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on Econometric Time Series |
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Issue of 2025–12–08
seven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | del Barrio Castro, Tomas; Sanso Rossello, Andreu; Sibbertsen, Philipp |
| Abstract: | In this paper we find an alternative explanation for the presence of long memory in marginalized time series of an autoregressive system in situations earlier explored by Bauwens, Chevillon and Laurent (2023) and Chevillon, Hecq, and Laurent (2018) which is the near cancellation of the damped trend shared by all the time series of the VAR(1) used in these papers and the MA(1) part of the data generating process followed by the marginalized time series. For a given time dimension T the long memory observed in the marginalized time series will depend on the number of time series in the VAR(1) system but not on the specific value of the main diagonal associated with the matrix of coefficients of the VAR(1) as stated in Chevillon, Hecq, and Laurent (2018) and Bauwens, Chevillon and Laurent (2023). Our results are based on the properties of circulant matrices and the Vector Moving Average representation of the VAR(1) model proposed in the previous two papers. Finally a Monte-Carlo experiment supports our analytical findings. |
| Keywords: | Long Memory, Marginalized Time Series, Damped Trend |
| JEL: | C3 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:han:dpaper:dp-742 |
| By: | Jason R. Blevins |
| Abstract: | Fractionally integrated time series exhibiting long memory are commonly found in economics, finance, and related fields. Semiparametric methods for estimating the memory parameter $d$ have proven to be effective and robust, but practitioners face difficulties arising from the availability of multiple estimators with different valid parameter ranges and the choice of bandwidth parameter $m$. This paper provides a comprehensive evaluation of local Whittle methods from Robinson's (1995) foundational estimator through the exact local Whittle approaches of Shimotsu and Phillips (2005) and Shimotsu (2010), where theoretical advances have expanded the feasible range of memory parameters and improved efficiency. Using a new implementation in Python, PyELW, we replicate key empirical and Monte Carlo results from the literature, providing external validation for both the original findings and the software implementation. We extend these empirical applications to demonstrate how method choice can affect substantive conclusions about persistence. Based on comprehensive simulation comparisons and empirical evidence, we provide practical guidance for applied researchers on how and when to use each method. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.15689 |
| By: | Guglielmo Maria Caporale; Luis Alberiko Gil-Alana; Nieves Carmona-González; Maria Fatima Romero-Rojo |
| Abstract: | This paper applies fractional integration methods to obtain evidence on ocean acidification, namely the decrease in the pH level in the Earth’s oceans, using the annual Hawaii Ocean Time-series Station ALOHA series as well as the logged one for the period 1985-2024. The chosen modelling framework is more general than standard ones based on the I(0) versus I(1) dichotomy and sheds light on the long memory and persistence properties, as well as on the possible presence of trends, in the pH Level in the Earth’s oceans. The results indicate that the series exhibit a negative and significant time trend; however, whether or not the null hypothesis of a unit root is rejected depends on the assumption made about the errors. The key finding (when the errors are not incorrectly specified as I(0) processes) is the presence of long memory, which implies that the effects of shocks are long-lived, regardless of whether or not mean reversion occurs. Moreover, the recursive analysis indicates that both the degree of persistence and the downward trend in the pH level have increased over time. This evidence points to the urgent need for decisive policies to address the issue of ocean acidification and protect marine life and biodiversity. |
| Keywords: | ocean acidification, PH level, earth’s oceans, persistence, fractional integration, recursive estimation |
| JEL: | C22 Q53 Q54 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12281 |
| By: | Andriy Norets (Brown University); Marco Stenborg Petterson (University of Naples Federico II and CSEF) |
| Abstract: | The paper considers a nonparametric Bayesian model for conditional densities. The model considered is a mixture of orthogonal polynomials with a prior on the number of components. The use of orthogonal polynomials allows for a great deal of flexibility in applications while maintaining useful approximation properties. We provide the posterior contraction rate in the case of Legendre polynomials. The algorithm proposed allows for cross-dimensional moves, allowing it to choose the optimal number of terms in the series expansion conditional on a penalty parameter. We also provide Monte Carlo simulations that show how well the model approximates known distributions also in finite sample situations. |
| Keywords: | Bayesian nonparametrics, orthogonal polynomials, variable dimensions model |
| JEL: | C11 C14 C13 |
| Date: | 2024–12–01 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:744 |
| By: | Karlsson, Sune (Örebro University School of Business); Österholm, Pär (Örebro University School of Business) |
| Abstract: | In this paper, we analyse whether two key macroeconomic relationships in Australia – Okun’s law and the Phillips curve – have been stable over time. This is done by estimating hybrid time-varying parameter Bayesian VAR models using quarterly data from 1978 to 2024. Model comparison based on marginal likelihoods indicates that Okun’s law has been stable, whereas the Phillips curve has not. Using the preferred specification of the BVAR for the unemployment rate and inflation, we also calculate trend values for both variables. The model’s trend unemployment rate at the end of the sample is approximately five percent; estimated trend inflation at the same point in time is close to the Reserve Bank of Australia’s inflation target. |
| Keywords: | Inflation; Unemployment; GDP growth; Bayesian VAR; Time-varying parameters |
| JEL: | C11 C32 E32 E52 |
| Date: | 2025–11–26 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_015 |
| By: | Masahiko Shibamoto (Research Institute for Economics and Business Administration and Center for Computational Social Science, Kobe University, JAPAN) |
| Abstract: | This study provides an empirical assessment of business cycle dynamics using a structuralvector autoregressive (VAR) model that measures cyclical output and identifies business cycle shocks as the main drivers. Using the same data and reduced-form VAR setup as Angeletos et al. (2020, American Economic Review), we estimate the dynamic effects of this shock on the U.S. economy. The cyclical output indicated by the model closely tracked the standard measure of the output gap. The identified business cycle shock has long-lasting effects on both demand- and supply-side factors, permanently influencing output and affecting labor productivity and total factor productivity. These findings contradict the prevailing notion that business cycles are short-term phenomena and suggest that the forces driving them contribute to medium-term dynamics. This implies a pivotal connection between short-term stabilization and long-term growth. |
| Keywords: | Business-cycle shocks; Structural vector autoregressive model; Finite-horizon identification; Cyclical output; Medium-term dynamics |
| JEL: | C32 E32 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:kob:dpaper:dp2025-30 |
| By: | Capilla, Javier; Alcaráz, Alba; Valarezo, Angel; Garcia-Hiernaux, Alfredo; Pérez-Amaral, Teodosio |
| Abstract: | Eco-RETINA is an innovative and eco-friendly algorithm explicitly designed for out-of-sample prediction. Functioning as a regression-based flexible approximator, it is linear in parameters but nonlinear in inputs, employing a selective model search to optimize performance. The algorithm adeptly manages multicollinearity, emphasizing speed, accuracy, and environmental sustainability. Its modular and transparent structure facilitates easy interpretation and modification, making it an invaluable tool for researchers in developing explicit models for out-of-sample forecasting. The algorithm generates outputs such as a list of relevant transformed inputs, coefficients, standard deviations, and confidence intervals, enhancing its interpretability. |
| JEL: | C14 C45 C51 C63 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331255 |