| Forecasting: theory and practice F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ... International Journal of forecasting 38 (3), 705-871, 2022 | 919 | 2022 |
| US deficit sustainability: A new approach based on multiple endogenous breaks GM Martin Journal of applied econometrics 15 (1), 83-105, 2000 | 241 | 2000 |
| Asymptotic properties of approximate Bayesian computation DT Frazier, GM Martin, CP Robert, J Rousseau Biometrika 105 (3), 593-607, 2018 | 140 | 2018 |
| Bayesian predictions of low count time series BPM McCabe, GM Martin International Journal of Forecasting 21 (2), 315-330, 2005 | 131 | 2005 |
| Bayesian analysis of the stochastic conditional duration model CM Strickland, CS Forbes, GM Martin Computational statistics & data analysis 50 (9), 2247-2267, 2006 | 85 | 2006 |
| Efficient probabilistic forecasts for counts BPM McCabe, GM Martin, D Harris Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2011 | 79 | 2011 |
| Auxiliary likelihood-based approximate Bayesian computation in state space models GM Martin, BPM McCabe, DT Frazier, W Maneesoonthorn, CP Robert Journal of Computational and Graphical Statistics 28 (3), 508-522, 2019 | 68* | 2019 |
| Parametric pricing of higher order moments in S&P500 options GC Lim, GM Martin, VL Martin Journal of Applied Econometrics 20 (3), 377-404, 2005 | 61 | 2005 |
| Inference for a class of stochastic volatility models using option and spot prices: Application of a bivariate Kalman filter CS Forbes, GM Martin, J Wright Econometric Reviews 26 (2-4), 387-418, 2007 | 50* | 2007 |
| Approximate bayesian forecasting DT Frazier, W Maneesoonthorn, GM Martin, BPM McCabe International Journal of Forecasting 35 (2), 521-539, 2019 | 49 | 2019 |
| Focused Bayesian prediction R Loaiza‐Maya, GM Martin, DT Frazier Journal of Applied Econometrics 36 (5), 517-543, 2021 | 45 | 2021 |
| Inference on self‐exciting jumps in prices and volatility using high‐frequency measures W Maneesoonthorn, CS Forbes, GM Martin Journal of Applied Econometrics 32 (3), 504-532, 2017 | 44 | 2017 |
| Parameterisation and efficient MCMC estimation of non-Gaussian state space models CM Strickland, GM Martin, CS Forbes Computational Statistics & Data Analysis 52 (6), 2911-2930, 2008 | 37 | 2008 |
| Computing Bayes: Bayesian computation from 1763 to the 21st century GM Martin, DT Frazier, CP Robert arXiv preprint arXiv:2004.06425, 2020 | 35 | 2020 |
| Simulation-based Bayesian estimation of an affine term structure model AD Sanford, GM Martin Computational statistics & data analysis 49 (2), 527-554, 2005 | 33 | 2005 |
| Approximating Bayes in the 21st century GM Martin, DT Frazier, CP Robert Statistical Science 39 (1), 20-45, 2024 | 30 | 2024 |
| High-frequency jump tests: Which test should we use? W Maneesoonthorn, GM Martin, CS Forbes Journal of Econometrics 219 (2), 478-487, 2020 | 30 | 2020 |
| The distribution of exchange rate returns and the pricing of currency options GC Lim, JN Lye, GM Martin, VL Martin Journal of International Economics 45 (2), 351-368, 1998 | 30 | 1998 |
| Bayesian forecasting in economics and finance: A modern review GM Martin, DT Frazier, W Maneesoonthorn, R Loaiza-Maya, F Huber, ... International Journal of Forecasting 40 (2), 811-839, 2024 | 28 | 2024 |
| Does the option market produce superior forecasts of noise‐corrected volatility measures? G M. Martin, A Reidy, J Wright Journal of Applied Econometrics 24 (1), 77-104, 2009 | 26 | 2009 |
David Tyler FrazierMonash University, Department of Econometrics and Business StatisticsВерификована је имејл адреса на monash.edu
