Sticky Prices and Monetary Policy

This paper by Boivin, Giannoni, and Mihov looking at price stickiness at the aggregate and sectoral levels is from the NBER. Here's the abstract followed by the introduction and conclusion along with a few brief comments about the paper.

The paper uses factor augmented vector autoregression to investigate price stickiness at the sectoral and aggregate levels. The paper's results on the price puzzle alone are worth the price of admission, at least for me, but there's much more than that. In particular, the finding that prices respond quickly to sectoral shocks and sluggishly to aggregate shocks explains why studies of disaggregated prices find much more price flexibility than studies of aggregate prices. That is, most of the short-run fluctuations in sectoral prices - 85% by the estimates below - are due to sectoral shocks. Aggregate shocks from sources such as monetary policy only explain 15% of the variation. But this 15% is very persistent and drives the persistence observed in sectoral inflation:

Sticky Prices and Monetary Policy: Evidence from Disaggregated U.S. Data, by Jean Boivin, Marc Giannoni, and Ilian Mihov, NBER WP 12824,  January 2007: Abstract This paper disentangles fluctuations in disaggregated prices due to macroeconomic and sectoral conditions using a factor-augmented vector autoregression estimated on a large data set. On the basis of this estimation, we establish eight facts: (1) Macroeconomic shocks explain only about 15% of sectoral inflation fluctuations; (2) The persistence of sectoral inflation is driven by macroeconomic factors; (3) While disaggregated prices respond quickly to sector-specific shocks, their responses to aggregate shocks are small on impact and larger thereafter; (4) Most prices respond with a significant delay to identified monetary policy shocks, and show little evidence of a "price puzzle," contrary to existing studies based on traditional VARs; (5) Categories in which consumer prices fall the most following a monetary policy shock tend to be those in which quantities consumed fall the least; (6) The observed dispersion in the reaction of producer prices is relatively well explained by the degree of market power; (7) Prices in sectors with volatile idiosyncratic shocks react rapidly to aggregate monetary policy shocks; (8) The sector-specific components of prices and quantities move in opposite directions. [Open link]

1 Introduction In this paper, we document the effects of macroeconomic fluctuations on disaggregated prices. Whether prices are generally flexible or sticky has been for a long time the subject of considerable controversy in macroeconomics. A proper assessment of the speed of price adjustment is crucial to understand the sources of business cycle fluctuations, as well as the effects of monetary policy on the economy.

Numerous studies focusing on specific wholesale or retail items have found evidence of prices maintained fixed for several months, in the U.S.[1] Surveys of firms also suggest that a large fraction of prices remain constant for many months (Blinder, Canetti, Lebow, and Rudd, 1998). In addition, studies involving vector autoregressions (VAR) usually provide evidence of stickiness of the aggregate price level. For instance, under a wide range of identifying assumptions, following an unexpected monetary policy tightening, aggregate price indices are commonly found to remain unchanged for about a year and a half, and start declining thereafter (see, e.g., Christiano, Eichenbaum and Evans, 1999). Largely motivated by this evidence, many macroeconomic models including models used for policy analysis rest on the assumption that prices are sticky. Such models, sometimes augmented with mechanisms to increase the persistence in inflation, have been argued to replicate many features of aggregate data (e.g., Rotemberg and Woodford, 1997; Christiano, Eichenbaum and Evans, 2005; Smets and Wouters 2004), and in particular the delayed and persistent effects of monetary policy shocks on prices.

However, recent evidence on disaggregated prices series has cast doubts on the validity of existing models with price rigidities. For instance, Bils and Klenow (2004) find that disaggregated consumer prices are much more volatile than conventionally assumed in studies based on aggregate data. In fact, looking at 350 categories of consumer goods and services that cover about 70% of U.S. consumer expenditure, Bils and Klenow (2004) estimate that the median time between price changes is 4.3 months.[2] The duration between price changes varies however considerably across sectors.[3] Bils and Klenow (2004) argue that sectoral inflation rates are much more volatile and short-lived than implied by simple sticky-price models. Klenow and Kryvtsov (2005) document that when prices change, they change by more than 13% on average, or by 8.5% when adjusting for temporary sales. Golosov and Lucas (2003), in turn, calibrate a menu-cost model with both aggregate and idiosyncratic shocks to match these facts, and find that monetary policy shocks have large and rapid effects on aggregate prices but only very little effect on economic activity.

The evidence about relatively flexible individual prices thus contrasts sharply with the evidence obtained from aggregate price indices. While simple sticky-price models designed to explain aggregate price behavior appear to explain poorly the behavior of more disaggregated price series, models with relatively flexible sectoral prices do not seem to explain the empirical evidence obtained from aggregate series.

