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Primary Research Interests: Time Series Econometrics, Applied Macroeconomics

Secondary Research Interests: Econometric Theory, Monetary Economics, Climate Economics


Predicting Tail Risks and the Evolution of Temperatures (Energy Economics, 2024)

joint with Vasco J. Gabriel & Luis F. Martins

This paper explores a range of simple models to study the relationship between global temperature anomalies and climate forcings. In particular, we consider quantile regression models with potentially time-varying parameters (TVP), implemented by Bayesian methods. In its most general specification, this approach is flexible in that it models distinct regions of distribution of global temperature anomalies, while also allowing us to investigate changes in the relationship between (natural and anthropogenic) climate forcings and temperatures. Our results indicate that there is indeed considerable variation over time in the relationship between temperatures and its drivers, and that these effects may be heterogeneous across different quantiles. We then perform a long-range forecasting exercise for temperatures, which suggests that incorporating TVP or explicitly modelling quantile levels or the combination of both features can improve prediction for different parts of the temperature distribution. In addition, we produce forecasts for 2030 considering the intermediate RCP 4.5 scenario: given that no single specification dominates, we account for model uncertainty by considering forecast averaging across all specifications. Our approach allows us to make statements about the probability of temperature levels — for instance, we find that a scenario of +1.8 °C will occur with a non-negligible probability under RCP 4.5.

Working Papers

Consistent Specification Test for the Quantile Autoregression With No Omitted Latent Factors

This paper proposes a test for the joint hypothesis of correct dynamic specification and no omitted latent factors for the Quantile Autoregression. If the composite null is rejected we proceed to disentangle the cause of rejection, i.e., dynamic misspecification or an omitted variable. We establish the asymptotic distribution of the test statistics under fairly weak conditions and show that factor estimation error is negligible. A Monte Carlo study shows that the suggested tests have good finite sample properties. Finally, we undertake an empirical illustration of modelling growth and inflation in the United Kingdom, where we find evidence that factor augmented models are correctly specified in contrast with their non-augmented counterparts when it comes to GDP growth, while also exploring the asymmetric behaviour of the growth and inflation distribution.

Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach

This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across models using weights that minimise the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Quantile Regression Information Criterion (QRIC) as well as the leave-one-out cross validation criterion. The unobserved factors are estimated by principal components of a large panel with N predictors over T periods under a recursive estimation scheme.  We apply the aforementioned methods to the UK GDP growth and CPI inflation rate. We find that, on average, for GDP growth, in terms of coverage and final prediction error, the equal weights or the weights obtained by the AIC and BIC perform equally well but are outperformed by the QRIC and the Jackknife approach on the majority of the quantiles of interest. In contrast, the naive QAR(1) model of inflation outperforms all model averaging methodologies.

The Time-Varying Inflation Risks (currently R&R)

joint with Dimitris Korobilis, Bettina Landau & Alberto Musso

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to model flexibly the whole distribution of inflation. In order to make our approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state-space form of the TVP quantile regression into an equivalent high-dimensional regression form. An application of this methodology points to a good forecasting performance of quantile regressions with TVPs augmented with specific credit and money-based indicators for the prediction of the conditional distribution of inflation in the euro area, both in the short and longer run, and specifically for tail risks.

Persistent Cycles and Long-run Covariability in Paleoclimate Time Series (currently under revision)

joint with Vasco J. Gabriel & Luis F. Martins

Motivated by the presence of a strong cyclical component in paleoclimate data, this paper considers the problem of conducting cointegration inference when the data contains very large and persistent cycles. Our first contribution is to show, analytically and through Monte Carlos simulations, that while point estimation remains consistent, commonly applied tests no longer are valid when the data contains persistent cycles. Our second contribution is empirical: we propose the use of the long-run covariability approach of Muller & Watson (2018) to quantify low-frequency comovement amongst a range of paleoclimate times series. These methods allow us to focus on the long run properties of the data, bypassing short and medium run fluctuations, while being agnostic regarding the order of integration of the time series. We provide new estimates for the long-run relationship between temperatures and CO2, concluding that in the long-run a 100 ppm increase in CO2 levels would raise temperatures by around 1◦C. Finally, we illustrate how joint modelling of this set of paleoclimate time series can be carried out by factor analysis and how long-term projections about temperature increases and ice-sheet retreat can be constructed.

Work In Progress

Climate Change: Across Time and Frequencies

joint with Vasco J. Gabriel & Luis F. Martins

Modelling Low-Frequency Covariability of Paleoclimatic Data

joint with Vasco J. Gabriel & Luis F. Martins

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