The general equilibrium model developed by Golosov et al. (2014), GHKT for short, is modified to allow for additional negative impacts of global warming on utility and productivity growth, mean reversion in the ratio of climate damages to production, labour-augmenting technical progress, and...

Employing an endogenous growth model with human capital, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate fluctuations in output, consumption, investment and hours. Given the importance of accounting for both the...

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying...

In recent years there has been increasing concern about the identification of parameters in dynamic stochastic general equilibrium (DSGE) models. Given the structure of DSGE models it may be difficult to determine whether a parameter is identified. For the researcher using Bayesian methods, a...

We use perturbation methods to derive a rule for the optimal risk-adjusted social cost of carbon (SCC) that incorporates the effects of uncertainties associated with climate and the economy from a calibrated DSGE model. We allow for different aversions to risk and intertemporal fluctuations,...

We examine the dynamic effects and empirical role of TFP news shocks in the context of frictions in financial markets. We document two new facts using VAR methods. First, a (positive) shock to future TFP generates a significant decline in various credit spread indicators considered in the...

This paper analyzes the optimal level of public debt when taxes are used not only for funding public expenditures but also for correcting externalities from climate change. Taking into account externalities implies that the optimal policy deviates from tax smoothing. Provided cumulative marginal...

We propose two novel methods to "bring ABMs to the data". First, we put forward a new Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time...