlinear forecasting model of GDP growth DN DeJong, R Liesenfeld, JF Richard Review of Economics and Statistics 87 (4), 697-708, 2005 | 29 | 2005 |
| Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes R Liesenfeld, JF Richard, J Vogler Journal of applied econometrics 32 (3), 600-620, 2017 | 26 | 2017 |
| Efficient estimation of probit models with correlated errors R Liesenfeld, JF Richard Journal of Econometrics 156 (2), 367-376, 2010 | 26 | 2010 |
| Determinants and dynamics of current account reversals: An empirical analysis R Liesenfeld, G Valle Moura, JF Richard Oxford Bulletin of Economics and Statistics 72 (4), 486-517, 2010 | 23 | 2010 |
| Monte Carlo methods and Bayesian computation: importance sampling R Liesenfeld, JF Richard Elsevier, 2015 | 20 | 2015 |
| Estimation of dynamic bivariate mixture models: comments on Watanabe (2000) R Liesenfeld, JF Richard Journal of Business & Economic Statistics 21 (4), 570-576, 2003 | 18 | 2003 |
Jean-Francois RichardProfessor of EconomicsVerificeret mail på pitt.edu
