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on Econometric Time Series |
| By: | Yuying Sun; Feng Chen; Jiti Gao |
| Abstract: | This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to structural changes. The TVMA method averages predictions from a set of time-varying vector autoregressive models using optimal time-varying combination weights selected by minimizing a penalized local criterion. This allows the relative importance of different models to adaptively evolve over time in response to structural shifts. We establish an asymptotic optimality for the proposed TVMA approach in achieving the lowest possible quadratic forecast errors. The convergence rate of the selected time-varying weights to the optimal weights minimizing expected quadratic errors is derived. Moreover, we show that when one or more correctly specified models exist, our method consistently assigns full weight to them, and an asymptotic normality for the TVMA estimators under some regularity conditions can be established. Furthermore, the proposed approach encompasses special cases including time-varying VAR models with exogenous predictors, as well as time-varying factor augmented VAR (FAVAR) models. Simulations and an empirical application illustrate the proposed TVMA method outperforms some commonly used model averaging and selection methods in the presence of structural changes. |
| Keywords: | : Asymptotic Optimality; Consistency; Structural Change; Time-varying Weight |
| JEL: | C52 C53 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2025-1 |
| By: | Li, Chenxing; Yang, Qiao |
| Abstract: | This paper introduces a novel Bayesian time series model that combines the nonparametric features of an infinite hidden Markov model with the volatility persistence captured by the GARCH framework, to effectively model and forecast short-term interest rates. When applied to US 3-month Treasury bill rates, the GARCH-IHMM reveals both structural and persistent changes in volatility, thereby enhancing the accuracy of density forecasts compared to existing benchmark models. Out-of-sample evaluations demonstrate the superior performance of our model in density forecasts and in capturing volatility dynamics due to its adaptivity to different macroeconomic environments. |
| Keywords: | Interest rates; Bayesian nonparametrics; GARCH; density forecasts |
| JEL: | C11 C14 C51 C53 C58 E43 E47 G17 |
| Date: | 2025–01–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123200 |
| By: | Gonzalo, Jesús; Pitarakis, Jean-Yves |
| Abstract: | We propose a two-step procedure to detect cointegration in high-dimensional settings, focusing on sparse relationships. First, we use the adaptive LASSO to identify the small subset of integrated covariates driving the equilibrium relationship with a target series, ensuring model-selection consistency. Second, we adopt an information-theoretic model choice criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding dependence on asymptotic distributional assumptions. Monte Carlo experiments confirm robust finite-sample performance, even under endogeneity and serial correlation. |
| Keywords: | Cointegration; High Dimensional Data; Adaptive Lasso; Unit Roots |
| JEL: | C32 C52 |
| Date: | 2025–01–27 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:45708 |
| By: | ALAMI CHENTOUFI, Reda |
| Abstract: | This paper introduces a two-step procedure for convex penalized estimation in dynamic location-scale models. The method uses a consistent, non-sparse first-step estimator to construct a convex Weighted Least Squares (WLS) optimization problem compatible with the Least Absolute Shrinkage and Selection Operator (LASSO), addressing challenges associated with non-convexity and enabling efficient, sparse estimation. The consistency and asymptotic distribution of the estimator are established, with finite-sample performance evaluated through Monte Carlo simulations. The method's practical utility is demonstrated through an application to electricity prices in France, Belgium, the Netherlands, and Switzerland, effectively capturing seasonal patterns and external covariates while ensuring model sparsity. |
| Keywords: | Weighted LSE; LASSO estimation; variable selection; GARCH models |
| JEL: | C01 C22 C51 C52 C58 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123283 |
| By: | Qianli Zhao; Chao Wang; Richard Gerlach; Giuseppe Storti; Lingxiang Zhang |
| Abstract: | Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure, combining the information from multiple realised measures in a nonlinear manner. Our proposed model extends existing linear methods, such as Principal Component Analysis and Independent Component Analysis, to reduce the dimensionality of realised measures. The empirical evaluation, conducted across four major stock markets from January 2000 to June 2022 and including the period of COVID-19, demonstrates both the feasibility of applying an autoencoder to synthesise volatility measures and the superior effectiveness of the proposed model in one-step-ahead rolling volatility forecasting. The model exhibits enhanced flexibility in parameter estimations across each rolling window, outperforming traditional linear approaches. These findings indicate that nonlinear dimension reduction offers further adaptability and flexibility in improving the synthetic realised measure, with promising implications for future volatility forecasting applications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.17136 |
| By: | Massimo Franchi; Iliyan Georgiev; Paolo Paruolo |
| Abstract: | This paper proposes a novel canonical correlation analysis for semiparametric inference in $I(1)/I(0)$ systems via functional approximation. The approach can be applied coherently to panels of $p$ variables with a generic number $s$ of stochastic trends, as well as to subsets or aggregations of variables. This study discusses inferential tools on $s$ and on the loading matrix $\psi$ of the stochastic trends (and on their duals $r$ and $\beta$, the cointegration rank and the cointegrating matrix): asymptotically pivotal test sequences and consistent estimators of $s$ and $r$, $T$-consistent, mixed Gaussian and efficient estimators of $\psi$ and $\beta$, Wald tests thereof, and misspecification tests for checking model assumptions. Monte Carlo simulations show that these tools have reliable performance uniformly in $s$ for small, medium and large-dimensional systems, with $p$ ranging from 10 to 300. An empirical analysis of 20 exchange rates illustrates the methods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19572 |