|
on Econometric Time Series |
|
Issue of 2025–10–13
seven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Leonardo N. Ferreira; Haroon Mumtaz; Ana Skoblar |
| Abstract: | This paper introduces a Bayesian vector autoregression (BVAR) with stochastic volatility-in-mean and time-varying skewness. Unlike previous approaches, the proposed model allows both volatility and skewness to directly affect macroeconomic variables. We provide a Gibbs sampling algorithm for posterior inference and apply the model to quarterly data for the US and the UK. Empirical results show that skewness shocks have economically significant effects on output, inflation and spreads, often exceeding the impact of volatility shocks. In a pseudo-real-time forecasting exercise, the proposed model outperforms existing alternatives in many cases. Moreover, the model produces sharper measures of tail risk, revealing that standard stochastic volatility models tend to overstate uncertainty. These findings highlight the importance of incorporating time-varying skewness for capturing macro-financial risks and improving forecast performance. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08415 |
| By: | Wing-Keung Wong (Department of Finance, Fintech Center, and Big Data Research Center, Asia University; Department of Medical Research, China Medical University Hospital, Taiwan; Business, Economic and Public Policy Research Centre, Hong Kong Shue Yan University; The Economic Growth Centre, Nanyang Technological University); Mu Yue (Engineering Systems and Design, Singapore University of Technology & Design) |
| Abstract: | We have read many papers in the literature and found that some papers report results of regressing a stationary time series on a non-stationary time series (we call it the IOI1 model). However, very few studies, if there are any, examine the IOI1 model and the robustness of inference in such settings remains an open question. To bridge the gap in the literature, in this paper, we investigate whether regressing a stationary time series, Yt, on a non-stationary time series, Xt (that is, Yt = α+βXt +ut) could get any meaningful result. To do so, we first conduct a simulation and find regressing a stationary time series on a non-stationary time series could be spurious. Thereafter, we develop the estimation and testing theory for the I0I1 model and find that the statistics Tβ N for testing Hβ 0 : β = β0 versus Hβ 1 : β ̸= β0 from the traditional regression model (we call it IOI0 model) does not have any asymptote distribution with E(Tβ N) → ∞ and V ar(Tβ N) → ∞ as N → ∞, and thus, it cannot be used for the I0I1 model. We have found other interesting results as shown in our paper. Thus, our paper extends the spurious regression literature to cover a previously unexplored case, thereby contributing to a more comprehensive understanding of time series modeling and inference. |
| Keywords: | Cointegration; stationarity; non-stationarity. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nan:wpaper:2504 |
| By: | Maria Gadea; Òscar Jordà |
| Abstract: | Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local projection at horizon 1. Although convenient, it is well documented that a VAR can be a poor approximation to impulse dynamics at horizons beyond its lag length. In this paper we assume instead that the precise form of the parametric model generating the data is not known. If one is willing to assume that the DGP is perhaps an infinite order process, a larger class of models can be accommodated and more tailored bootstrap procedures can be constructed. Using the moving average representation of the data, we construct appropriate bootstrap procedures. |
| Keywords: | local projections; inference |
| JEL: | C31 C32 |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:101873 |
| By: | Wing-Keung Wong (Department of Finance, Fintech Center, and Big Data Research Center, Asia University; Department of Medical Research, China Medical University Hospital, Taiwan; Department of Economics and Finance, The Hang Seng University of Hong Kong); Yushan Cheng (School of Mathematics and Statistics, Xi’an Jiaotong University, China); Mu Yue (Engineering Systems and Design, Singapore University of Technology & Design) |
| Abstract: | Most of the literature on spurious regression has found that regression of independent and (nearly) non-stationary time series could result in spurious outcomes. Very few studies address the issue of whether regression of stationary time series could also result in spurious outcomes and the study is not comprehensive and thorough. To bridge the gap in the literature, we first conjecture that regression of stationary time series could also result in spurious outcomes. We then examine whether the conjecture holds by providing a comprehensive and thorough study. We further provide a remedy algorithm to correct the spurious problem and improve the interpretability of the model. Extensive simulations are carried out to support our conjecture and demonstrate the effectiveness of the remedy. To demonstrate the applicability of our proposed approach and address the issue of spuriousness, we conduct a numerical analysis and demonstrate the usefulness of our proposed remedy algorithm. |
| Keywords: | spurious regression, stationarity, non-stationarity, autoregressive model. |
| JEL: | C01 C15 C22 C58 C60 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nan:wpaper:2503 |
| By: | Ziyan Zhao (Economic Growth Centre, School of Social Sciences, Nanyang Technological University); Qingfeng Liu (Department of Industrial and Systems Engineering, Hosei University) |
| Abstract: | This study proposes a time-varying structural approximate dynamic factor (TVS-ADF) model by extending the ADF model in state-space form. The TVS-ADF model considers time-varying coefficients and a time-varying variance–covariance matrix of its innovation terms, so that it can capture complex dynamic economic characteris- tics. We propose the identification scheme of the common factors in the TVS-ADF and derive the identification theory. We also propose an effective Markov chain Monte Carlo (MCMC) algorithm to estimate the TVS-ADF. To avoid the overparameterization caused by the time-varying characteristics of the TVS-ADF, we include the shrinkage and sparsification approaches in the MCMC algorithm. Additionally, we propose several effective information criteria for the determination of the number of factors in the TVS-ADF. Extensive artificial simulations demonstrate that the TVS-ADF has better forecast performance than the ADF in almost all settings for different numbers of explained variables, numbers of explanatory variables, sparsity levels, and sample sizes. An empirical application to macroeconomic forecasting also indicates that our model can substantially improve predictive accuracy and capture the dynamic features of an economic system better than the ADF. |
| Keywords: | MCMC, shrinkage, sparsification, overparameterization, algorithms |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:nan:wpaper:2401 |
| By: | Samuel N. Cohen; Cephas Svosve |
| Abstract: | We explore a link between stochastic volatility (SV) and path-dependent volatility (PDV) models. Using assumed density filtering, we map a given SV model into a corresponding PDV representation. The resulting specification is lightweight, improves in-sample fit, and delivers robust out-of-sample forecasts. We also introduce a calibration procedure for both SV and PDV models that produces standard errors for parameter estimates and supports joint calibration of SPX/VIX smile. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.02024 |
| By: | Karsten Reichold; Ulrike Schneider |
| Abstract: | This paper establishes new asymptotic results for the adaptive LASSO estimator in cointegrating regression models. We study model selection probabilities, estimator consistency, and limiting distributions under both standard and moving-parameter asymptotics. We also derive uniform convergence rates and the fastest local-to-zero rates that can still be detected by the estimator, complementing and extending the results of Lee, Shi, and Gao (2022, Journal of Econometrics, 229, 322--349). Our main findings include that under conservative tuning, the adaptive LASSO estimator is uniformly $T$-consistent and the cut-off rate for local-to-zero coefficients that can be detected by the procedure is $1/T$. Under consistent tuning, however, both rates are slower and depend on the tuning parameter. The theoretical results are complemented by a detailed simulation study showing that the finite-sample distribution of the adaptive LASSO estimator deviates substantially from what is suggested by the oracle property, whereas the limiting distributions derived under moving-parameter asymptotics provide much more accurate approximations. Finally, we show that our results also extend to models with local-to-unit-root regressors and to predictive regressions with unit-root predictors. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.07204 |