How then, can the facts just laid out be reconciled? One possibility is that studies based on aggregate series mistakenly assume that prices are sticky in the face of macroeconomic fluctuations, when in fact prices adjust more frequently to changes in economic conditions. In such a case, sectoral prices would be expected to respond on average rapidly to macroeconomic disturbances such as monetary policy shocks. And they would be expected to respond more rapidly in sectors that adjust prices more frequently. Another possibility is that prices respond differently to sectoral and macroeconomic shocks. In that case, individual prices may respond rapidly and strongly to shocks specific to the particular price categories, but may adjust more slowly to aggregate macroeconomic factors.

In addition, while aggregate inflation is often argued to be persistent over long samples,[4] disaggregated series appear much more transient. Several authors have argued that the apparent persistence of aggregate inflation may reflect an aggregation bias or a structural break in the mean inflation during the sample.[5] Yet, as another possible explanation, the differences in inflation persistence at the aggregate and disaggregate level may also be due to different responses to macroeconomic and sector-specific shocks.

One limitation of the existing evidence such as that of Bils and Klenow (2004), Klenow and Kryvtsov (2005) is that while they provide a careful description of individual prices movements, they do not distinguish between sector-specific and aggregate sources of fluctuations. It thus not possible to infer from these studies whether sectoral prices respond rapidly or slowly, strongly or moderately to macroeconomic shocks. Such distinctions would however provide crucial insights on the determination of prices, hence guidance for the development of appropriate macroeconomic models.

In this paper, we disentangle the fluctuations in disaggregated U.S. consumer and producer prices which are due to aggregate macroeconomic factors from those due to sectoral conditions. We do so by estimating a factor-augmented vector autoregression (FAVAR) that relates a large panel of economic indicators and individual price series to a relatively small number of estimated common factors. This framework allows us to assess the relative importance of macroeconomic and sectoral disturbances in determining disaggregate price fluctuations, and to decompose the persistence in inflation in terms of macroeconomic and sector-specific factors. Using this, we can analyze the typical response of disaggregate prices to macroeconomic shocks and to sector-specific shocks.

In addition, we estimate the effects of U.S. monetary policy on disaggregated prices after identifying monetary policy shocks using the information from the entire data set. We study the magnitude of the price responses to monetary policy shocks, and whether monetary policy has delayed effects on prices. While extensive research has attempted to characterize the effects of monetary policy on macroeconomic indicators, little research has analyzed its effects on disaggregated prices. Two exceptions are Bils, Klenow and Kryvtsov (2003), and Balke and Wynne (2003).

These authors estimate the responses of individual prices to a monetary policy shock by appending individual price series to a separately-estimated VAR. However, their estimated price responses display a considerable “price puzzle”, i.e., a price increase following an unexpected monetary policy tightening, which stands in sharp contrast to predictions of conventional models. As argued in Sims (1992) and Bernanke, Boivin and Eliasz (2005), such evidence of a price puzzle may be indicative of VAR misspecification due, e.g., to the lack of information considered in the VAR estimation. In the context of our data-rich FAVAR, this risk of misspecification is considerably reduced, as we use all of the available information in the estimation. Consistency of our estimates is furthermore guaranteed by the fact that we estimate within the same framework the parameters describing the dynamics of the common factors and the parameters that relate the individual price series to common factors.

After documenting the responses of prices to a monetary policy shock, we attempt to provide an explanation for the cross-sectional dispersion of price responses. To this end, we collect data on industry characteristics that are related to various theories of price stickiness. In general, models that allow for imperfect competition and variable speed of price adjustment predict that firms in very competitive industries will react quickly to changes in the economic environment (see Barro, 1972). The standard workhorse monetary model with Calvo pricing assumes a fixed degree of price stickiness as measured by the probability of re-optimizing prices, so that industry characteristics do not affect this probability. Extensions of this model allow differences in probabilities of re-optimizing prices across sectors (see, e.g., Aoki, 2001; Benigno, 2003; Woodford, 2003, Chap. 3; Carvalho, 2006), but these models still do not explain why differences in the speed of price adjustments might emerge as part of the optimizing behavior of firms. Nevertheless, in these New Keynesian models, one industry characteristic – the degree of competition – affects directly the degree of strategic complementarity (or “real rigidity” as in Ball and Romer, 1990) in price setting, and therefore the trajectory of price adjustment.

