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Econometrics, Volume 10, Issue 4 (December 2022) – 6 articles

Cover Story (view full-size image): This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for non-stationary processes when the trend component is computed with the Hodrick–Prescott filter rendering a time-reversible closed-form solution. This paper also links the concept of an environmental tipping point to the statistical property of time irreversibility and assesses fourteen climate indicators. We find evidence of time irreversibility in greenhouse gas emissions, global temperature, global sea levels, sea ice area, and some natural oscillation indices. While not conclusive, our findings urge the implementation of correction policies to avoid the worst consequences of climate change and to not miss the window of opportunity, which might still be available, despite closing quickly. View this paper

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9 pages, 291 KiB

Open AccessArticle

Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series

by Marc Hallin

Econometrics 2022, 10(4), 37; https://doi.org/10.3390/econometrics10040037 - 13 Dec 2022

Viewed by 1680

Abstract

For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In this short note, we chose to concentrate on a relatively little-known aspect of Manfred’s contribution that nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models. Full article

(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)

18 pages, 569 KiB

Open AccessEditor’s ChoiceArticle

Is Climate Change Time-Reversible?

by Francesco Giancaterini, Alain Hecq and Claudio Morana

Econometrics 2022, 10(4), 36; https://doi.org/10.3390/econometrics10040036 - 07 Dec 2022

Cited by 1 | Viewed by 3142

Abstract

This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for non-stationary processes when the trend component is computed with the Hodrick–Prescott filter rendering a time-reversible closed-form solution. This paper also links the concept of an environmental tipping point to the statistical property of time irreversibility and assesses fourteen climate indicators. We find evidence of time irreversibility in greenhouse gas emissions, global temperature, global sea levels, sea ice area, and some natural oscillation indices. While not conclusive, our findings urge the implementation of correction policies to avoid the worst consequences of climate change and not miss the opportunity window, which might still be available, despite closing quickly. Full article

(This article belongs to the Collection Econometric Analysis of Climate Change)

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26 pages, 395 KiB

Open AccessArticle

Linear System Challenges of Dynamic Factor Models

by Brian D. O. Anderson, Manfred Deistler and Marco Lippi

Econometrics 2022, 10(4), 35; https://doi.org/10.3390/econometrics10040035 - 06 Dec 2022

Cited by 3 | Viewed by 1627

Abstract

A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed application of the model, be it for example prediction or business cycle determination. Mixed frequency problems are also considered, in which certain entries of an underlying vector process are only available for measurement at a submultiple frequency of the original process. Certain classes of processes are shown to be generically identifiable, and others not to have this property. Full article

(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)

24 pages, 2695 KiB

Open AccessArticle

Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing

by Merlin Keller, Guillaume Damblin, Alberto Pasanisi, Mathieu Schumann, Pierre Barbillon, Fabrizio Ruggeri and Eric Parent

Econometrics 2022, 10(4), 34; https://doi.org/10.3390/econometrics10040034 - 29 Nov 2022

Viewed by 1598

Abstract

In this paper, we present a case study aimed at determining a billing plan that ensures customer loyalty and provides a profit for the energy company, whose point of view is taken in the paper. The energy provider promotes new contracts for residential buildings, in which customers pay a fixed rate chosen in advance, based on an overall energy consumption forecast. For such a purpose, we consider a practical Bayesian framework for the calibration and validation of a computer code used to forecast the energy consumption of a building. On the basis of power field measurements, collected from an experimental building cell in a given period of time, the code is calibrated, effectively reducing the epistemic uncertainty affecting the most relevant parameters of the code (albedo, thermal bridge factor, and convective coefficient). The validation is carried out by testing the goodness of fit of the code with respect to the field measurements, and then propagating the posterior parametric uncertainty through the code, obtaining probabilistic forecasts of the average electrical power delivered inside the cell in a given period of time. Finally, Bayesian decision-making methods are used to choose the optimal fixed rate (for the energy provider) of the contract, in order to balance short-term benefits with customer retention. We identify three significant contributions of the paper. First of all, the case study data were never analyzed from a Bayesian viewpoint, which is relevant here not only for estimating the parameters but also for properly assessing the uncertainty about the forecasts. Furthermore, the study of optimal policies for energy providers in this framework is new, to the best of our knowledge. Finally, we propose Bayesian posterior predictive p-value for validation. Full article

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27 pages, 4994 KiB

Open AccessArticle

Detecting and Quantifying Structural Breaks in Climate

by Neil R. Ericsson, Mohammed H. I. Dore and Hassan Butt

Econometrics 2022, 10(4), 33; https://doi.org/10.3390/econometrics10040033 - 25 Nov 2022

Cited by 2 | Viewed by 2471

Abstract

Structural breaks have attracted considerable attention recently, especially in light of the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and quantifying breaks. The current paper presents a unified framework for analyzing breaks; and it implements that framework to test for and quantify changes in precipitation in Mauritania over 1919–1997. These tests detect a decline of one third in mean rainfall, starting around 1970. Because water is a scarce resource in Mauritania, this decline—with adverse consequences on food production—has potential economic and policy consequences. Full article

(This article belongs to the Collection Econometric Analysis of Climate Change)

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28 pages, 5763 KiB

Open AccessArticle

On the Bayesian Mixture of Generalized Linear Models with Gamma-Distributed Responses

by Irwan Susanto, Nur Iriawan and Heri Kuswanto

Econometrics 2022, 10(4), 32; https://doi.org/10.3390/econometrics10040032 - 04 Oct 2022

Cited by 1 | Viewed by 2320

Abstract

This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the mean and linear predictors of the model, is implemented to ensure non-negative values of the predicted gamma-distributed responses. The simulation-based inferential processes related to the Bayesian-MCMC method is carried out using the Gibbs sampler algorithm. The performance of proposed model is conducted through two real data applications on the gross domestic product per capita at purchasing power parity and the annual household income per capita. Graphical posterior predictive checks are carried out to verify the adequacy of the fitted model for the observed data. The predictive accuracy of this model is compared with other Bayesian models using the widely applicable information criterion (WAIC). We find that the Bayesian mixture of GLMs with gamma-distributed responses performs properly when the appropriate prior distributions are applied and has better predictive accuracy than the Bayesian mixture of linear regression model and the Bayesian gamma regression model. Full article

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