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Discussion of:

Discussion of:. Reconciling micro-data and macro estimates of price stickiness Discussant: Iulia Pasa. Summary. Reconcile the micro data on price setting with estimates from a macro model. The Calvo framework is extensively used in many DSGE models.

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Discussion of:

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  1. Discussion of: Reconciling micro-data and macro estimates of price stickiness Discussant: Iulia Pasa

  2. Summary • Reconcile the micro data on price setting with estimates from a macro model. • The Calvo framework is extensively used in many DSGE models. • The aggregate Phillips curve appears to overstate price stickiness • Ignoring heterogeneity has consequences

  3. Methodology • Introduce into a standard model: - heterogeneity across firms - a richer production structure, incorporating intermediate goods • Calibrate the model using the micro data, and simulate macro aggregates. • Compare these macro estimates to the calibrated true values.

  4. Comparisons on Calvo probabilities • Macro Evidence: – Assumption – all firms reset the prices with the same probability – in theory macroec. estimate of aggregate price stickiness should be less than the microeconomic based estimate - In practice - opposite NKPC based estimates tend to imply much more price stickiness

  5. Comparisons on Calvo probabilities (cont.) • Micro Evidence: Jensen’s inequality tells us that when firms face different Calvo probabilities, θmicro will be greater than the average Calvo probability. • Micro and Macro in tension

  6. Relax the tension Better way to calculate the coefficient on marginal costs for each of the sectorial NKPCs, and then use the weighted average of these sectorial coefficients as the coefficient on aggregate real marginal costs for the aggregate NKPC, in which case one should obtain θmacrotheory

  7. Why Do Prices Look So StickyThrough the Lense of Calvo? • In Aggregate Data, Price Seems to Respond Very Little to Marginal Cost • Calvo Interprets this as Reflecting Price Setting Frictions

  8. Agents Households Final goods firms Intermediate goods firms Monetary authority Sources of uncertainty Consumption shock Aggregate technology shock for i.g.f Sector specific technology shock for i.g.f M.P. shock The Model

  9. The model (cont.) • Complex interdependence between firms within and across sectors; • Each sector characterized by its production technology and Calvo probability; • Parameters were calibrated; • A key part – heterogeneity in Calvo probability across sectors

  10. Properties of the model • Hazard functions - downward sloping precisely because of the sampling bias induced by heterogeneity; • Moments of the simulated data – growth in GDP/capita, inflation, nominal interest rate - inclusion of heterogeneity diminishes the persistence of inflation • Impulse response functions - initial response of value added to an aggregate shock is larger when heterogeneity or roundabout production is present. When combined, the two features appear to increase the persistence of value added responses to aggregate shocks.

  11. Conclusion • Very Interesting Paper! • Heterogeneity and roundabout production have a non-trivial effect on model dynamics • Calvo probability used in most calibrated models is likely not accurate • The model helps resolve some of discrepancy between micro/macro data • We Also Need to Know About Wages!

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