In this paper, we first utilize a dynamic factor model with stochastic volatility (DFM‐SV) to filter out the national factor from the local components of weekly state‐level economic conditions indexes of the United States (US) over the period of April 1987 to August 2021. In the second step, we forecast the state‐level factors in a panel data set‐up based on the information content of corresponding state‐level climate risks, as proxied by changes in temperature and its SV. The forecasting experiment depicts statistically significant evidence of out‐of‐sample predictability over a one‐month‐ to one‐year‐ahead horizon, with stronger forecasting gains derived for states that do not believe that climate change is happening and are Republican. We also find evidence of national climate risks in accurately forecasting the national factor of economic conditions. Our analyses have important policy implications from a regional perspective.
SummaryWe propose a novel econometric approach to estimating time‐varying policy effects using external instruments in the presence of time‐varying instrument relevance in a factor‐augmented VAR model with data on the United States, Canada, Germany, Japan, and the United Kingdom. We find that US monetary policy shocks are an important driver of the exchange rate movements, with no delayed overshooting. We show that estimates of spillover effects of US monetary policy shocks on the inflation and real economic activity would be distorted without considering time variation in instrument relevance, and time variation in policy effects reflects primarily varying shock size, not their transmission.
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