Data and code for "Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model"

Principal Investigator(s): View help for Principal Investigator(s) Ina Hajdini, Federal Reserve Bank of Cleveland

Name	File Type	Size	Last Modified
CEE			04/03/2025 03:59:PM
Data			04/03/2025 05:19:PM
Figures			04/03/2025 04:22:PM
Post estimation			04/03/2025 04:16:PM
RE			04/03/2025 05:03:PM
SAC			04/03/2025 04:08:PM
Section-3			12/12/2024 01:19:PM
Unrestricted			04/03/2025 04:11:PM
routines			10/02/2024 07:04:PM
README.pdf	application/pdf	206.1 KB	04/03/2025 02:43:PM

Project Citation:

Hajdini, Ina. Data and code for “Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model.” Nashville, TN: American Economic Association [publisher], 2026. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2026-03-05. https://doi.org/10.3886/E209448V1

Project Description

Summary:

The paper considers a New Keynesian framework in which agents form expectations based on a combination of autoregressive mis-specified forecasts and myopia. The proposed expectations formation process is shown to be consistent with all three empirical facts on consensus inflation forecast errors: delayed over-shooting to shocks, under-reaction to ex-ante forecast revisions, and over-reaction to recent events. However, while mis-specified forecasts can be both sufficient and necessary to match all three facts, myopia alone is neither. The paper then derives the general equilibrium solution consistent with the proposed expectations formation process and estimates the model with likelihood-based Bayesian methods, yielding three results:

(i) macroeconomic data strongly prefer a combination of autoregressive mis-specified

forecasting rules and myopia over other alternatives; (ii) learning of mis-specified forecasting rules improves model fit relative to the consistent expectations equilibrium; and (iii) the proposed expectations generate internal persistence and amplification to exogenous shocks. The posterior distribution of the model-implied inflation expectations fits survey data well and reveals that learning of mis-specified forecasting rules is preferred in terms of replicating all three empirical facts on forecast errors.

Scope of Project

Subject Terms: mis-specified forecasts; myopia; inflation expectations; Bayesian estimation; Internal propagation

JEL Classification:
      C11 Bayesian Analysis: General
      C53 Forecasting Models; Simulation Methods
      D84 Expectations; Speculations
      E10 General Aggregative Models: General
      E30 Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)
      E50 Monetary Policy, Central Banking, and the Supply of Money and Credit: General
      E70 Macro-Based Behavioral Economics: General

Geographic Coverage: Unites States of America

Time Period(s): 12/1954 – 12/2018 (Quarterly data)

Collection Date(s): 1/5/2024 – 2/5/2024 (This is a rough estimate of the timeline when the final datasets were put together.)

Universe: Real GDP
GDP implicit price deflator
Civilian noninstitutional population
Annual Fed funds rate
Consumers' annual inflation expectations
Professional forecasters annual inflation expectations

Data Type(s): aggregate data; survey data

Collection Notes: Data was collected from three sources:
- FRED, Federal Reserve Bank of St. Louis

- Survey of Professional Forecasters, Federal Reserve Bank of Philadelphia

- Survey of Consumers, University of Michigan

Methodology

Unit(s) of Observation: Real GDP in billions of 2012 dollars; GDP implicit price deflator index, 2012 = 100; Civilian noninstitutional population in thousands; Annual Fed funds rate in percent; Consumers' annual inflation expectations in percent; Professional forecasters annual inflation expectations in percent

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Published Versions

V1 [2026-03-05]

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