lecture 3 : current applications€¦ · d'acunto, ospina, and weber (2015) i micro data underlying...

Lecture 3 :Current Applications

Prof. Michael WeberUniversity of Chicago Booth School of Business

September 17, 2015

1 )

Lecture Outline

I Overview of data sources

I Simple multi-sector New Keynsian Model

I Applications

2 )

Overview of Data Sources

3 )

Micro Pricing Data

I Micro data underlying US CPI and PPI

I Bureau of Labor StatisticsI Accessible in-house for researchersI Details under: On-site Visiting Researcher ProgramI Recent papers using data: Bils and Klenow (2004), Nakamura

and Steinsson (2008), Goldberg and Hellerstein (2011), Bergerand Vavra (2013), Kehoe and Midrigan (2014), Vavra (2014),Bhattarai and Schoenle (2014) Gorodnichenko and Weber(2015), Weber (2015), D'Acunto, Liu, P�ueger, and Weber(2015)

I Scanner price and quantity data from Symphony IRI

I Accessible for researchersI Details under: IRI Academic data setI Recent papers using data: Stroebel and Vavra (2015), Coibion

et al. (2015)

4 )

http://www.bls.gov/bls/blsresda.htmhttp://www.iriworldwide.com/en-US/solutions/Academic-Data-Set

Micro Pricing Data

I Consumer Panel and Retail Scanner Data from AC Nielsen

I Panel of 40,000 - 60,000 US households and purchases offast-moving consumer goods

I Weekly pricing and volume from more than 90 participatingretail chains

I Accessible to researcherI Details under: Kilts Center for Marketing Booth School of

BusinessI Recent paper using data: Beraja, Hurst, and Ospina (2015),

D'Acunto, Ospina, and Weber (2015)

I Micro data underlying UK CPI

I Accessible onlineI Details under: Tables with micro data; no establishment namesI Recent paper using data: Kryvtsov and Vincent (2014)

5 )

http://research.chicagobooth.edu/nielsen/http://research.chicagobooth.edu/nielsen/http://www.ons.gov.uk/ons/datasets-and-tables/index.html

Micro Pricing Data

I Online shopping platforms

I See for an overview: MIT Billion Price ProjectI See also Gorodnichenko and Talavera (2015)

I Other data sources include: shopping catalogues, nationalprice statistics, prices for speci�c goods, Dominick'ssupermarket price data, etc.

I See also Klenow and Malin (2010) and Nakamura andSteinsson (2013) for recent reviews

6 )

http://bpp.mit.edu/

Simple Multi-Sector New Keynesian Model

7 )

Simple Multi-Sector New Keynesian Model

I Carvalho (2006) multi-sector model

I Here: brief discussion of model

I See paper for more details

8 )

Households

I Representative household lives forever

I The instantaneous utility: positive in consumption; negative inlabor supply

I IES: σ

I Labor supply is �rm-speci�c

I For each �rm, the elasticity of labor supply is η

I Household's discount factor: β

I Households have a love for variety: CES Dixit-Stiglitzaggregator with the elasticity of substitution θ

9 )

Firms

I Firms set prices as in Calvo (1983)

I k sectors populated by a continuum of �rms

I Each sector is characterized by a �xed λk : probability toadjust price

I Share of �rms in industry k in the total number of �rms:density function f (k)

I Firms are monopolistic competitors

I Elasticity of substitution: θ; same within and across industries(relaxed later)

I Production function for output Y is linear in labor N

10 )

Firms' Problem

Problem of �rm j in industry k is to pick a reset price Xjkt :

maxEt∞∑s=0

Qt,t+s(1− λk)s [XjktYjkt+s −Wjkt+sNjkt+s ]

s.t. Yjkt+s = Njkt+s

Yjkt+s = Yt+s

(XjktPt+s

)−θQt,t+s = β

s

(Yt+s

Yt

)−σVariables without subscripts k and j : aggregate variablesW : wages (taken as given by �rms)Q: stochastic discount factor

11 )

Wage Rate

Household's optimization problem determines wages paid by �rms:

WjktPt

=N1/ηjkt

C−σt

12 )

Aggregate Prices and Output

The aggregate price level and output are given by:

Pt =

(∫ 10

f (k)P(1−θ)kt dk

)1/(1−θ)Pkt =

(∫ 10

P(1−θ)jkt dj

)1/(1−θ)Yt =

(∫ 10

f (k)1/θY(θ−1)/θkt dk

)θ/(θ−1)Ykt = f (k)

