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on Econometric Time Series |
| By: | Michal Andrle; Jan Bruha |
| Abstract: | We propose an algorithm to estimate unobserved states and shocks in a state-space model under sparsity constraints. Many economic models have a linear state-space form - for example, linearized DSGE models, VARs, time-varying VARs, and dynamic factor models. Under the conventional Kalman filter, which is essentially a recursive OLS algorithm, all estimated shocks are non-zero. However, the true shocks are often zero for multiple periods, and non-zero estimates are due to noisy data or ill-conditioning of the model. We show applications where sparsity is the natural solution. Sparsity of filtered shocks is achieved by applying an elastic-net penalty to the least-squares problem and improves statistical efficiency. The algorithm can be adapted for non-convex penalties and for estimates robust to outliers. |
| Keywords: | Kalman filter, regularization, sparsity |
| JEL: | C32 C52 C53 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:cnb:wpaper:2023/13&r=ets |
| By: | Ardia, David; Dufays, Arnaud; Ordás Criado, Carlos |
| Abstract: | We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting. |
| Keywords: | Change-point; Minimum description length; Model selection/combination; Structural change. |
| JEL: | C11 C12 C22 C32 C52 C53 |
| Date: | 2023–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119486&r=ets |
| By: | Xiaosai Liao; Xinjue Li; Qingliang Fan |
| Abstract: | We propose a robust hypothesis testing procedure for the predictability of multiple predictors that could be highly persistent. Our method improves the popular extended instrumental variable (IVX) testing (Phillips and Lee, 2013; Kostakis et al., 2015) in that, besides addressing the two bias effects found in Hosseinkouchack and Demetrescu (2021), we find and deal with the variance-enlargement effect. We show that two types of higher-order terms induce these distortion effects in the test statistic, leading to significant over-rejection for one-sided tests and tests in multiple predictive regressions. Our improved IVX-based test includes three steps to tackle all the issues above regarding finite sample bias and variance terms. Thus, the test statistics perform well in size control, while its power performance is comparable with the original IVX. Monte Carlo simulations and an empirical study on the predictability of bond risk premia are provided to demonstrate the effectiveness of the newly proposed approach. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.01064&r=ets |