Our main findings can be summarized as follows:

First, most of the fluctuations in sectoral inflation rates are due to sector-specific factors. On average, only about 15% of inflation fluctuations are due to macroeconomic factors (17% for personal consumption expenditure prices and 13% for producer prices). Thus, the relative flexibility of sectoral prices found by Bils and Klenow (2004) is to a large extent due to sector-specific disturbances. Consistent with the evidence on disaggregated price series, we also find considerable disparities in the magnitude of price changes and in the persistence of inflation across price categories, both for consumer and producer prices. These disparities are due to a large extent to differences in the volatility of sector-specific components, and only little to different responses to macroeconomic factors.

Second, sectoral inflation fluctuations are persistent, but this persistence is driven primarily by common macroeconomic components and not by sector-specific disturbances. While sector-specific shocks may cause large fluctuations in the individual inflation rates, these fluctuations are shortlived on average. In contrast, aggregate macroeconomic shocks tend to have more persistent effects on a wide range of sectoral inflation rates.

Third, prices and quantities respond differently to macroeconomic shocks and to sector-specific shocks. While sector-specific shocks induce an immediate and permanent change in sectoral prices and quantities, macroeconomic shocks have a small impact on prices and quantities but they generate larger effects thereafter.

Fourth, most prices respond with a significant delay to identified monetary policy shocks, and show little evidence of a “price puzzle,” contrary to existing studies based on monetary policy shocks identified from small-scale VARs. This suggests that exploiting a large information set in the estimation provides more accurate estimates of the effects of monetary policy.

The picture that emerges then, is one in which many prices fluctuate considerably in response to sector-specific shocks, but they respond only sluggishly to aggregate macroeconomic shocks such as monetary policy shocks. This difference in responses to various shocks can explain why, at the disaggregated level, individual prices are found to be adjusted relatively frequently, while estimates of the degree of price rigidity are much higher when based on aggregate data. This explains why models that assume considerable price stickiness have often been successful at replicating the effects of monetary policy shocks.

Looking across price categories, we find that the observed dispersion in the reaction of producer prices is relatively well explained by the degree of market power, that prices in sectors with volatile idiosyncratic shocks react rapidly to aggregate monetary policy shocks, and that PCE categories in which prices fall the most following a monetary policy shock tend to be those in which quantities consumed fall the least. Finally, we find that the idiosyncratic components of prices and quantities move mostly in opposite directions suggesting that idiosyncratic shocks are supply-type shocks.

Our results are robust to changes in the sample. In particular we show that our main results are qualitatively similar for the period after 1984.

The rest of the paper is organized as follows. Section 2 reviews the econometric framework, by discussing the formulation and estimation of the FAVAR. In Section 3, we discuss various data sets used in our estimation. Section 4 presents empirical results about the sources of fluctuations in disaggregated prices. It includes a description of the price responses to sector-specific shocks and to macroeconomic fluctuations. Section 5 investigates the effects of monetary policy shocks and relates the responses of producer prices in various sectors to industry characteristics. Section 6 reports some robustness results for the post-1984 sample. Section 7 concludes.

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7 Conclusion

In this paper, we disentangle the fluctuations in disaggregated U.S. consumer and producer prices which are due to aggregate macroeconomic shocks from those due to shocks to individual price series. We do so by estimating a factor-augmented VAR that relates a large panel of economic indicators and of individual price series to a relatively small number of estimated common factors. After identifying monetary policy shocks using all of the information available, we estimate consistently the effects of U.S. monetary policy on disaggregated prices. This is important not only to get a better understanding of the nature of the fluctuations in disaggregated prices, and of how prices react to macroeconomic shocks, but also to assess the impact of monetary policy on prices in various sectors.

We obtain several empirical results that can be summarized as follows:

1. At the level of disaggregation considered, most of the sectoral prices fluctuations appear to be due to sector-specific factors, and only about 15% of individual sectoral price fluctuations, on average, are due to aggregate macroeconomic factors.

2. Sectoral inflation fluctuations are relatively persistent, but this persistence is essentially due to the very high degree of persistence in the components driven by common or macroeconomic shocks, and not to sector-specific disturbances.

3. Sectoral prices respond very differently to sector-specific shocks and to macroeconomic shocks: while sector-specific shocks may cause large fluctuations in sectoral inflation, these fluctuations are typically short lived so that prices tend to move immediately to their new permanent level; aggregate macroeconomic shocks instead tend to have more persistent and sluggish effects on a wide range of sectoral inflation rates.

