Hostname: page-component-6bf8c574d5-pdxrj Total loading time: 0 Render date: 2025-03-04T00:04:41.351Z Has data issue: false hasContentIssue false

REAL-TIME MONITORING WITH RCA MODELS

Published online by Cambridge University Press:  03 March 2025

Lajos Horváth
Affiliation:
University of Utah
Lorenzo Trapani*
Affiliation:
Universita’ di Pavia and University of Leicester
*
Address correspondence to Lorenzo Trapani, Department of Economics and Management, University of Pavia, Via San Felice al Monastero 5, 27100 Pavia, Italy, e-mail: lorenzo.trapani@unipv.it.

Abstract

We propose a family of weighted statistics based on the CUSUM process of the WLS residuals for the online detection of changepoints in a Random Coefficient Autoregressive model, using both the standard CUSUM and the Page-CUSUM process. We derive the asymptotics under the null of no changepoint for all possible weighing schemes, including the case of the standardized CUSUM, for which we derive a Darling–Erdös-type limit theorem; our results guarantee the procedure-wise size control under both an open-ended and a closed-ended monitoring. In addition to considering the standard RCA model with no covariates, we also extend our results to the case of exogenous regressors. Our results can be applied irrespective of (and with no prior knowledge required as to) whether the observations are stationary or not, and irrespective of whether they change into a stationary or nonstationary regime. Hence, our methodology is particularly suited to detect the onset, or the collapse, of a bubble or an epidemic. Our simulations show that our procedures, especially when standardising the CUSUM process, can ensure very good size control and short detection delays. We complement our theory by studying the online detection of breaks in epidemiological and housing prices series.

Type
ARTICLES
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We are grateful to the Editor, P.C.B. Phillips, the Co-Editor, G. Cavaliere, and three anonymous referees for very constructive feedback. All remaining errors are ours.

