Money and real activity are strongly correlated at business cycle frequencies. The strength of this correlation has led the profession to a widespread use of models employing nominal rigidities in an attempt to explain salient macroeconomic phenomena. Two distinct approaches to modeling nominal rigidities have been used in earlier work.
The first approach, exemplified by the work of Fisher (1977) and Taylor (1980), assumes that the timing of price changes is independent of shocks affecting the firm in each period. Institutional restrictions or information-gathering costs prevent firms from meeting too often and instead, firms have predetermined schedules of price adjustment. These are the so-called time-dependent models, which, because of their computational tractability, have received most of the profession’s attention in the last two decades.
The second tradition, dating back to Sheshinski and Weiss (1977), assumes that observing the state of the world is inexpensive, but that firms incur fixed physical costs of price adjustment every time they undergo a new price change. This second generation of models, the so-called state-dependent models, are grounded in solid micro-foundations as they explicitly model the source of nominal rigidities, but have been, with a few notable exceptions, neglected by the profession because of their computational complexity.
Despite the fact that most policy-oriented macroeconomic models use the time dependent assumption of an exogenous timing of price changes, the distinction between state and time-dependent sticky price models is not innocuous. Caplin and Spulber (1987) show that under special assumptions about the distribution of firm prices and the stochastic process of the money supply, a monetary expansion has no effect on output: although few firms adjust in response to the shock, the firms that do adjust are those that need the largest changes in their nominal prices and the aggregate price level in their economy grows at the same rate as the money supply.
Recent research, grounded in explicit household and firm maximization, and using more realistic stochastic forcing processes calibrated from the US data, has overturned this neutrality result, but nevertheless reaches the conclusion that state-dependent pricing models generate smaller real effects from monetary shocks. In Dotsey, King and Wolman (1999), firms synchronize prices in response to aggregate disturbances, and increase price in tandem in response to large aggregate disturbances. Golosov and Lucas (2004) solve a state-dependent pricing model in which firms are subject to marginal cost shocks and find that the model, calibrated to match microeconomic data on the size and frequency of price changes, generates very little output volatility.
CONTENTS
Abstract
Acknowledgments
Vita
List of Tables
List of Figures
Chapters:
1. Is Firm Pricing State or Time-Dependent? Evidence from US Manufacturing 1
- 1.1 Introduction
1.2 A Test of State-Dependent Pricing Models
- 1.2.1 Heuristic Example
1.2.2 A Partial Equilibrium Model
1.3 Measures of technology shocks
- 1.3.1 Data
1.3.2 Measuring technology shocks
1.3.3 Relationship with other evidence
1.4 Testing the State-Dependent Pricing Model
- 1.4.1 Empirical Results
1.4.2 Robustness Checks
1.5 Conclusion
2. Menu Costs, Multi-Product Firms, and Aggregate Fluctuations
- 2.1 Introduction
2.2 Data
- 2.2.1 The Size and Frequency of Price Changes
2.2.2 Ex-ante heterogeneity?
2.2.3 Relationship with other evidence
2.3 Evidence of synchronization
2.4 A Multi-Product State-Dependent Pricing Model
- 2.4.1 Model Economy
2.4.2 Equilibrium
2.4.3 Computing the Equilibrium
2.5 Quantitative Results
- 2.5.1 Calibration and Parametrization
2.5.2 Results
2.5.3 Counterfactual Experiments
2.5.4 Leptokurtic Shocks or Multi-Product Firms?
2.6 Conclusion
3. International Price Dispersion in State-Dependent Pricing Models
- 3.1 Introduction
3.2 The Model
- 3.2.1 Setup
3.2.2 Equilibrium
3.3 The relationship between RPV and NER volatility
- 3.3.1 Parametrization and calibration
3.3.2 Results
3.4 The Data
- 3.4.1 Evidence from Micro-price data
3.4.2 Evidence from Macro-Data
3.4.3 Discussion
3.5 Conclusion
Appendices:
- A. Solving the State-Dependent Model
B. Standard Errors for Two-Stage Estimates
- C. Scanner Price Data
- C.1 Construction of Price Series
- C.1.1 AC Nielsen
C.1.2 Dominick’s
C.2 Time-Aggregation and Treatment of Sales
Bibliography
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