(∫ 10

Y(θ−1)/θjkt dj

)θ/(θ−1)

13 )

Taylor Rule

Central bank follows an interest rate rule:

exp(it) =

(Pt

Pt−1

)φπ( YtYt−1

)φyβ−1 exp(mpt)

mpt = ρmpmpt−1 + vt

exp(it): nominal interest rateφπ: response to in�ationφy : response to output growthvt : i.i.d. zero-mean policy innovation

14 )

Firm Value

Substitute in optimal reset prices, �rm-speci�c demand and wagesValue of the �rm V with price Pjkt is given by:

V (Pjkt) = Et{Y σt Pt

[∆

(1)kt

(PjktPt

)1−θ− ∆(2)kt

(PjktPt

)−θ(1+1/η)+ Υ

(1)kt − Υ

(2)kt

]}∆

(1)kt = Y

1−σt + β(1− λk)

(Pt+1Pt

)θ−1∆

(1)kt+1

∆(2)kt = Y

1+1/ηt + β(1− λk)

(Pt+1Pt

)θ(1+1/η)∆

(2)kt+1

Υ(1)kt = λkβ

(Xk,t+1Pt+1

)1−θ∆

(1)kt+1 + βΥ

(1)kt+1

Υ(2)kt = λkβ

(Xk,t+1Pt+1

)−θ(1+1/η)∆

(2)kt+1 + βΥ

(2)kt+1

15 )

Applications

16 )

Gorodnichenko and Weber (2015)

Research Question:

Does observed price stickiness at micro level imply costly stickyprices?

I New Keynesian model imply costly sticky prices

I New Monetary search model: stickiness equilibrium outcomeand costless for �rms

17 )

Framework

I Micro price data from the Bureau of Labor Statistics

I Stock returns for constituents of S&P500

I Monetary policy shock using federal funds futures

I Narrow event windows to rule out alternative explanations

18 )

Model Intuition: Pro�t as Function of Price (static model)

Price P

Profit π

P?

π∗

Old Profit Function

Student Version of MATLAB

19 )


Price P

Profit π

P?

π∗

Band of InactionL

PL PL

φL

Old Profit Function


20 )


Price P

Profit π

P?

π∗

PL PL

φL

P?new

Band of InactionL

Old Profit FunctionNew Profit Function


21 )


Price P

Profit π

P?

π∗

PL PL

φL

P?new

Band of InactionL



22 )


Price P

Profit π

P?

π∗

PL PL

φL

P?new

Band of InactionL

PH PH

φH



23 )

Theoretical Framework: Recap

I Returns can increase or decrease following shift in pro�tfunction

I After shocks, larger increase in volatility for high-menu cost�rms

I Empirical Speci�cation:

R2it = b0 + b1 × v2t + b2 × λi × v2t + b3 × λi + Controls + εit ,

where Rit is return of �rm i at time t, vt is a monetary policy surpriseand λi is frequency of price adjustment at �rm level

Prediction: Monetary policy shocks increase return variability

(b1 > 0).

This e�ect decreases in price �exibility (b2 < 0).

24 )

Data and Sample Period

I 137 event dates between February 1994 and December 2009

I 30 & 60 min event windows around the press releases of theFOMC

I Time stamps of press releases from FOMC

I Stock returns for constituents of S&P500 from NYSE taq

I Firm level controls such as size, book-to-market, price-costmargin, sales volatilities, sta� expenditures from CRSP andCompustat

I Micro data underlying PPI to measure price stickiness

25 )

Measuring Price Stickiness

I Frequency of price adjustment: mean fraction of months withprice change at good level

I Example:

I Observed price path of $4 for two months, then $5 for threemonths

I One price change during �ve months: frequency of 1/5

I Aggregation at establishment level: BLS identi�er

I Aggregation at �rm level: manual matching

I Equally and value of shipment-weighted frequencies

26 )

Monetary Policy Shocks

I High-frequency identi�cation of monetary policy shocks

I Tick-by-tick federal funds futures (FFF) data from CME

I FFF settles on average e�ective fed funds rate: use scaledchange

vt =D

D − t(ff 0t+∆t+ − ff

0t−∆t−) where D is # of days in month

03:00 09:00 15:00

5.25

5.27

5.29 Press release

August 8, 2006

03:00 09:00 15:00

4.85

4.95

5.05 Press release

September 18, 2007

03:00 09:00 15:00

2.55

2.60

2.65Press release

March 18, 2008


I High trading activity with immediate market reaction

27 )