References

REFERENCES

Akharif, A., & Hallin, M. (2003). Efficient detection of random coefficients in autoregressive models. Annals of Statistics , 31(2), 675704.CrossRefGoogle Scholar
Anděl, J. (1976). Autoregressive series with random parameters. Mathematische Operationsforschung und Statistik , 7(5), 735741.CrossRefGoogle Scholar
Astill, S., Taylor, A. R., Kellard, N., & Korkos, I. (2023). Using covariates to improve the efficacy of univariate bubble detection methods. Journal of Empirical Finance , 70, 342366.CrossRefGoogle Scholar
Aue, A., Hörmann, S., Horváth, L., & Reimherr, M. (2009). Break detection in the covariance structure of multivariate time series models. Annals of Statistics , 37(6B), 40464087.CrossRefGoogle Scholar
Aue, A., & Horváth, L. (2004). Delay time in sequential detection of change. Statistics & Probability Letters , 67(3), 221231.CrossRefGoogle Scholar
Aue, A., & Horváth, L. (2011). Quasi-likelihood estimation in stationary and nonstationary autoregressive models with random coefficients. Statistica Sinica , 21(3), 973999.Google Scholar
Aue, A., Horváth, L., Kokoszka, P., & Steinebach, J. (2008). Monitoring shifts in mean: asymptotic normality of stopping times. Test , 17, 515530.CrossRefGoogle Scholar
Aue, A., Horváth, L., & Steinebach, J. (2006). Estimation in random coefficient autoregressive models. Journal of Time Series Analysis , 27(1), 6176.CrossRefGoogle Scholar
Aue, A., & Kirch, C. (2024). The state of cumulative sum sequential changepoint testing 70 years after page. Biometrika , 111(2), 367391.CrossRefGoogle Scholar
Berkes, I., Hörmann, S., & Schauer, J. (2011). Split invariance principles for stationary processes. Annals of Probability , 39(6), 24412473.CrossRefGoogle Scholar
Berkes, I., Horváth, L., & Ling, S. (2009). Estimation in nonstationary random coefficient autoregressive models. Journal of Time Series Analysis , 30(4), 395416.CrossRefGoogle Scholar
Bollerslev, T., Patton, A. J., & Wang, W. (2016). Daily house price indices: Construction, modeling, and longer-run predictions. Journal of Applied Econometrics , 31(6), 10051025.CrossRefGoogle Scholar
Casini, A., & Perron, P. (2019). Structural breaks in time series. In Oxford research encyclopedia of economics and finance . Oxford University Press, Doi: https://doi.org/10.1093/acrefore/9780190625979.013.179.Google Scholar
Cavaliere, G., & Rahbek, A. (2021). A primer on bootstrap testing of hypotheses in time series models: With an application to double autoregressive models. Econometric Theory , 37(1), 148.CrossRefGoogle Scholar
Chu, C., Stinchcombe, M., & White, H. (1996). Monitoring structural change. Econometrica 64(5), 10451066.CrossRefGoogle Scholar
Conlisk, J. (1974). Stability in a random coefficient model. International Economic Review , 15(2), 529533.CrossRefGoogle Scholar
Csörgő, M., & Horváth, L. (1993). Weighted approximations in probability and statistics . John Wiley & Sons.Google Scholar
Darling, D. A., & Erdős, P. (1956). A limit theorem for the maximum of normalized sums of independent random variables. Duke Mathematical Journal , 23(1), 143155.CrossRefGoogle Scholar
Diba, B. T., & Grossman, H. I. (1988). The theory of rational bubbles in stock prices. The Economic Journal , 98(392), 746754.CrossRefGoogle Scholar
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica , 50(4), 9871007.CrossRefGoogle Scholar
Fan, J., & Yao, Q. (2008). Nonlinear time series: Nonparametric and parametric methods . Springer Science & Business Media.Google Scholar
Fremdt, S. (2015). Page’s sequential procedure for change-point detection in time series regression. Statistics , 49(1), 128155.CrossRefGoogle Scholar
Ghysels, E. (2018). Mixed frequency models. In Oxford research encyclopedia of economics and finance . Oxford University Press, Doi: https://doi.org/10.1093/acrefore/9780190625979.013.176.CrossRefGoogle Scholar
Gombay, E., & Horváth, L. (1996). On the rate of approximations for maximum likelihood tests in change-point models. Journal of Multivariate Analysis , 56(1), 120152.CrossRefGoogle Scholar
Granger, C. W., & Swanson, N. R. (1997). An introduction to stochastic unit-root processes. Journal of Econometrics , 80(1), 3562.CrossRefGoogle Scholar
Harvey, D. I., Leybourne, S. J., & Sollis, R. (2017). Improving the accuracy of asset price bubble start and end date estimators. Journal of Empirical Finance , 40, 121138.CrossRefGoogle Scholar
Hill, J., Li, D., & Peng, L. (2016). Uniform interval estimation for an AR(1) process with AR errors. Statistica Sinica , 26(1), 119136.Google Scholar
Hill, J., & Peng, L. (2014). Unified interval estimation for random coefficient autoregressive models. Journal of Time Series Analysis , 35(3), 282297.CrossRefGoogle Scholar
Homm, U., & Breitung, J. (2012). Testing for speculative bubbles in stock markets: A comparison of alternative methods. Journal of Financial Econometrics , 10(1), 198231.CrossRefGoogle Scholar
Horváth, L., Hušková, M., Kokoszka, P., & Steinebach, J. (2004). Monitoring changes in linear models. Journal of Statistical Planning and Inference , 126(1), 225251.CrossRefGoogle Scholar
Horváth, L., Kokoszka, P., & Steinebach, J. (2007). On sequential detection of parameter changes in linear regression. Statistics and Probability Letters , 80, 18061813.Google Scholar
Horváth, L., Liu, Z., & Lu, S. (2022). Sequential monitoring of changes in dynamic linear models, applied to the US housing market. Econometric Theory 38(2), 209272.CrossRefGoogle Scholar
Horváth, L., & Trapani, L. (2016). Statistical inference in a random coefficient panel model. Journal of Econometrics , 193(1), 5475.CrossRefGoogle Scholar
Horváth, L., & Trapani, L. (2019). Testing for randomness in a random coefficient autoregression model. Journal of Econometrics , 209(2), 338352.CrossRefGoogle Scholar
Horváth, L., & Trapani, L. (2023a). Changepoint detection in heteroscedastic random coefficient autoregressive models. Journal of Business & Economic Statistics , 41(4), 13001314.CrossRefGoogle Scholar
