Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks

Pretis, Felix; Reade, J. James; Sucarrat, Genaro

doi:10.18637/jss.v086.i03

Cited by 74 publications

(54 citation statements)

References 34 publications

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“…IIS has been applied to various settings, including the identification of unknown volcanic eruptions in historic temperature records [ 46 , 47 ], to evaluate climate models [ 48 ], assess economic forecasts [ 49 ] and test the robustness of panel models of unemployment [ 50 ]. For the present analysis, IIS is implemented using the R-package ‘gets' [ 20 ]. All models are estimated as a dynamic panel 1 with the general model specified as where is an iid (independent and identically distributed) mean-zero error term, VT i , t denotes the variance of monthly temperatures for country i in year t , and Max T i , t (Min T i , t ) refers to the maximum (minimum) monthly average temperature for country i in year t .…”

Section: Methodsmentioning

confidence: 99%

Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming

Pretis

Schwarz

Tang

et al. 2018

Phil. Trans. R. Soc. A.

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Empirical evidence suggests that variations in climate affect economic growth across countries over time. However, little is known about the relative impacts of climate change on economic outcomes when global mean surface temperature (GMST) is stabilized at 1.5°C or 2°C warming relative to pre-industrial levels. Here we use a new set of climate simulations under 1.5°C and 2°C warming from the ‘Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) project to assess changes in economic growth using empirical estimates of climate impacts in a global panel dataset. Panel estimation results that are robust to outliers and breaks suggest that within-year variability of monthly temperatures and precipitation has little effect on economic growth beyond global nonlinear temperature effects. While expected temperature changes under a GMST increase of 1.5°C lead to proportionally higher warming in the Northern Hemisphere, the projected impact on economic growth is larger in the Tropics and Southern Hemisphere. Accounting for econometric estimation and climate uncertainty, the projected impacts on economic growth of 1.5°C warming are close to indistinguishable from current climate conditions, while 2°C warming suggests statistically lower economic growth for a large set of countries (median projected annual growth up to 2% lower). Level projections of gross domestic product (GDP) per capita exhibit high uncertainties, with median projected global average GDP per capita approximately 5% lower at the end of the century under 2°C warming relative to 1.5°C. The correlation between climate-induced reductions in per capita GDP growth and national income levels is significant at the p < 0.001 level, with lower-income countries experiencing greater losses, which may increase economic inequality between countries and is relevant to discussions of loss and damage under the United Nations Framework Convention on Climate Change.This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

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Section: Methodsmentioning

confidence: 99%

Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming

Pretis

Schwarz

Tang

et al. 2018

Phil. Trans. R. Soc. A.

Self Cite

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“…Since then, multiple extensions of the GARCH scedastic function have been proposed to capture additional stylized facts observed in financial and economic time series, such as nonlinearities, asymmetries, and long-memory properties; see Teräsvirta (2009) for a review. According to the Time Series Analysis (Hyndman 2019) and Empirical Finance (Eddelbuettel 2019) task views at https://CRAN.R-project.org/web/views, the following implementations of univariate GARCH-type models are available in the R (R Core Team 2018) programming language: bayesGARCH (Ardia and Hoogerheide 2010), fGarch (Wuertz, Chalabi, Miklovic, Boudt, and Chausse 2016), GAS (Ardia, Boudt, and Catania 2019b), gets (Pretis, Reade, and Sucarrat 2018), GEVStableGarch (Sousa, Otiniano, Lopes, and Diethelm 2015), lgarch (Sucarrat 2015), rugarch (Ghalanos 2017) and tseries (Trapletti and Hornik 2017). In GARCHtype models, the conditional volatility is driven by shocks in the observed time series.…”

Section: Introductionmentioning

confidence: 99%

Markov-Switching GARCH Models in R: The MSGARCH Package

Ardia¹,

Bluteau²,

Boudt³

et al. 2019

J. Stat. Soft.

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We describe the package MSGARCH, which implements Markov-switching GARCH (generalized autoregressive conditional heteroscedasticity) models in R with efficient C++ object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The package MSGARCH allows the user to perform simulations as well as maximum likelihood and Bayesian Markov chain Monte Carlo estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, value-at-risk, and expected-shortfall are also available. We illustrate the broad functionality of the MSGARCH package using exchange rate and stock market return data.

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“…A related iterative algorithm is Impulse Indicator Saturation, based on an idea of Hendry (1999), see also (Hendry & Doornik, 2014, §15). It is implemented in Ox, see Doornik (2009) and R, see Pretis et al (2018). A stylized version of the algorithm is the split-half algorithm suggested by Hendry et al (2008).…”

Section: Applicationsmentioning

confidence: 99%

The Analysis of Marked and Weighted Empirical Processes of Estimated Residuals

2019

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An extended and improved theory is presented for marked and weighted empirical processes of residuals of time series regressions. The theory is motivated by 1step Huber-skip estimators, where a set of good observations are selected using an initial estimator and an updated estimator is found by applying least squares to the selected observations. In this case, the weights and marks represent powers of the regressors and the regression errors, respectively. The inclusion of marks is a non-trivial extention to previous theory and requires refined martingale arguments.

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Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks

Cited by 74 publications

References 34 publications

Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming

Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming

Markov-Switching GARCH Models in R: The MSGARCH Package

The Analysis of Marked and Weighted Empirical Processes of Estimated Residuals

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