Event Returns

2.15pm

FOMC press release

2.05pm 2.25pm

Tight Event Window: -10 min -- +20 min

Pit-1 Pit+1

I Use tick-by-tick data from NYSE taq

I Last trade before (Pit−1) and �rst trade after (Pit+1) eventwindow

I Volume weighted returns as robustness

I Check all returns larger than 5% in absolute value

28 )

Baseline Analysis

I Study heterogeneity in reaction of volatility to monetary policyshock

I Empirical Speci�cation:

R2it = b0 + b1 × v2t + b2 × λi × v2t + b3 × λi + Controls + εit ,

where Rit is return of �rm i at time t, vt is a monetary policy surpriseand λi is frequency of price adjustment at �rm level

Prediction: Monetary policy shocks increase return variability

(b1 > 0).

This e�ect decreases in price �exibility (b2 < 0).

I Cluster standard errors at event level (similar if useDriscoll-Kraay)

29 )

Baseline Results

R2it = b0 + b1 × v2t + b2 × λi × v2t + b3 × λi + εit

Baseline Baseline

(Tight Window) (Wide Window)

All obs. No outliers All obs. No outliers

(1) (2) (3) (4)

v2t 128.50∗∗∗ 76.95∗∗∗ 119.60∗∗∗ 95.38∗∗∗(29.50) (15.95) (38.89) (24.82)

λi × v2t −169.80 ∗ ∗ −67.26∗∗∗ −130.40∗ −78.07∗∗∗(82.32) (5.02) (77.88) (27.10)

λi 0.41 0.09 0.55 0.08

(0.33) (0.16) (0.59) (0.21)

# Obs 57,541 57,441 57,541 55,002

R2 0.12 0.12 0.03 0.01

I 25 bps monetary policy surprise leads to increase in squared returns of8%2 for the most sticky �rms (0.252 × 128.50 = 8.03)

I Reduced by factor of 3 for �exible price �rms: (b1 − 0.5× b2)/b1 ≈ 1/330 )

Dynamic General Equilibrium Model

I Carvalho (BE Macro Advances, 2006) model:

I Mutliple sectors (5 in calibration)

I Continuum of �rms in each sector (100 in calibration)

I Sectors k di�er in Calvo rate of price adjustment λk

I Monopolistic Competions (CES aggregator)

I Linear production function in labor

I Taylor rule

I Monetary shocks are only source of variation

I Derive �rm valuations as function of �rm's price

I Calibrate and simulate model at quarterly frequency

31 )

Calibration

Parameter Value Source

Frisch η 2 Ashenfelter et al. (2010)

IES σ 0.5 standard

Demand Elasticity θ 7 standard

Discount Factor β 0.99 standard

In�ation Response φpi 1.5 Taylor (1993)

Output Gap Response φy 0.5/4 Taylor (1993)

Shock Persistence ρmp 0.9 Coibon and Gorodnichenko (2012)

Volatility of Shocks stdvt 0.0043 Coibon et al. (2012)

Sector k Share Frequency of Price Adjustment

1 0.2 0.094

2 0.2 0.164

3 0.2 0.277

4 0.2 0.638

5 0.2 0.985

32 )

Regressions on Simulated Data

R2it = b0 + b1 × v2t + b2 × λi × v2t + b3 × λi + εit

Baseline Alternative b1 b2 b3

Baseline 221.5 -256.0 -0.008

IES σ 1/2 1/3 161.2 -177.5 -0.006

Frisch η 2 1 433.5 -513.8 -0.014

Demand Elasticity θ 7 6 114.0 -120.7 -0.004

Taylor Rule Parameters

φπ 1.5 2 108.0 -127.5 -0.004

φy 0.5/4 0.75/4 245.7 -287.9 -0.009

ρmp 0.9 0.91 410.5 -494.7 -0.015

std(vt) 0.0043 0.004 197.7 -226.2 -0.006

I Simulate 100 �rms per sector for 2,000 periods

I Discard �rst 1,850 periods as burn-in (similar to empirical sample size)

I Report average values of regression coe�cients

33 )

Conclusion

I Fundamental questions:

I Does money matter?

I Are sticky prices costly?