Horváth, L., & Trapani, L. (2023b). Lp-functionals for change point detection in random coefficient autoregressive models. Statistics & Probability Letters , 201, 109829.CrossRefGoogle Scholar
Horváth, L., Trapani, L., & VanderDoes, J. (2024). The maximally selected likelihood ratio test in random coefficient models. The Econometrics Journal , 27(3), 384411.CrossRefGoogle Scholar
Horvath, L., Trapani, L., & Wang, S. (2024). Sequential monitoring for explosive volatility regimes. arXiv preprint arXiv:2404.17885.Google Scholar
Ibragimov, I. A. (1962). Some limit theorems for stationary processes. Theory of Probability & Its Applications , 7(4), 349382.CrossRefGoogle Scholar
Kirch, C., & Stoehr, C. (2022a). Asymptotic delay times of sequential tests based on U-statistics for early and late change points. Journal of Statistical Planning and Inference , 221, 114135.CrossRefGoogle Scholar
Kirch, C., & Stoehr, C. (2022b). Sequential change point tests based on u-statistics. Scandinavian Journal of Statistics , 49(3), 11841214.CrossRefGoogle Scholar
Koul, H. L., & Schick, A. (1996). Adaptive estimation in a random coefficient autoregressive model. Annals of Statistics , 24(3), 10251052.CrossRefGoogle Scholar
Kurozumi, E. (2017). Monitoring parameter constancy with endogenous regressors. Journal of Time Series Analysis , 38(5), 791805.CrossRefGoogle Scholar
Lewis, D. J., Mertens, K., Stock, J. H., & Trivedi, M. (2022). Measuring real activity using a weekly economic index. Journal of Applied Econometrics , 37(4), 667687.CrossRefGoogle Scholar
Leybourne, S. J., McCabe, B. P., & Tremayne, A. R. (1996). Can economic time series be differenced to stationarity? Journal of Business & Economic Statistics , 14(4), 435446.CrossRefGoogle Scholar
Li, F., Tian, Z., and Qi, P. (2015). Structural change monitoring for random coefficient autoregressive time series. Communications in Statistics-Simulation and Computation , 44(4), 9961009.CrossRefGoogle Scholar
Li, F., Tian, Z., Qi, P., & Chen, Z. (2015). Monitoring parameter changes in RCA(p) models. Journal of the Korean Statistical Society , 44(1), 111122.CrossRefGoogle Scholar
Ling, S. (2004). Estimation and testing stationarity for double-autoregressive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , 66(1), 6378.CrossRefGoogle Scholar
Liu, W., & Lin, Z. (2009). Strong approximation for a class of stationary processes. Stochastic Processes and their Applications , 119(1), 249280.CrossRefGoogle Scholar
Na, O., Lee, J., & Lee, S. (2010). Monitoring parameter changes for random coefficient autoregressive models. Journal of the Korean Statistical Society , 39(3), 281288.CrossRefGoogle Scholar
Nicholls, D. F., & Quinn, B. G. (2012). Random coefficient autoregressive models: An introduction . Vol. 11. Springer Science & Business Media.Google Scholar
Otto, S., & Breitung, J. (2023). Backward cusum for testing and monitoring structural change with an application to covid-19 pandemic data. Econometric Theory , 39(4), 659692.CrossRefGoogle Scholar
Perron, P., & Yamamoto, Y. (2014). A note on estimating and testing for multiple structural changes in models with endogenous regressors via 2sls. Econometric Theory , 30(2), 491507.CrossRefGoogle Scholar
Phillips, P. C. B., & Shi, S. (2018). Financial bubble implosion and reverse regression. Econometric Theory , 34(4), 705753.CrossRefGoogle Scholar
Phillips, P. C. B., Shi, S., & Yu, J. (2015a). Testing for multiple bubbles: historical episodes of exuberance and collapse in the S&P 500. International Economic Review , 56(4), 10431078.CrossRefGoogle Scholar
Phillips, P. C. B., Shi, S., & Yu, J. (2015b). Testing for multiple bubbles: limit theory of real-time detectors. International Economic Review , 56(4), 10791134.CrossRefGoogle Scholar
Phillips, P. C. B., Wu, Y., & Yu, J. (2011). Explosive behavior in the 1990s Nasdaq: when did exuberance escalate asset values? International Economic Review , 52(1), 201226.CrossRefGoogle Scholar
Phillips, P. C. B., & Yu, J. (2011). Dating the timeline of financial bubbles during the subprime crisis. Quantitative Economics , 2(3), 455491.CrossRefGoogle Scholar
Regis, M., Serra, P., & van den Heuvel, E. R. (2022). Random autoregressive models: A structured overview. Econometric Reviews , 41(2), 207230.CrossRefGoogle Scholar
Schick, A. (1996). $\sqrt{n}$ -consistent estimation in a random coefficient autoregressive model. Australian & New Zealand Journal of Statistics , 38(2), 155160.Google Scholar
Shtatland, E. S., & Shtatland, T. (2008). Another look at low-order autoregressive models in early detection of epidemic outbreaks and explosive behaviors in economic and financial time series. In SGF proceedings . 363.Google Scholar
Skrobotov, A. (2023). Testing for explosive bubbles: A review. Dependence Modeling , 11(1), 20220152.CrossRefGoogle Scholar
Trapani, L. (2021). Testing for strict stationarity in a random coefficient autoregressive model. Econometric Reviews .CrossRefGoogle Scholar
Tsay, R. S. (1987). Conditional heteroscedastic time series models. Journal of the American Statistical Association , 82(398), 590604.CrossRefGoogle Scholar
Whitehouse, E. J., Harvey, D. I., & Leybourne, S. J. (2023). Real-time monitoring of bubbles and crashes. Oxford Bulletin of Economics and Statistics , 85(3), 482513.CrossRefGoogle Scholar
Wood, S. N. (2022). Inferring UK Covid-19 fatal infection trajectories from daily mortality data: were infections already in decline before the UK lockdowns? Biometrics , 78(3), 11271140.CrossRefGoogle ScholarPubMed
Wu, W. B. (2005). Nonlinear system theory: another look at dependence. Proceedings of the National Academy of Sciences of the United States of America , 102(40), 1415014154.CrossRefGoogle Scholar
Supplementary material: File

Horváth and Trapani supplementary material

Horváth and Trapani supplementary material
Download Horváth and Trapani supplementary material(File)
File 678.5 KB