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on Econometric Time Series |
| By: | Aknouche, Abdelhakim; Rabehi, Nadia |
| Abstract: | This work proposes a class of seasonal autoregressive integrated moving average models whose period is an independent and identically distributed random process valued in a finite set. The causality, invertibility, and autocovariance shape of the model are first revealed. Then, the estimation of the parameters which are the model coefficients, the innovation variance, the probability distribution of the period, and the (unobserved) sample-path of the period, is carried out using the expectation-maximization algorithm. In particular, a procedure for random elimination of seasonality is proposed. An application of the methodology to the annual Wolfer sunspot numbers is provided. |
| Keywords: | Seasonal ARIMA models, irregular seasonality, random period, non-integer period, SARIMAR model, EM algorithm. |
| JEL: | C13 C18 C52 |
| Date: | 2024–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120758&r=ets |
| By: | Marín Díazaraque, Juan Miguel; Romero, Eva; Lopes Moreira Da Veiga, María Helena |
| Abstract: | In this paper, we propose a novel asymmetric stochastic volatility model that uses a heterogeneous autoregressive process to capture the persistence and decay of volatility asymmetry over time, which is different from traditional approaches. We analyze the properties of the model in terms of volatility asymmetry and propagation using a recently introduced concept in the field and find that the new model can generate both volatility asymmetry and propagation effects. We also introduce Data Cloning for parameter estimation, which provides robustness and computational efficiency compared to conventional techniques. Our empirical analysis shows that the new proposal outperforms a recent competitor in terms of in-sample fit and out-of-sample volatility prediction across different financial return series, making it a more effective tool for capturing the dynamics of volatility asymmetry in financial markets. |
| Keywords: | Data cloning; Propagation; Stochastic volatility; Volatility asymmetry |
| Date: | 2024–05–07 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:43887&r=ets |
| By: | Kunyang Song; Feiyu Jiang; Ke Zhu |
| Abstract: | We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD).Our MDD-based estimation method is formed in the framework of a continuum of unconditional moment restrictions. Unlike the existing estimation methods in this framework, the MDD-based estimation method adopts a non-integrable weighting function, which could grab more information from unconditional moment restrictions than the integrable weighting function to enhance the estimation efficiency. Due to the nature of shift-invariance in MDD, our MDD-based estimation method can not identify the intercept parameters. To overcome this identification issue, we further provide a two-step estimation procedure for the model with intercept parameters. Under regularity conditions, we establish the asymptotics of the proposed estimators, which are not only easy-to-implement with analytic asymptotic variances, but also applicable to time series data with an unspecified form of conditional heteroskedasticity. Finally, we illustrate the usefulness of the proposed estimators by simulations and two real examples. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11092&r=ets |