I Alt. models w/ di�erent normative & positive implications

I NK Macroeconomics: �menu� cost of price adjustment

I Challenge: �menu� costs have di�erent varieties and shapes

I We present robust and model-free evidence that conditionalvolatility of stock returns is larger for sticky price �rmscompared to �rms with �exible prices � qualitatively andquantitatively consistent with New Keynesian interpretation atthe �rm level

34 )

Weber (2015)

I Nominal rigidities central in macro, unexplored in �nance

I Substantial variation in frequency across industries and �rms

Research Question:

I Same rigidities central for macro and asset pricing?

I Price stickiness determinant for cross section of stock returns?

35 )

Framework

I Micro-level price data from the Bureau of Labor Statistics

I Calculate frequency of product price adjustment at �rm level

I Merge with stock returns for constituents of S&P500

36 )

Portfolio Level

Average annual portfolio raw returns

Sticky S2 S3 S4 Flexible S1-S5

07/1963 - 06/2011 18.84 18.42 18.26 16.97 16.10 2.74∗(2.85) (2.02) (2.03) (2.19) (1.97) (1.46)

07/1982 - 06/2007 24.22 21.98 22.03 21.00 19.84 4.38 ∗ ∗(3.08) (2.66) (2.35) (2.46) (2.47) (1.91)

07/1982 - 06/1998 28.77 25.59 25.20 24.39 22.05 6.72∗∗∗(3.53) (2.93) (3.23) (2.64) (2.89) (1.61)

Newey-West standard errors in parentheses

∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

I Sort all stocks into 5 portfolios

I Portfolio 1: stocks with low frequencies (sticky price �rms)

I Portfolio 5: stocks with high frequencies (�exible price �rms)

37 )

Panel Regressions

I Return di�erences potentially due to reasons ⊥ to frequency

I Di�erent cyclical properties of demand

I Di�erences in market power

I Di�erent characteristics

I Exploit XS variation in stock level panel regressions

I Add controls individually and jointly

I Cluster standard errors at �rm (and year) level

38 )

Panel Regressions

Ri,t = α+ βFreqi × Freqi +∑n

βn × Controlsin,t + YearFE + �i,t

Controls βFreqi βn Year FE # Obs.

(1) Baseline (no controls) −10.04∗∗∗ No 13,810(2.16)

(2) Baseline (no controls) −10.97∗∗∗ Yes 13,810(2.28)

(3) Size −8.04∗∗∗ −4.38∗∗∗ Yes 13,810(2.44) (0.29)

(4) Book to Market −12.94∗∗∗ 3.22∗∗∗ Yes 13,582(2.50) (0.84)

(5) CAPM Beta −8.54∗∗∗ 4.12∗∗∗ Yes 13,319(2.29) (0.75)

Baseline implies premium for sticky price �rms of 6.02% per year (10.04 × 0.6)

39 )

Panel Regressions cont.



Controls βFreqi βn Year FE # Obs.

(6) Leverage −10.99∗∗∗ 1.06 Yes 13,735(2.32) (1.38)

(7) Cash Flow −10.98∗∗∗ −10.97 ∗ ∗ Yes 13,746(2.31) (5.55)

(8) Share Turnover −9.83∗∗∗ 52.39∗∗∗ Yes 13,810(2.27) (3.87)

(9) Bid-Ask Spread −10.16∗∗∗ −5.45∗∗∗ Yes 13,810(2.34) (0.54)

(10) Price to Cost Margin −9.52∗∗∗ 5.61∗∗∗ Yes 13,744(2.25) (1.66)

(11) Her�ndahl Index Sales −10.79∗∗∗ 0.23 Yes 13,210(2.36) (0.70)

40 )

Panel Regressions: All Controls



Controls β Year FE # Obs.

