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Abstract
In this paper we propose a parsimonious way of combining survey expectations with empirical models to produce forecasts. We do so by augmenting a traditional vector autoregression model with forecasts for different variables and horizons from the ECB Survey of Professional Forecasters. The additional information improves estimation efficiency while maintaining a treatable model. In terms of forecasting performance, the gains from adding survey forecasts are greater at the one and two year ahead horizons, while they are limited at shorter horizons (below one year). Larger gains are found in terms of density performance than in terms of point. Forecasts of real GDP growth benefit the most from survey information, whereas inflation forecasts are improved the least. This latter result is partially driven by the very poor performance of SPF during the 2022 high inflation period. Forecasts for unemployment also benefit from including expectations for GDP and inflation, although not during the COVID pandemic period.
Keywords: Expectations, Forecasting, Judgement, Survey of Professional Forecasters
JEL codes: C32, C33, C51, D84, E37
Forecasting with the help of survey information
The term structure of judgement: interpreting survey disagreement
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Abstract
Consensus forecasts by professionals are highly accurate, yet hide large heterogeneity. We develop a framework to extract the judgement component from survey forecasts and analyse the extent to which it contributes to respondents’ disagreement. For the average respondent, we find a substantial contribution of judgement about the current quarter, which often steers unconditional forecasts towards the realisation, thereby improving accuracy. We identify the structural components of judgement by exploiting stochastic volatility and give an economic interpretation to expected future shocks. For individual respondents, just over one-third of the disagreement is due to differences in the coefficients or models used, and the remainder is due to different assessments of future shocks; the latter mostly concerns the size of the shocks, while there is general agreement on their source.
Keywords: Expectations Formation, Identification via Stochastic Volatility, Judgement, Survey of Professional Forecasters
JEL classification: C32, C33, C51, D84, E37
Striking a Bargain: Narrative Identification of Wage Bargaining Shocks
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Abstract
We quantify wage bargaining shocks’ effects on macroeconomic aggregates in Germany using a structural vector auto-regression model. We identify exogenous variation in bargaining power from episodes of minimum wage introduction and industrial disputes. This disciplines the impulse responses of unemployment and output, and sharpens inference on the behaviour of other variables, which is consistent with theoretical predictions from search and matching models. We find that wage bargaining shocks are an important contributor to agregate fluctuations in unemployment and inflation, exhibit close to full passthrough to consumer prices, and imply plausible dynamics for the vacancy rate, firms’ profits, and the labor share.
Keywords: Wage bargaining, minimum wage, industrial action, narrative restrictions, structural vector autoregression.
JEL classification: J2, J3, E32, C32.