Lumpy Price Adjustments: A Microeconometric Analysis

Dhyne, Emmanuël; Fuss, Catherine; Pesaran, M. Hashem; Sevestre, Patrick

doi:10.2139/ssrn.1688935

Cited by 102 publications

(3 citation statements)

References 87 publications

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“…Aucremanne and Druant (2005) proxy the deviation of the current price from its desired level by the accumulated rate of inflation. Dhyne et al (2006) use econometric techniques to identify the size of shocks, both common and idiosyncratic, from the observations of size and frequency of price adjustments in the micro data underlying the calculation of the CPI. such as dummies for changing prices every n periods, to proxy for Taylor contracting, and seasonal dummies.…”

Section: Introductionmentioning

confidence: 99%

When do firms adjust prices? Evidence from micro panel data

Lein

2010

Journal of Monetary Economics

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This paper analyzes the price setting behavior of firms using data from a large panel of quarterly firm surveys from 1984 to 2006. These data allow to track changes in firms' prices, their price expectations and several other firm-specific developments such as changes in costs for input products and capacity utilization rates. The analysis shows that state dependent pricing is clearly important and that variables measuring the current situation of the firm add a lot to the explanatory power of a price adjustment probability model, compared to purely time dependent features. Although the rate of inflation is a significant explanatory variable, the inclusion of macroeconomic variables adds only marginally to the explanatory power of the model with the firm specific variables. Furthermore, when taking into account sticky plan models by excluding possibly predetermined price changes, the importance of state dependent factors becomes even larger. The data also display features that suggest that sticky information plays a role for price setting.JEL classification: E31, E32, E50.

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Section: Introductionmentioning

confidence: 99%

When do firms adjust prices? Evidence from micro panel data

Lein

2010

Journal of Monetary Economics

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“…This result hints at the relevance of menu cost models. Likewise, menu costs are found to affect firm decisions in an analysis of Dhyne et al (2011) and Honoré et al (2012).…”

Section: Introductionmentioning

confidence: 97%

Price Changes - Stickiness and Internal Coordination in Multiproduct Firms

Letterie

Nilsen

2016

SSRN Journal

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We assess empirically the micro-foundations of producers' sticky pricing behaviour. We account for various functional forms of menu costs. The focus is on the analysis of multiproduct plants, and the menu costs therefore also allow for economies of scope. The structural model developed is tested using monthly product-and plant-specific producer prices for Norwegian plants. We find evidence of linear and fixed menu costs that account for inaction of price adjustment. Convex menu costs are statistically significant but of moderate importance. Finally, our estimates suggest economies of scope in adjusting prices resulting in (incomplete) synchronization of price changes.

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“…Despite the range of tests available, little conclusive evidence exists on whether commodity price cycles are duration dependent or not. In particular, it can be difficult to distinguish between irregular cycles and other explanations for dynamic responses to exogenous shocks like lumpy prices (Dhyne et al 2011). Also, rejecting duration dependence requires observations over multiple cycles, and as such a substantial time-series of observations.…”

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confidence: 99%

Cyclical non-stationarity in commodity prices

Øglend

Asche

2016

Empir Econ

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This paper applies the Hylleberg et al. (J Econom 44 (1): 1990) parametric seasonal unit root test to test for cyclical non-stationarity in commodity prices. The testing procedure is simple and involves evaluating various linear restrictions on lagged price levels in an error correction model of prices, equivalent to the Augmented Dickey-Fuller test. Unit root behaviour at low frequencies implies cyclical non-stationarity. In our empirical application, we fail to reject unit roots at frequencies associated with 3-to 5-year-long price cycles for 7 of 16 major commodities. Our results suggest that longer cycles in many commodity prices are highly stochastic, and care should be taken when interpreting the regularity and out-of-sample predictability of such cycles using historical price movements.

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Lumpy Price Adjustments: A Microeconometric Analysis

Cited by 102 publications

References 87 publications

When do firms adjust prices? Evidence from micro panel data

When do firms adjust prices? Evidence from micro panel data

Price Changes - Stickiness and Internal Coordination in Multiproduct Firms

Cyclical non-stationarity in commodity prices

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