Freq −7.07∗∗∗ Yes 13,029(2.73)

Size −4.97∗∗∗Book to Market 3.21∗∗∗CAPM Beta 0.10

Leverage 4.35 ∗ ∗Cash Flow 3.76

Share Turnover 37.00∗∗∗Bid-Ask Spread −7.37∗∗∗Price to Cost Margin 7.84∗∗∗Her�ndahl Index Sales 1.79∗

41 )

Conditional CAPM

Rolling time series regressions of portfolio excess return Rp on market excess return Rm

Rp,t = αp + βp × Rm,t + �i,t

Sticky S2 S3 S4 Flexible S1-S5

(1) (2) (3) (4) (5) (6)

mean αp 0.41∗∗∗ 0.35∗∗∗ 0.38∗∗∗ 0.37∗∗∗ 0.40∗∗∗ 0.00(0.19) (0.12) (0.14) (0.13) (0.14) (0.14)

mean βp 1.29∗∗∗ 1.21∗∗∗ 1.15∗∗∗ 1.08∗∗∗ 0.91∗∗∗ 0.37∗∗∗(0.05) (0.04) (0.05) (0.04) (0.06) (0.05)


∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

I CAPM cannot explain level of excess returns

I CAPM can explain spread in returns

I Spread in returns fully explained by di�erences in systematic risk

42 )

Monetary Policy Surprises

I Monetary policy important determinant of risk premia

I Empirically: Bernanke and Kuttner (2005), Savor and Wilson(2013a, 2013b), Lucca and Moench (2013), Campbell,P�ueger and Viceira (2013)

I In model: see later

I Heterogeneous response to monetary policy shocks?

I Regress monthly excess returns, Rp,t , on FFR surprises

43 )

Monetary Policy Surprises cont.

Excess returns, Rp , on negative of surprise component of change in FFR, −∆iutRp,t = αp + βp,FFR ×−∆iut + �p,t

Market Sticky S2 S3 S4 Flexible

(0) (1) (2) (3) (4) (5)

βactualp,FFR 9.35% 11.42% 10.19% 9.35% 8.85% 5.01%

[2.66]∗∗∗ [4.12]∗∗∗ [3.46]∗∗∗ [3.36]∗∗∗ [3.37]∗∗∗ [2.98]∗

βpredp,FFR

10.88% 10.65% 10.02% 9.38% 7.45%


∗p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01

I Strong positive reaction to expansionary monetary policy shocks

I Stronger responsiveness of �rms with higher nominal rigidity

I Predicted sensitivity, βpredp,FFR , equals CAPM β times market sensitivity,

βactualmarket,FFR

44 )

Mutli-sector Calvo Model

I Households

I Utility from composite consumption and leisure

I External habit formation

I Provide di�erent labor types

I Have market power in setting wages

I Reset wage with certain probability each period: Calvo friction

I Production

I Production organized in k sectors

I Reset price with certain probability each period: Calvo friction

I Di�erent Calvo rates across sectors 1− θkI Monopolistic competition (CES aggregator)

I Taylor rule

I Monetary and technology shocks only source of variation

45 )

Calibration

I Solve 5 sector version of model to second order using dynare

I Calibrate model at quarterly frequency

I Simulate for 500 quarters and 400 �rms / sector

I Discard �rst 250 periods as burn-in

Sectoral Distribution of Frequency of Price Adjustment

Sector k Share Frequency of Price Adjustment

1 0.2 0.105

2 0.2 0.164

3 0.2 0.277

4 0.2 0.638

5 0.2 0.985

46 )

Calibration Parameters

Parameter Value Source

Discount Factor β 0.99 standard

Habit Persistence b 0.76 Altig et al 2011

Risk Aversion γ 5 Jermann 1998

Frisch σ 2.5 Carvalho 2006

Consumption Elasticity across �c 8 Carvalho 2006

Consumption Elasticity within �ck 12 Carvalho 2006

Wage Calvo Rate θw 0.825 Heer et al 2012

Elasticity labor types �w 8 Altig et al 2011

In�ation Response φπ 1.24 Rudebusch 2002

Output Gap Response φy 0.33/4 Rudebusch 2002

Tech Shock Persistence ρa 0.95 Smets and Wouters 2007

MP Shock Persistence ρm 0.90 Coibion and Gorodnichenko 2012

47 )

Model Implied Annual Stock Returns

Sticky S2 S3 S4 Flexible S1-S5 ERP SR

(1) Baseline 7.91 6.84 6.56 5.96 5.51 2.39 6.56 0.39

(2) �ck = 13 8.70 6.81 6.41 5.68 5.15 3.55 6.55 0.36

(3) �ck = 11 7.40 6.89 6.70 6.21 5.85 1.55 6.61 0.41

(4) �c = 10 8.15 6.86 6.55 5.94 5.50 2.66 6.60 0.39

(5) �c = 6 7.71 6.82 6.57 5.98 5.53 2.18 6.52 0.39

(6) σε= 0.009 10.21 7.66 7.19 6.40 5.82 4.39 7.46 0.38

(7) φπ = 1.3 5.98 5.88 5.76 5.41 5.16 0.82 5.64 0.38

(8) φx = 0.5/4 7.90 6.84 6.56 5.96 5.51 2.39 6.55 0.39

(9) MP only 6.81 5.87 5.64 5.03 4.59 2.23 5.59 0.34

(10) Technol only 1.08 0.97 0.89 0.83 0.81 0.27 0.92 0.47

Larger Taylor Rule coe�cients on in�ation (φπ) and output growth (φx )

