Publications
2024
Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach
Hong, Y., Linton, O., McCabe, B., Sun, J., & Wang, S. (2024). Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach. Journal of Econometrics, 238(2), 105603. doi:10.1016/j.jeconom.2023.105603
2022
A score statistic for testing the presence of a stochastic trend in conditional variances
Hong, Y., Linton, O., McCabe, B., & Sun, J. (2022). A score statistic for testing the presence of a stochastic trend in conditional variances. Economics Letters, 213, 110394. doi:10.1016/j.econlet.2022.110394
A semi-parametric integer-valued autoregressive model with covariates
Rao, Y., Harris, D., & McCabe, B. (2022). A semi-parametric integer-valued autoregressive model with covariates. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 71(3), 495-516. doi:10.1111/rssc.12543
2021
An adjusted-range based self-normalization test for correlation change
Chen, J., Hong, Y., McCabe, B., & Sun, J. (2021). An adjusted-range based self-normalization test for correlation change.
Model Averaging of Integer-Valued Autoregressive Model With Covariates
Sun, J., Sun, Y., Zhang, X., & McCabe, B. (2021). Model Averaging of Integer-Valued Autoregressive Model With Covariates.
Approximate Bayesian forecasting (vol 35, pg 521, 2018)
Frazier, D. T., Maneesoonthorn, W., Martin, G. M., & McCabe, B. P. M. (2021). Approximate Bayesian forecasting (vol 35, pg 521, 2018). INTERNATIONAL JOURNAL OF FORECASTING, 37(3), 1301. Retrieved from https://www.webofscience.com/
2020
Distributions You Can Count On …But What’s the Point?
McCabe, B. P. M., & Skeels, C. L. (n.d.). Distributions You Can Count On …But What’s the Point?. Econometrics, 8(1), 9. doi:10.3390/econometrics8010009
Structural change and the problem of phantom break locations
Rao, Y., & Mccabe, B. (n.d.). Structural change and the problem of phantom break locations. The Manchester School. doi:10.1111/manc.12298
2019
Semi-Parametric Independence Testing in Count Data and the Role of the Support
Harris, D., & Mccabe, B. P. M. (2019). Semi-Parametric Independence Testing in Count Data and the Role of the Support. Econometric Theory, 35(6), 1111-1145. doi:10.1017/S0266466618000403
Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models
Martin, G. M., McCabe, B. P. M., Frazier, D. T., Maneesoonthorn, W., & Robert, C. P. (2019). Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models. Journal of Computational and Graphical Statistics. doi:10.1080/10618600.2018.1552154
2017
Approximate Bayesian Forecasting
Frazier, D. T., Maneesoonthorn, W., Martin, G. M., & McCabe, B. P. M. (2019). Approximate Bayesian forecasting. INTERNATIONAL JOURNAL OF FORECASTING, 35(2), 521-539. doi:10.1016/j.ijforecast.2018.08.003
Is MORE LESS? The Role of Data Augmentation in Testing for Structural Breaks
Rao, Y., & McCabe, B. (2017). Is MORE LESS? The Role of Data Augmentation in Testing for Structural Breaks. Economics Letters, 155, 131-134. doi:10.1016/j.econlet.2017.03.033
The effect of regression design on optimal tests for finding break positions
Rao, Y., & McCabe, B. (n.d.). The effect of regression design on optimal tests for finding break positions. SSRN: https://ssrn.com/abstract=2867141.
2016
Real-time surveillance for abnormal events: the case of influenza outbreaks
Rao, Y., & McCabe, B. (2016). Real-time surveillance for abnormal events: the case of influenza outbreaks. STATISTICS IN MEDICINE, 35(13), 2206-2220. doi:10.1002/sim.6857
2015
Real time monitoring for abnormal events: An application to influenza outbreaks
Rao, Y., & McCabe, B. (n.d.). Real time monitoring for abnormal events: An application to influenza outbreaks. Statistics in Medicine.
2014
Approximate Bayesian Computation in State Space Models
Martin, G. M., McCabe, B. P. M., Maneesoonthorn, W., & Robert, C. P. (2014). Approximate Bayesian Computation in State Space Models. Retrieved from http://arxiv.org/abs/1409.8363v1
2013
Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models
Ng, J., Forbes, C. S., Martin, G. M., & McCabe, B. P. M. (2013). Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models. International Journal of Forecasting, 29(3), 411-430. doi:10.1016/j.ijforecast.2012.10.005
Score statistics for testing serial dependence in count data
Sun, J., & McCabe, B. P. (2013). Score statistics for testing serial dependence in count data. Journal of Time Series Analysis, 34(3), 315-329. doi:10.1111/jtsa.12014
Testing for parameter constancy in non‐Gaussian time series
Han, L., & McCabe, B. (2013). Testing for parameter constancy in non‐Gaussian time series. Journal of Time Series Analysis, 34(1), 17-29. doi:10.1111/j.1467-9892.2012.00810.x
2011
Efficient Probabilistic Forecasts for Counts
McCabe, B. P. M., Martin, G. M., & Harris, D. (2011). Efficient Probabilistic Forecasts for Counts. Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(2), 253-272. doi:10.1111/j.1467-9868.2010.00762.x
A QUASI-LOCALLY MOST POWERFUL TEST FOR CORRELATION IN THE CONDITIONAL VARIANCE OF POSITIVE DATA
McCabe, B., Martin, G., & Freeland, K. (2011). A QUASI-LOCALLY MOST POWERFUL TEST FOR CORRELATION IN THE CONDITIONAL VARIANCE OF POSITIVE DATA. Australian and New Zealand Journal of Statistics (available online), 53(1), 43-62.
