|
on Econometric Time Series |
| By: | Wei Zhang |
| Abstract: | High-dimensional matrix-valued time series are of significant interest in economics and finance, with prominent examples including cross region macroeconomic panels and firms' financial data panels. We introduce a class of Bayesian matrix dynamic factor models that utilize matrix structures to identify more interpretable factor patterns and factor impacts. Our model accommodates time-varying volatility, adjusts for outliers, and allows cross-sectional correlations in the idiosyncratic components. To determine the dimension of the factor matrix, we employ an importance-sampling estimator based on the cross-entropy method to estimate marginal likelihoods. Through a series of Monte Carlo experiments, we show the properties of the factor estimators and the performance of the marginal likelihood estimator in correctly identifying the true dimensions of the factor matrices. Applying our model to a macroeconomic dataset and a financial dataset, we demonstrate its ability in unveiling interesting features within matrix-valued time series. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08354 |
| By: | Francisco Blasques (Vrije Universiteit Amsterdam); Janneke van Brummelen (Vrije Universiteit Amsterdam); Paolo Gorgi (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam) |
| Abstract: | The equivalence of the Beveridge-Nelson decomposition and the trend-cycle decomposition is well established. In this paper we argue that this equivalence is almost immediate when a Gaussian score-driven location model is considered. We also provide a natural extension towards heavy-tailed distributions for the disturbances which lead to a robust version of the Beveridge-Nelson decomposition. |
| Keywords: | trend and cycle, filtering, autoregressive integrated moving average model, score-driven model, heavy-tailed distributions |
| JEL: | C22 E32 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240003 |
| By: | Martin C. Arnold; Thilo Reinschl\"ussel |
| Abstract: | We propose sieve wild bootstrap analogues to the adaptive Lasso solution path unit root tests of Arnold and Reinschl\"ussel (2024) arXiv:2404.06205 to improve finite sample properties and extend their applicability to a generalised framework, allowing for non-stationary volatility. Numerical evidence shows the bootstrap to improve the tests' precision for error processes that promote spurious rejections of the unit root null, depending on the detrending procedure. The bootstrap mitigates finite-sample size distortions and restores asymptotically valid inference when the data features time-varying unconditional variance. We apply the bootstrap tests to real residential property prices of the top six Eurozone economies and find evidence of stationarity to be period-specific, supporting the conjecture that exuberance in the housing market characterises the development of Euro-era residential property prices in the recent past. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.07859 |
| By: | Omer Faruk Akbal |
| Abstract: | This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good trade-off between accuracy and speed, which makes it especially useful for large dimensional data. Unlike traditional numerical maximization approaches, this methodology benefits from closed-form solutions for parameter estimation, enhancing its practicality for real-time applications and historical data exercises with focus on frequent updates. In a nowcasting application to vintage US data, I study the information content and relative performance of regime-switching model after each data releases in a fifteen year period, which was only feasible due to the time efficiency of the proposed estimation methodology. While existing literature has already acknowledged the performance improvement of nowcasting models under regime-switching, this paper shows that the superior nowcasting performance observed particularly when key economic indicators are released. In a backcasting exercise, I show that the model can closely match the recession starting and ending dates of the NBER despite having less information than actual committee meetings, where the fit between actual dates and model estimates becomes more apparent with the additional available information and recession end dates are fully covered with a lag of three to six months. Given that the EM algorithm proposed in this paper is suitable for various regime-switching configurations, this paper provides economists and policymakers with a valuable tool for conducting comprehensive analyses, ranging from point estimates to information decomposition and persistence of recessions in larger datasets. |
| Date: | 2024–09–06 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/190 |
| By: | Zhan Gao; Ji Hyung Lee; Ziwei Mei; Zhentao Shi |
| Abstract: | LASSO introduces shrinkage bias into estimated coefficients, which can adversely affect the desirable asymptotic normality and invalidate the standard inferential procedure based on the $t$-statistic. The desparsified LASSO has emerged as a well-known remedy for this issue. In the context of high dimensional predictive regression, the desparsified LASSO faces an additional challenge: the Stambaugh bias arising from nonstationary regressors. To restore the standard inferential procedure, we propose a novel estimator called IVX-desparsified LASSO (XDlasso). XDlasso eliminates the shrinkage bias and the Stambaugh bias simultaneously and does not require prior knowledge about the identities of nonstationary and stationary regressors. We establish the asymptotic properties of XDlasso for hypothesis testing, and our theoretical findings are supported by Monte Carlo simulations. Applying our method to real-world applications from the FRED-MD database -- which includes a rich set of control variables -- we investigate two important empirical questions: (i) the predictability of the U.S. stock returns based on the earnings-price ratio, and (ii) the predictability of the U.S. inflation using the unemployment rate. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.10030 |