Search Results
Discussion Paper
What Do Climate Risk Indices Measure?
As interest in understanding the economic impacts of climate change grows, the climate economics and finance literature has developed a number of indices to quantify climate risks. Various approaches have been employed, utilizing firm-level emissions data, financial market data (from equity and derivatives markets), or textual data. Focusing on the latter approach, we conduct descriptive analyses of six text-based climate risk indices from published or well-cited papers. In this blog post, we highlight the differences and commonalities across these indices.
Discussion Paper
Measuring and Managing COVID-19 Model Risk
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation's (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Working Paper
The Economic Effects of Trade Policy Uncertainty
We study the effects of unexpected changes in trade policy uncertainty (TPU) on the U.S. economy. We construct three measures of TPU based on newspaper coverage, firms' earnings conference calls, and aggregate data on tari rates. We document that increases in TPU reduce investment and activity using both firm-level and aggregate macroeconomic data. We interpret the empirical results through the lens of a two-country general equilibrium model with nominal rigidities and firms' export participation decisions. In the model as in the data, news and increased uncertainty about higher future ...
Working Paper
A new monthly indicator of global real economic activity
In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian?s index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and ...
Working Paper
Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs
We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model ...
Journal Article
Measuring and Managing COVID-19 Model Risk
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation's (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Working Paper
The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes
We assess the causal impact of epidemic-induced lockdowns on health and macroeconomic outcomes and measure the trade-off between containing the spread of an epidemic and economic activity. To do so, we estimate an epidemiological model with time-varying parameters and use its output as information for estimating SVARs and LPs that quantify the causal effects of nonpharmaceutical policy interventions. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. We find that additional government mandated mobility curtailments would have reduced deaths at a very small cost in ...
Working Paper
Minimum Distance Estimation of Dynamic Models with Errors-In-Variables
Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the model and the least squares biases. In cases when this mapping is not analytically tractable, a special algorithm is designed to simulate the latent predictors without completely ...
Discussion Paper
Measuring and Managing COVID-19 Model Risk
One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation’s (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
Working Paper
Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach
This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information in this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows us to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show ...