Forecast Decomposition

Separate from the standard Forecasting routines, we have also implemented a function decompose_forecast for explaining why a forecast changes as new data becomes available and new estimations are run. Please note that this function does not decompose a forecast into the shocks that produce it. For example, if you want to understand whether TFP or financial shocks are driving a forecast, then you should be calculating the shock decomposition output variable (see Calculating Shock Decompositions).

DSGE.decompose_forecast — Function

decompose_forecast(m_new, m_old, df_new, df_old, input_type, cond_new, cond_old,
    classes; verbose = :low, kwargs...)

decompose_forecast(m_new, m_old, df_new, df_old, params_new, params_old,
    cond_new, cond_old, classes; check = false)

explains the differences between an old forecast and a new forecast by decomposing the differences into three sources:

(1) Data revisions, (2) News (e.g. new data that has become available since the old forecast), (3) Re-estimation (i.e. changes in model parameters).

This function does not compute which shocks explain a forecast. For example, if you want to know whether TFP or financial shocks drive a given forecast, then you want to compute the shock decomposition output variable (see ?shock_decompositions, forecast_one, and compute_meansbands).

Note that this function currently does not work for a model in which there are changes in the degree of "regime-switching" in the TTT, RRR, CCC, ZZ, and DD matrices, e.g. decomposing the changes in the forecast when the monetary policy rule changes or if a temporary policy is implemented that did not occur in the old forecast.

Inputs

m_new::M and m_old::M where M<:AbstractDSGEModel
df_new::DataFrame and df_old::DataFrame
cond_new::Symbol and cond_old::Symbol
classes::Vector{Symbol}: some subset of [:states, :obs, :pseudo]

Method 1 only:

input_type::Symbol: estimation type to use. Parameters will be loaded using load_draws(m_new, input_type) and load_draws(m_old, input_type) in this method

Method 2 only:

params_new::Vector{Float64} and params_old::Vector{Float64}: single parameter draws to use

Keyword Arguments

check::Bool: whether to check that the individual components add up to the correct total difference in forecasts. This roughly doubles the runtime

Method 1 only:

verbose::Symbol

Outputs

The first method returns nothing. The second method returns decomp::Dict{Symbol, Matrix{Float64}}, which has keys of the form :decomp<component><class> and values of size Ny x Nh, where

Ny is the number of variables in the given class
Nh is the number of common forecast periods, i.e. periods between date_forecast_start(m_new) and date_forecast_end(m_old)

source

For an example of how to use this functionality, see decompose_forecast.jl on the Github page (or directly inside the directory where DSGE.jl has been downloaded).