2008
Maximum likelihood estimation of higher‐order integer‐valued autoregressive processes
Bu, R., McCabe, B., & Hadri, K. (2008). Maximum likelihood estimation of higher‐order integer‐valued autoregressive processes. Journal of Time Series Analysis, 29(6), 973-994. doi:10.1111/j.1467-9892.2008.00590.x
Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach
Bu, R., & McCabe, B. (2008). Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach. International Journal of Forecasting, 24(1), 151-162. doi:10.1016/j.ijforecast.2007.11.002
2006
A RESIDUAL-BASED TEST FOR STOCHASTIC COINTEGRATION
McCabe, B., Leybourne, S., & Harris, D. (2006). A RESIDUAL-BASED TEST FOR STOCHASTIC COINTEGRATION. Econometric Theory, 22(03). doi:10.1017/s026646660606021x
TESTING FOR LONG MEMORY
Harris, D., McCabe, B., & Leybourne, S. (2008). TESTING FOR LONG MEMORY. Econometric Theory, 24(01). doi:10.1017/s0266466608080080
MODIFIED KPSS TESTS FOR NEAR INTEGRATION
Harris, D., Leybourne, S., & McCabe, B. (2007). MODIFIED KPSS TESTS FOR NEAR INTEGRATION. Econometric Theory, 23(02). doi:10.1017/s0266466607070156
2005
Panel Stationarity Tests for Purchasing Power Parity With Cross-Sectional Dependence
Harris, D., Leybourne, S., & McCabe, B. (2005). Panel Stationarity Tests for Purchasing Power Parity With Cross-Sectional Dependence. Journal of Business & Economic Statistics, 23(4), 395-409. doi:10.1198/073500105000000090
Asymptotic properties of CLS estimators in the Poisson AR(1) model
Keith Freeland, R., & McCabe, B. (2005). Asymptotic properties of CLS estimators in the Poisson AR(1) model. Statistics & Probability Letters, 73(2), 147-153. doi:10.1016/j.spl.2005.03.006
Bayesian predictions of low count time series
McCabe, B. P. M., & Martin, G. M. (2005). Bayesian predictions of low count time series. International Journal of Forecasting, 21(2), 315-330. doi:10.1016/j.ijforecast.2004.11.001
Assessing Persistence In Discrete Nonstationary Time‐Series Models
McCabe, B. P. M., Martin, G. M., & Tremayne, A. R. (2005). Assessing Persistence In Discrete Nonstationary Time‐Series Models. Journal of Time Series Analysis, 26(2), 305-317. doi:10.1111/j.1467-9892.2005.00402.x
2004
Analysis of Low Count Time Series Data by Poisson Autoregression
Freeland, R. K., & McCabe, B. P. M. (2004). Analysis of Low Count Time Series Data by Poisson Autoregression. Journal of Time Series Analysis, 25(5), 701-722.
Forecasting discrete valued low count time series
Freeland, R. K., & McCabe, B. P. M. (2004). Forecasting discrete valued low count time series. International Journal of Forecasting, 20(3), 427-434. doi:10.1016/s0169-2070(03)00014-1
Analysis of Count Data by means of the Poisson Autoregressive Model.
McCabe, B. P. M., & Freeland, K. (2004). Analysis of Count Data by means of the Poisson Autoregressive Model.. Journal of Time Series Analysis., 25(5), 701-722.
2003
SOME LIMIT THEORY FOR AUTOCOVARIANCES WHOSE ORDER DEPENDS ON SAMPLE SIZE
Harris, D., McCabe, B., & Leybourne, S. (2003). SOME LIMIT THEORY FOR AUTOCOVARIANCES WHOSE ORDER DEPENDS ON SAMPLE SIZE. Econometric Theory, 19(05). doi:10.1017/s0266466603195060
2002
Stochastic cointegration: estimation and inference
Harris, D., McCabe, B., & Leybourne, S. (2002). Stochastic cointegration: estimation and inference. Journal of Econometrics, 111(2), 363-384. doi:10.1016/s0304-4076(02)00111-2
2000
A general Method of Testing for Random Parameter Variation in Statistical Models.
McCabe, B. P. M., & Leybourne, S. J. (2000). A general Method of Testing for Random Parameter Variation in Statistical Models.. In R. D. H. Heijmans, D. S. G. Pollock, & A. Satorra (Eds.), Innovations in Multivariate Statistical Analysis: A Festschrift for Heinz Neudecker. (pp. 75-85). Amsterdam: Kluwer.