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on Econometric Time Series |
| By: | Lucas P. Harlaar (Vrije Universiteit Amsterdam); Jacques J.F. Commandeur (Vrije Universiteit Amsterdam); Jan A. van den Brakel (Maastricht University); Siem Jan Koopman (Vrije Universiteit Amsterdam); Niels Bos (SWOV Institute for Road Safety Research); Frits D. Bijleveld (Vrije Universiteit Amsterdam) |
| Abstract: | This paper investigates the feasibility of using earlier provisional data to improve the now- and forecasting accuracy of final and official statistics. We propose the use of a multivariate structural time series model which includes common trends and seasonal components to combine official statistics series with related auxiliary series. In this way, more precise and more timely nowcasts and forecasts of the official statistics can be obtained by exploiting the higher frequency and/or the more timely availability of the auxiliary series. The proposed method can be applied to different data sources consisting of any number of missing observations both at the beginning and at the end of the series simultaneously. Two empirical applications are presented. The first one focuses on fatal traffic accidents and the second one on labour force participation at the municipal level. The results demonstrate the effectiveness of our proposed approach in improving forecasting performance for the target series and providing early warnings to policy-makers. |
| Keywords: | nowcasting, multivariate structural time series model, seemingly unrelated time series equations, Kalman filter, road fatalities, labour market statistics |
| JEL: | C32 |
| Date: | 2024–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240037 |
| By: | F. Blasques (Vrije Universiteit Amsterdam); S.J. Koopman (Vrije Universiteit Amsterdam); G. Mingoli (Vrije Universiteit Amsterdam); S. Telg (Vrije Universiteit Amsterdam) |
| Abstract: | In economics and finance, speculative bubbles take the form of locally explosive dynamics that eventually collapse. We propose a test for the presence of speculative bubbles in the context of mixed causal-noncausal autoregressive processes. The test exploits the fact that bubbles are anticipative, that is, they are generated by an extreme shock in the forward- looking dynamics. In particular, the test uses both path level deviations and growth rates to assess the presence of a bubble of given duration and size, at any moment of time. We show that the distribution of the test statistic can be either analytically determined or numerically approximated, depending on the error distribution. Size and power properties of the test are analyzed in controlled Monte Carlo experiments. An empirical application is presented for a monthly oil price index. It demonstrates the ability of the test to detect bubbles and to provide valuable insights in terms of risk assessments in the spirit of Value-at-Risk. |
| Keywords: | noncausality, bubbles, testing, date-stamping, risk assessment |
| JEL: | C22 E31 E37 |
| Date: | 2024–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240036 |
| By: | Artem Prokhorov; Peter Radchenko; Alexander Semenov; Anton Skrobotov |
| Abstract: | We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the $l_0$-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding provably optimal solutions using a well-known optimization solver. Compared to the popular $l_1$-penalized regression (LASSO) and other classical methods, the MIO framework permits simultaneous estimation of the number and location of structural breaks as well as regression coefficients, while accommodating the option of specifying a given or minimal number of breaks. We derive the asymptotic properties of the estimator and demonstrate its effectiveness through extensive numerical experiments, confirming a more accurate estimation of multiple breaks as compared to popular non-MIO alternatives. Two empirical examples demonstrate usefulness of the framework in applications from business and economic statistics. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.05665 |
| By: | Ranieri Dugo (DEF, University of Rome "Tor Vergata"); Giacomo Giorgio (Dept of Mathematics, University of Rome "Tor Vergata"); Paolo Pigato (DEF, University of Rome "Tor Vergata") |
| Abstract: | Starting from the notion of multivariate fractional Brownian Motion introduced in [F. Lavancier, A. Philippe, and D. Surgailis. Covariance function of vector self-similar processes. Statistics & Probability Letters, 2009] we define a multivariate version of the fractional Ornstein-Uhlenbeck process. This multivariate Gaussian process is stationary, ergodic and allows for different Hurst exponents on each component. We characterize its correlation matrix and its short and long time asymptotics. Besides the marginal parameters, the cross correlation between one-dimensional marginal components is ruled by two parameters. We consider the problem of their inference, proposing two types of estimator, constructed from discrete observations of the process. We establish their asymptotic theory, in one case in the long time asymptotic setting, in the other case in the infill and long time asymptotic setting. The limit behavior can be asymptotically Gaussian or non-Gaussian, depending on the values of the Hurst exponents of the marginal compo-nents. The technical core of the paper relies on the analysis of asymptotic properties of functionals of Gaussian processes, that we establish using Malliavin calculus and Stein's method. We provide numerical experiments that support our theoretical analysis and also suggest a conjecture on the application of one of these estimators to the multivariate fractional Brownian Motion. |
| Keywords: | Fractional process, multivariate process, ergodic process, long-range dependence, cross-correlation, parameters inference, rough volatility. |
| Date: | 2024–08–28 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:581 |