|
on Econometric Time Series |
| By: | Jinyuan Chang; Qiao Hu; Zhentao Shi; Jia Zhang |
| Abstract: | Economic and financial models -- such as vector autoregressions, local projections, and multivariate volatility models -- feature complex dynamic interactions and spillovers across many time series. These models can be integrated into a unified framework, with high-dimensional parameters identified by moment conditions. As the number of parameters and moment conditions may surpass the sample size, we propose adding a double penalty to the empirical likelihood criterion to induce sparsity and facilitate dimension reduction. Notably, we utilize a marginal empirical likelihood approach despite temporal dependence in the data. Under regularity conditions, we provide asymptotic guarantees for our method, making it an attractive option for estimating large-scale multivariate time series models. We demonstrate the versatility of our procedure through extensive Monte Carlo simulations and three empirical applications, including analyses of US sectoral inflation rates, fiscal multipliers, and volatility spillover in China's banking sector. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.18970 |
| By: | Annika Camehl (Erasmus University Rotterdam); Tomasz Wo\'zniak (University of Melbourne) |
| Abstract: | We propose a novel Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects among alternative patterns of exclusion restrictions to identify structural shocks within the Markov process regimes. We implement the selection through a multinomial prior distribution over these patterns, which is a spike'n'slab prior for individual parameters. By combining a Markov-switching structural matrix with heteroskedastic structural shocks following a stochastic volatility process, the model enables shock identification through time-varying volatility within a regime. As a result, the exclusion restrictions become over-identifying, and their selection is driven by the signal from the data. Our empirical application shows that data support time variation in the US monetary policy shock identification. We also verify that time-varying volatility identifies the monetary policy shock within the regimes. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.19659 |
| By: | Sihan Tu; Zhaoxing Gao |
| Abstract: | Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Supervised Screening and Regularized Factor-based (SSRF) framework that systematically addresses high-dimensional predictor sets through a structured four-step procedure integrating both static and dynamic forecasting mechanisms. The static approach selects predictors via marginal correlation screening and scales them using univariate predictive slopes, while the dynamic method screens and scales predictors based on time series regression incorporating lagged predictors. PCA then extracts latent factors from the scaled predictors, followed by LASSO regularization to refine predictive accuracy. In the simulation study, we validate the effectiveness of SSRF and identify its parameter adjustment strategies in high-dimensional data settings. An empirical analysis of macroeconomic indices in China demonstrates that the SSRF method generally outperforms several commonly used forecasting techniques in out-of-sample predictions. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15275 |
| By: | Yannick Hoga; Christian Schulz |
| Abstract: | This paper is the first to propose valid inference tools, based on self-normalization, in time series expected shortfall regressions. In doing so, we propose a novel two-step estimator for expected shortfall regressions which is based on convex optimization in both steps (rendering computation easy) and it only requires minimization of quantile losses and squared error losses (methods for both of which are implemented in every standard statistical computing package). As a corollary, we also derive self-normalized inference tools in time series quantile regressions. Extant methods, based on a bootstrap or direct estimation of the long-run variance, are computationally more involved, require the choice of tuning parameters and have serious size distortions when the regression errors are strongly serially dependent. In contrast, our inference tools only require estimates of the quantile regression parameters that are computed on an expanding window and are correctly sized. Simulations show the advantageous finite-sample properties of our methods. Finally, two applications to stock return predictability and to Growth-at-Risk demonstrate the practical usefulness of the developed inference tools. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.10065 |
| By: | Wei Miao; Jad Beyhum; Jonas Striaukas; Ingrid Van Keilegom |
| Abstract: | This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors by employing a sparse-group penalty. We establish finite-sample bounds for the estimation error, accounting for censoring, the MIDAS approximation error, and heavy tails. The superior performance of the method is demonstrated through Monte Carlo simulations. Finally, we present an extensive application of our methodology to predict the financial distress of Chinese-listed firms. Our novel procedure is implemented in the R package 'Survivalml'. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.09740 |