This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model.
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model.
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model.
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model.
The estimation of dynamic stochastic general equilibrium (DSGE) models is a computationally demanding task. As these models change to address new challenges (such as household and firm heterogeneity, the lower bound on nominal interest rates, and occasionally binding financial constraints), they become even more complex and difficult to estimate—so much so that current estimation procedures are no longer up to the task. This post discusses a new technique for estimating these models which belongs to the class of sequential Monte Carlo (SMC) algorithms, an approach we employ to estimate the New York Fed DSGE model.
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model.