Sticky Prices or Sticky Wages? An Equivalence Result
Florin Bilbiie, Mathias Trabandt
Review of Economics and Statistics,
forthcoming
Abstract
We show an equivalence result in the standard representative agent New Keynesian model after demand, wage markup and correlated price markup and TFP shocks: assuming sticky prices and flexible wages yields identical allocations for GDP, consumption, labor, inflation and interest rates to the opposite case- flexible prices and sticky wages. This equivalence result arises if the price and wage Phillips curves' slopes are identical and generalizes to any pair of price and wage Phillips curve slopes such that their sum and product are identical. Nevertheless, the cyclical implications for profits and wages are substantially different. We discuss how the equivalence breaks when these factor-distributional implications matter for aggregate allocations, e.g. in New Keynesian models with heterogeneous agents, endogenous firm entry, and non-constant returns to scale in production. Lastly, we point to an econometric identification problem raised by our equivalence result and discuss possible solutions thereof.
Read article
Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach
Katja Heinisch, Fabio Scaramella, Christoph Schult
IWH Discussion Papers,
No. 6,
2025
Abstract
Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts.
Read article
DPE Course Programme Archive
DPE Course Programme Archive 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2025 Causal Machine Learning Martin Huber April 10-11, 2025 (IWH) Microeconomics…
See page
Alumni
IWH Alumni The IWH maintains contact with its former employees worldwide. We involve our alumni in our work and keep them informed, for example, with a newsletter. We also plan…
See page
People
People Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Doctoral Students Afroza Alam (Supervisor: Reint Gropp ) Annika Backes (Supervisors: Simon…
See page
Management Board and Supervisory Board
Management Board and Supervisory Board As an association established and registered under German civil law the IWH is composed of different internal bodies through which it is led…
See page
Courses
Courses Courses are organised in coordination with partner institutions within the Central-German Doctoral Program Economics (CGDE) network. IWH organises First-Year Courses in…
See page
Lecturers
Lecturers at CGDE Institutions Jordan Adamson Assistant Professor at Institute for Empirical Economic Research, Leipzig University. Website Course: Econometrics (winter term…
See page
Teaching
Teaching Within the framework of its cooperations with both German and foreign universities IWH researchers are actively committed to teaching by offering academic courses. These…
See page
Tracking Weekly State-Level Economic Conditions
Christiane Baumeister, Danilo Leiva-León, Eric Sims
Review of Economics and Statistics,
No. 2,
2024
Abstract
This paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.
Read article
