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on Econometric Time Series |
| By: | Aknouche, Abdelhakim |
| Abstract: | state spaces with periodically time-varying transition probabilities is introduced. The finite-dimensional probability distributions of these time-periodic chains are first studied and their correspondence with the marginal distributions and transition probabilities is shown. Then, the concepts of periodic stability/regularity and limiting behaviors are proposed. The communicability and classification of states necessary for establishing periodic stability are then examined. In particular, periodic irreducibility and the main solidarity/class properties are presented, namely periodic recurrence, periodic positive recurrence, periodic transience, and periodic aperiodicity. Furthermore, sufficient conditions for periodic stochastic stability of time-periodic Markov chains are derived. Finally, various applications to some operations research models and time series analysis are considered. In particular, periodic Markov decision processes, periodic integer-valued time series models, and periodic Markov-switching time series models are examined. |
| Keywords: | Time-periodic Markov chains, Harris periodic ergodicity, periodic irreducibility, periodic recurrence, periodic stability, periodic invariant distributions, periodic integer-valued time series models, Markov-switching periodic models, periodic Markov decision process. |
| JEL: | C01 C02 C30 |
| Date: | 2024–10–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122287 |
| By: | Ignace De Vos; Gerdie Everaert (-) |
| Abstract: | Local projections (LPs) are often regarded as more robust to model misspecification than impulse responses (IRs) derived from forward-iterated dynamic model estimates, as LPs impose fewer restrictions on the underlying dynamics. However, because forecast errors accumulate in the LP errors over the projection horizon, this robustness comes at the price of an increase in variance. To address this, several Generalized Least Squares (GLS) estimators have been proposed to reduce error accumulation and enhance efficiency. We demonstrate, however, that the implied conditioning on dynamic model (horizon-one LP) residuals imposes strong restrictions on the underlying data generating process, undermining the very robustness to misspecification that LPs are valued for. In fact, we show that these GLS LP estimators tend to align more closely with forward-iterated IRs from potentially misspecified models, than with OLS-estimated LPs. Furthermore, we find that conditioning on previous horizon LP residuals fails to deliver efficiency improvements over OLS-estimated LPs. |
| Keywords: | Impulse response functions, local projections, dynamic models, generalized least squares, efficiency, robustness |
| JEL: | C22 C13 C53 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:rug:rugwps:24/1095 |
| By: | Kiss, Tamás (Örebro University School of Business); Mazur, Stepan (Örebro University School of Business); Nguyen, Hoang (Linköping University); Österholm, Pär (Örebro University School of Business) |
| Abstract: | In this paper, we extend the standard Gaussian stochastic-volatility Bayesian VAR by employing the generalized hyperbolic skew Student’s t distribution for the innovations. Allowing the skewness parameter to vary over time, our specification permits flexible modelling of innovations in terms of both fat tails and – potentially dynamic – asymmetry. In an empirical application using US data on industrial production, consumer prices and economic policy uncertainty, we find support – although to a moderate extent – for time-varying skewness. In addition, we find that shocks to economic policy uncertainty have a negative effect on both industrial production growth and CPI inflation. |
| Keywords: | Bayesian VAR; Generalized hyperbolic skew Students’s t distribution; Stochastic volatility; Economic policy uncertainty |
| JEL: | C11 C32 C52 E44 E47 G17 |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2024_008 |
| By: | Manuel Stapper |
| Abstract: | Standard M-Estimation techniques are biased if an asymmetric distribution is assumed. This article proposes a novel approach that uses an adaptive asymmetric loss function to tackle the bias. Its consistency and asymptotic normality are proven. The robustness properties are assessed in a simulation study showing similar performance compared to existing approaches. Its versatility is demonstrated in three applications to time series data, an instrumental regression and a classification task. |
| Keywords: | Robust Statistics, M-Estimation, Computational Statistics |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:cqe:wpaper:10924 |
| By: | Laura Coroneo; Fabrizio Iacone |
| Abstract: | We analyse the properties of the Diebold and Mariano (1995) test in the presence of autocorrelation in the loss differential. We show that the power of the Diebold and Mariano (1995) test decreases as the dependence increases, making it more difficult to obtain statistically significant evidence of superior predictive ability against less accurate benchmarks. We also find that, after a certain threshold, the test has no power and the correct null hypothesis is spuriously rejected. Taken together, these results caution to seriously consider the dependence properties of the loss differential before the application of the Diebold and Mariano (1995) test. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.12662 |
| By: | Anna Kiriliouk; Chen Zhou |
| Abstract: | This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment of tail risks. We discuss the two main approches to tail risk estimation, unconditional and conditional quantile forecasting. We use the S&P 500 index as a case study to assess serial (extremal) dependence, perform an unconditional and conditional risk analysis, and apply backtesting methods. Additionally, the chapter explores the impact of serial dependence on multivariate tail dependence. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18643 |