4. Most disaggregated prices respond with a significant delay to identified monetary policy shocks, and show little evidence of a “price puzzle,” contrary to existing studies based on traditional VARs. The absence of a strong price puzzle suggests that by exploiting a large information set in the estimation of a FAVAR, we may obtain more accurate estimates of the effects of monetary policy.

5. PCE categories in which prices fall the most following a monetary policy shock tend to be those in which quantities consumed fall the least.

6. The observed dispersion in the reaction of producer prices to monetary policy shocks is relatively well explained by the degree of market power.

7. Prices react more rapidly to monetary policy shocks in sectors with volatile idiosyncratic and persistent idiosyncratic shocks.

8. The correlations between the idiosyncratic components of prices and quantities tend to be negative, suggesting that sector-specific shocks are mostly supply-type shocks.

This collection of stylized facts on the response of disaggregated U.S. prices to various shocks presents challenges to current models of price determination. An evaluation of various models on the basis of these stylized facts is beyond the scope of this paper. Nevertheless, it is worth pointing out that our finding number 3 – namely that sectoral prices respond differently to macroeconomic and sector-specific shocks – may explain why sticky-price models such as the Calvo model have been so popular in characterizing the effects of monetary policy actions on aggregate variables, while they have been sharply criticized at the same time by authors focused on disaggregate price series.

Clearly, is would be desirable to have models that can fully account for the responses of aggregate and disaggregated prices to both macroeconomic and sector-specific disturbances. Some recent papers are very promising in this respect. Carvalho (2006) generalizes the Calvo model to allow for heterogeneity in price stickiness across sectors. He finds that in the presence of strategic complementarities, firms which adjust prices infrequently have a disproportionately large effect on the decisions of other firms, and thus on the aggregate price level. Even if most sectors have relatively flexible prices, and thus respond quickly to sector-specific disturbances, they may respond sluggishly to nominal shocks. Gertler and Leahy (2006) propose a state-dependent pricing model that involves volatile prices due to idiosyncratic shocks, but that predicts sluggish price responses to a monetary shock, as reported here, due to real rigidities.[16] The model also predicts that a high volatility of idiosyncratic shocks should be associated with more volatile prices and a more volatile response to monetary shocks, as we find in the data. In yet another direction, recent models on rational inattention such as those proposed by Reis (2006) and Ma´ckowiak and Wiederholt (2006) are also able to generate different responses of sectoral prices to sector-specific shocks and aggregate shocks. The model of Reis (2006), for instance predicts that (i) stickiness is higher in industries with low price elasticity of demand; (ii) costs of processing information are positively related with inattentiveness; (iii) volatility of shocks requires more frequent updating.

Assessing the empirical success of each of these theories along the many dimensions documented in this paper is not a trivial task. Even though a strict and literal interpretation of any of these models may always be rejected on some dimension, a fair assessment requires moving beyond the strict interpretation and determining whether some enriched version of existing theories can be successful. This is in our view an important avenue for future research.
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1 See for instance Carlton (1986), Cecchetti (1986), Kashyap (1995), Levy, Bergen, Dutta and Venable (1997), MacDonald and Aaronson (2001), and Kackmeister (2001).

2 The median duration remains below 5 months when they account for temporary sales. More recently, however, Nakamura and Steinsson (2006), analyzing CPI microdata, argue that the median duration is between 8 and 11 months when they exclude sales and price changes due to product substitutions. The upper bound is similar to the median duration found in Euro area data (see, e.g., Dhyne et al., 2005, and several other studies which are part of the Eurosystem Inflation Persistence Network).

3 It ranges from less than a month (for gasoline prices) to more than 80 months (coin-operated apparel laundry and dry-cleaning).

4 See, e.g., Fuhrer and Moore (1995), Galí and Gertler (1999), Cogley and Sargent (2001, 2005), Sims (2001), Stock (2001), Pivetta and Reis (2003), Levin and Piger (2003), Clark (2003).

5 Pesaran and Smith (1995) and Imbs, Mumtaz, Ravn and Rey (2005) argue that heterogeneity – across categories – in the persistence of individual series may result in a large estimated persistence of the aggregate even if individual series display on average little persistence. Cogley and Sargent (2001, 2005), Levin and Piger (2003) and Clark (2003) find that inflation persistence drops when they allow for changes in mean inflation over time.

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16 In contrast, the state-dependent model of Golosov and Lucas (2003) which has idiosyncratic productivity shocks but which abstracts from strategic complementarities generates rapid and strong price responses following a monetary policy shock. Midrigan (2006), however, extends the model of Golosov and Lucas (2003) to a multi-product setting and calibrates the distribution of idiosyncratic shocks in a way that mitigates the price responses to monetary shocks.