Identification of Seasonal Effects in Impulse Responses Using Score-Driven Multivariate Location Models

Escribano

2022

Econometrics

Self Cite

We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous orbital variables: eccentricity of the Earth’s orbit, obliquity, and precession of the equinox. We introduce score-driven ice-age models which use robust filters of the conditional mean and variance, generalizing the updating mechanism and solving the misspecification of a recent climate–econometric model (benchmark ice-age model). The score-driven models control for omitted exogenous variables and extreme events, using more general dynamic structures and heteroskedasticity. We find that the score-driven models improve the performance of the benchmark ice-age model. We provide out-of-sample forecasts of the climate variables for the last 100 thousand years. We show that during the last 10–15 thousand years of the forecasting period, for which humanity influenced the Earth’s climate, (i) the forecasts of Icet are above the observed Icet, (ii) the forecasts of CO2,t level are below the observed CO2,t, and (iii) the forecasts of Tempt are below the observed Tempt. The forecasts for the benchmark ice-age model are reinforced by the score-driven models.

Section: Score-driven Homoskedastic Ice-age Modelmentioning

confidence: 99%

Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models

Escribano

2022

Econometrics

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“…In the work of Creal et al (2014), the score-driven t-QVARMA model is introduced for I(0) variables. In the recent works of Blazsek, Escribano, and Licht (2020, 2021a, 2021b, the statistical performance of t-QVARMA is studied and the model is extended to combinations of I(0) and co-integrated I(1) variables, which are further extended in the present work to include exogenous explanatory variables.…”

Section: Score-driven Time Series Modelsmentioning

confidence: 99%

Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions

Kobor

Studies in Nonlinear Dynamics &Amp; Econometrics

2022

Self Cite

In this paper, the effects of United States (US) policy actions on mortgage-backed security and mortgage loan spreads are measured, by using data before, during, and after the US subprime mortgage crisis. We study the effects of the following policy actions: (i) the placement of Fannie Mae and Freddie Mac into US Government conservatorship; (ii) the US Federal Reserve quantitative easing (QE) programs. We provide the following contributions to the literature: (i) for a robust measurement of policy effects, a new multi-equation score-driven t-QVARMA (quasi-vector autoregressive moving average) model is used. (ii) In addition to the measurement of the effects of QE, the effects of government conservatorship are also measured in this paper. (iii) Furthermore, the data period of the relevant literature is extended to the period of June 1998 to March 2020.

Factor-augmented QVAR models: an observation-driven approach

Alanya-Beltran

2023

Macroecon. Dynam.

I develop and study a factor-augmented quasi-vector autoregressive (FAQVAR) model for economic policy analysis in tumultuous times. An observation-driven framework that exploits the information from the score of the model allows a maximum likelihood estimation. This multivariate FAQVAR model, which assumes a Student t error distribution, is robust to atypical observations such as the global financial crisis and the recent pandemic. The model outperforms the FAVAR moving average model because of the assumed heavy tails that capture the COVID-19 atypical data and other turbulent episodes. An empirical application to the U.S. economy assessing its monetary policy reveals that estimates and impulse responses are stable when considering the sample before and during COVID-19.