1. The use of Macro-Economic Modelling at RBNZ
Grant Spencer
Forecasting Bootcamp - International training
23-27 Sept 2019, Wellington, New Zealand

2. The Use of Economic Modelling at RBNZ
Purpose of modelling
RBNZ modelling history
Common characteristics of RB macro models
RBNZ’s forecasting - policy process
Pros and Cons of model-based policy process
Current issues with RB’s modelling framework
Future direction of modelling?

3. Be clear on purpose of modelling
Alternative purposes of economic/financial modelling
Forecasting tool
Story telling – eg to describe the drivers of growth and inflation
Policy decision making (eg OCR decisions)
Economic analysis – eg via simulation experiments
Financial sector stress testing
RBNZ has used macro-models mainly for the first 3 purposes

4. RBNZ - macro-modelling since 1970’s
International surge in macro-econometric modelling in 1970s facilitated by development of computers and quarterly national accounts. Led by the US (Lawrence Klein, Wharton model, FRB-MIT model)
Early RBNZ efforts required construction of quarterly national accounts data for NZ
RB models became large and relatively ad-hoc before reducing to smaller structures that were more theoretically coherent
RBNZ built a DSGE model in but it was not good at forecasting and not very useful for policy analysis/ story-telling
GFC not well handled by RB(and other CB) macro-models due to weak representation of financial sector linkages

5. Common characteristics of RBNZ models
In the Keynesian demand-side tradition: Y = C + I + G +X – M
Simple supply-side representation with 2-3 “products:, eg one non-traded good and two traded (export and import) goods
Model explains short-term (2-3 year horizon) dynamics around simple long term trends
Endogenous OCR since 1999; Exch rate partially endogenous
Key exogenous variables: World GDP, export volumes, import and export prices (in USD), net migration, Govt spending, tax rates
Two key monetary policy transmission channels: housing and the exchange rate

6. RBNZ’s forecasting – policy process
Quarterly forecasting/MPS/OCR decision cycle with 6 weekly interim OCR decisions based on forecast “updates”
Economics Dept presents “first pass” forecast to MPC plus internal advisory group. Forecasts unpicked over several days. Focus on forecasts, not policy. Outcome is “second pass” (ie final) forecast, owned by MPC
MPC plus smaller internal advisory group then meets to discuss policy decision, communications, potential financial market impact
MPC makes OCR decision and agrees text of press release and MPC minutes
Governor presents decision at press conference and fronts parliamentary committee
Unpicking of forecasts includes testing assumptions re projections of TOT, world growth, immigration, export volumes, savings rate, house prices, participation rate, exchange rate. Also persistence/decay of recent forecast errors,

7. Pros and cons of model-based policy process
Provides a consistent and coherent framework for reconciling the large amount of information relevant to Monetary policy decisions
Facilitates story telling about how recent information is likely to affect future growth and inflation outcomes
Simple model structure allows MPC to easily assess the impact of alternative assumptions re exogenous variables and model parameters
Cons
New MPC members may find the process complex and impenetrable – risk that MPC members may be captured by the model “auto-pilot”
RB’s self-imposed discipline of publishing exch rate and int rate tracks can be restrictive
Risk of ignoring factors that are not part of the model, eg supply-side, confidence and “balance-sheet” factors
Publication of the OCR track is a double-edged sword: It adds transparency and credibility to the policy story, but can also complicate the Bank’s policy communications

8. Current issues with RB modelling framework
Supply side modelling is weak at a time when the economy is being impacted by significant supply shocks. (Positive supply shocks from globalization and digitisation). Output gap (Q-Q*) is not well identified
Absence of a Labour market in the model – this is changing with the new dual mandate but the output gap (not unemployment) remains the key transmission channel
Significant movement and uncertainty around key equilibrium values: U* (NAIRU) and r* (equilibrium real interest rate)
Simplicity of financial sector transmission channels. No credit effects, no distinction between substitution and cash flow effects of int rate changes
Uncertainty and risk are not well handled

9. Future direction of modelling?
IT advances are facilitating more disaggregated models of specific markets (eg Labour mkt), larger DSGE models and agent-based computational models. However, policy makers are likely to continue using simple macro-models for forecasting and story telling
Expect increasing use of “big data” and AI in financial regulation (Regtech), including financial sector modelling for stress testing (eg Federal Reserve collects micro-financial data)
Potential to integrate macro and financial system models to support analysis of macro-financial stability