Semi-Structural VAR and Unobserved Components Models to Estimate Finance-Neutral Output Gap

Abstract

The paper assesses the impact of adding information on financial cycles on the output gap estimates for eight advanced economies using two unobserved components models: a reduced form extended Hodrick-Prescott filter, and a standard semi-structural unobserved components model. To complement these models, a semi-structural vector autoregression model is proposed in which only supply shocks are identified. The accuracy of the output gap estimates is assessed based on their performance in predicting recessions. The models with financial variables generally produce more accurate output gap estimates at the expense of increased real-time volatility. While the extended Hodrick-Prescott filter is particularly appealing for its real-time stability, it lags behind the two semi-structural models in terms of forecasting performance. The vector autoregression model augmented with financial variables features the best in-sample forecasting performance, and it has similar real-time prediction capabilities to the semi-structural unobserved components model. Overall, financial cycles appear to be relevant in Japan, Spain, the UK, and -to a lesser extent -in the US and in France, while they are relatively muted in Canada, Germany, and Italy.

Unobserved Components Model(S): Output Gaps and Financial Cycles

“…By contrast, the small UCM which embeds the real house prices variable (model 6) shows very unreliable PC-inflation forecasts. Both models 5 and 6 support the evidence from the literature that suggests that FNOGs may differ from standard output gap estimates (Katay et al, 2020) and therefore might not be adequate gauges of inflationary pressures. The output gaps estimated with the larger UCM (models 6 and 7) prove to have good predictive inflation performance in comparison with the EC output gap, although the forecast errors are larger than for the output gaps of the small UCMs.…”

“…This is also what our work will highlight. Katay et al (2020) also show that FNOGs may be not robust to alternative assumptions for some countries in their sample.…”