Sticky Information Phillips Curves: European Evidence

Article excerpt

FORMATION OF EXPECTATIONS, information transmission, and learning have recently again attracted much interest. (1) Several new papers, including Mankiw and Reis (2002, 2003, 2006), argue that models in which agents update their information occasionally rather than instantaneously resolve some stylized business cycle puzzles. (2) These puzzles include the facts that, in the data, inflation is considerably persistent and disinflations are found to be costly. (3) Carroll's (2003) work on "epidemiological expectations" elaborates the theoretical microfoundations for the new sticky information paradigm. Reis (2006) and Mankiw and Reis (2006) also discuss the microfoundations of the sticky information approach and argue that the Sticky Information Phillips Curve (SIPC) combines sound theory (missing in the backward-looking Phillips curves) and good empirical performance (for the lack of which the standard New Keynesian Phillips curves are often criticized, e.g., by Rudd and Whelan 2006).

Interestingly, there has been little research on estimation of the key parameters of the SIPC. Carroll (2003) and Dopke et al. (2008) estimate the epidemiological model of transmission of information between households and forecasters using the U.S. and European survey data, respectively. Among the few papers we are aware of that estimate the SIPC directly are Khan and Zhu (2002, 2006). However, due to data limitations, Khan and Zhu have to use inflation and output forecasts obtained from a VAR model with actual inflation, output gap, and the world output gap as a proxy for the actual forecasts. Similarly, Kiley (2005), Korenok (2005), and Laforte (2005) also proxy for inflation expectations. In contrast to these papers, we use survey-based inflation expectations directly.

Using recent data from four major European economies, we estimate the parameter ([lambda]) that governs the amount of information stickiness. We find that producers in France, Germany, and the United Kingdom update their information sets about once a year, while those in Italy about once each 6 months. These results are quite robust across the two estimation methods that we use (equation-by-equation estimation and seemingly unrelated regressions [SUR]) and the number of lags of right-hand side variables included. The estimates of [lambda] close to 0.3 are consistent with those of Dopke et al. (2008), except for Italy, whose [lambda] they pin down to be comparable to the other countries. Khan and Zhu find similar results for Canada, the United Kingdom, and the United States, and Korenok (2005) for the United States. Kiley (2005) reports that [lambda] in his models ranges between 0.44 and 0.71 (in the U.S. data).

1. SIPC

1.1 The Model

Mankiw and Reis (2002) assume that for each period, only a fraction [lambda] of firms gather the up-to-date information about the current state of the economy and recomputes and adjusts the optimal path of future prices. Remaining (1 - [lambda]) firms continue using their previous plans and set prices based on outdated information. The firm's probability of information updating is exogenously determined and independent of price adjustment history. Under this assumption, Mankiw and Reis derive the following closed economy version of the SIPC:

[[pi].sub.t] = [alpha][lambda]/1 - [lambda] [[??].sub.t] + [lambda] [[infinity].summation over (j=0)] [(1 - [lambda]).sup.j] [E.sub.t-1-j] ([pi].sub.t] + [alpha] [DELTA] [[??].sub.t]) + [[epsilon].sub.t], (1)

where [[pi].sub.t] is the inflation rate and [[??].sub.t] the output gap. [E.sub.t](*) denotes the rational (mathematical) expectation as of time t. The parameter a measures the sensitivity of the optimal relative price to the current output gap and depends on the structure of the economy (e.g., the preferences, technology, and the market structure parameters). (4)

Note that in contrast to the standard (forward-looking) sticky price model, in which current expectations of the future state of the economy play an important role, what matters in the sticky information model (see equation (1)) are the past expectations of the present events. …