I Equity risk premium: ERPI Sharpe ratio: SRI Coe�cient of regressing annual returns on monthly frequency of price adjustment: βFreq

48 )

Conclusions

Does heterogeneity in price rigidity matter for XS of returns?

I Frequency of price adjustment associated w/ premium of 4%

I Premium strongly predictable in times series

I In XS, di�erences in systematic risk capture return premium

I Su�ciently high elasticity of substitution of within industryconsumption varieties crucial to obtain cross-sectional returnpremium

Price rigidities central in Macro and in Asset Pricing

49 )

Bibliography I

Beraja, M., E. Hurst, and J. Ospina (2015). The regional evolutionof prices and wages during the great recession. Unpublishedmanuscript, University of Chicago.

Berger, D. and J. Vavra (2013). Dynamics of the US pricedistribution. Unpublished manuscript, University of Chicago.

Bhattarai, S. and R. Schoenle (2014). Multiproduct �rms andprice-setting: Theory and evidence from U.S. producer prices.Journal of Monetary Economics 66, 178�192.

Bils, M. and P. J. Klenow (2004). Some Evidence on theimportance of sticky prices. The Journal of PoliticalEconomy 112, 947�985.

Calvo, G. A. (1983). Staggered prices in a utility-maximizingframework. Journal of Monetary Economics 12(3), 383�398.

50 )

Bibliography II

Carvalho, C. (2006). Heterogeneity in price stickiness and the reale�ects of monetary shocks. The B.E. Journal ofMacroeconomics 2(1), 1�56.

Coibion, O., Y. Gorodnichenko, and G. H. Hong (2015). Thecyclicality of sales, regular and e�ective prices: Business cycleand policy implications. American Economic Review 105(3),993�1029.

D'Acunto, F., R. Liu, C. P�ueger, and M. Weber (2015). Flexibleprices and leverage. Unpublished manuscript, University ofChicago.

D'Acunto, F., J. Ospina, and M. Weber (2015). Experienced pricechanges, salience, and in�ation expectations. Unpublishedmanuscript, University of Chicago.

Goldberg, P. P. and R. Hellerstein (2011). How rigid are producerprices? Unpublished manuscript, Yale University .

51 )

Bibliography III

Gorodnichenko, Y. and O. Talavera (2015). Price setting in onlinemarkets: Basic facts, international comparisons, and cross-borderintegration. Unpublished Manuscript, University of California.

Gorodnichenko, Y. and M. Weber (2015). Are sticky prices costly?Evidence from the stock market. AAmerican Economic Review(forthcoming).

Kehoe, P. and V. Midrigan (2014). Prices are sticky after all.Journal of Monetary Economics.

Klenow, P. J. and B. A. Malin (2010). Microeconomic evidence onprice-setting. Handbook of Monetary Economics 3, 231�284.

Kryvtsov, O. and N. Vincent (2014). On the importance of salesfor aggregate price �exibility. Technical report, UnpublishedManuscript, Bank of Canada.

52 )

Bibliography IV

Nakamura, E. and J. Steinsson (2008). Five facts about prices: Areevaluation of menu cost models. The Quarterly Journal ofEconomics 123(4), 1415�1464.

Nakamura, E. and J. Steinsson (2013). Price rigidity:Microeconomic evidence and macroeconomic implications.Annual Review of Economics 5, 133�163.

Stroebel, J. and J. Vavra (2015). House prices, local demand, andretail prices. Unpublished manuscript, University of Chicago.

Vavra, J. (2014). In�ation dynamics and time-varying volatility:New evidence and an ss interpretation. The Quarterly Journal ofEconomics 129(1), 215�258.

Weber, M. (2015). Nominal rigidities and asset pricing.Unpublished manuscript, University of Chicago.

53 )

lecture 3 : current applications€¦ · d'acunto, ospina, and weber (2015) i micro data underlying...

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