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Discussion of “Intertemporal Disturbances” By G. Primiceri, E. Schaumburg, and A. Tambalotti Marc Giannoni Columbia University NY Area Workshop on Monetary Policy FRB New York, November 2005 Paper’s Main Question | What drives business cycle fluctuations? z Intratemporal disturbances • shocks to FOC that relate MRS to MRT within a period z Intertemporal disturbances • shocks to FOCs that relate MRS to MRT across periods | Short answers: z z Hall (1997), CKM (2005): Intratemporal This paper: Intertemporal 1 What are these shocks? | PST offer 3 interpretations: z z z | Genuine shifts in technology, preferences, … Reduced-form representation of frictions (wedges, CKM) “convenient” representation of model misspecification (shocks = model failure) But adopt the last one: z “intertemporal disturbances, i.e., the empirical failures of the intertemporal optimization conditions” Why is it important to know which shocks matter? | | | Important to understand business cycles Policy response may vary depending on shocks If shocks are interpreted as indicating model failure: provide directions for future economic research z Hall (1997): “The finding that the [intratemporal] preference shift bears almost all of the burden of explaining recessions should be the starting point for research… more in areas of atemporal analysis than in intertemporal analysis” z PST: “The finding that intertemporal disturbances are paramount leads us to conclude that more effort should be directed towards understanding agents’ intertemporal choices” 2 Using shocks as diagnostic for where to conduct research? What is dangerous with this logic? z Shocks may provide insights, but they remain conditional on model adopted! z Changing one mechanism in model may change all shocks! Importance of intertemporal disturbances (b): Basic intuition Euler Equation: 1= E{[β(Ct+1/Ct)-1 (bt+1/bt)] (1+rt+1)} b = small with macro data (rk) but large with financial data return stocks (NYSE value weighted) return 3m T-bill rk-d = a (Y/K)-d 1.2 1.15 1.1 1.05 1 0.95 0.9 0.85 Q3:03 Q4:00 Q1:98 Q2:95 Q3:92 Q4:89 Q1:87 Q2:84 Q3:81 Q4:78 Q1:76 Q2:73 Q3:70 Q4:67 Q1:65 Q2:62 Q3:59 0.8 Q4:56 | If we had really believed that in 1997: we would all have focused on improving intratemporal conditions, and would have never seen the model in PST! Q1:54 | 3 Basic intuition (2) | Hall, CKM: z use rk = α(Y/K) as real return z Very smooth, as is Ct+1/Ct z EE “fits”: small intertemporal shocks | PST: z Use financial data: real 3month T-Bill rate z More volatile z EE does not “fit” well: need large intertemporal shocks z Conclude: Finding of small intertemporal shocks is an artifact of omitting information from asset prices | Important lesson: z Be careful not omitting relevant information in estimating model! (get back later) Return on Capital A self-interested question: Where can I invest in this capital stock and earn rk (with almost no risk)? 1954:12004:3 Mean Std. dev. | z z rk-δ 7.89 0.76 capital can be rented each period or can invest in capital and resell on economy-wide market next quarter Realistic? What if capital is firm-specific? rk more volatile? z | Real return on stocks 8.40 28.20 rk: presumes: z | 3m Tbill (real) 1.66 2.36 If so, EE may not fit even with rk … but rk would be consistent with financial data Firm-specific capital => important intertemporal disturbances? 4 Empirical analysis | Estimate state-of-the-art model w/ many frictions, shocks (CEE, Smets-Wouters) | Look at special cases z z z z | NK model: sticky prices, flexible wages, fixed capital RBC model (replicate Hall, CKM) RBC model w/ real frictions RBC model + price rigidities + Add interest rate as observable variable Potentially interesting: allows us to understand what ingredients are responsible results Empirical analysis: Quibbles | Problems: Change the set of shocks for each model z Change set of observable variables for each model z Introduce financial market data only jointly w/ price rigidities z • Why not use real returns from financial markets to estimate RBC model? | Too many things change at same time! 5 Quibbles: NK Model | 4 shocks: b, technology, MP, markup …but no intratemporal taste shock (unfortunate!) (not separately identified from markup) | NKPC: | Problem: 100% variance in πt due to markup! Either k≈0 or mc≈0 | πt = k mct + β Etπt+1 + markupt Taste shock would have a larger effect on output than markup: z z affects directly mc => employment => output and/or mc => inflation (if k>0) => output Quibble with Conclusion | Full Model z z | Inter. pref. shock accounts for 48% of ΔlogC …. but only 1% of ΔlogY Other intertemporal shock (Invest.-specific tech. shock) accounts for 40% of ΔlogY and 57% of ΔlogL: huge! But when remove rigidities on consumption, investment, wage (Table 8): z z z Inter. pref. shock accounts for 5% of ΔlogY Invest.-specific tech. shock accounts for 4% of ΔlogY and 2% of ΔlogL Without these rigidities: • “Variability of output remains an intratemporal phenonemon” • Consistent with Hall, CKM despite the fact that include financial data! | To assess sources of business cycles: z z need to allow for a large class of possible shocks, frictions and data series. Not only data! 6 On the dangers of omitting information PST show that it is important to use financial data to assess sources of business cycles | Full model estimated with 7 series: | one for each concept: Y, C, I, L, W/P, π, R z Observation equation Xt = Ft z • Xt = data (e.g. Δlog(GDP defl.)) • Ft = model concepts (e.g. π) | Is that enough? Inflation: Which series? Quarterly inflation (demeaned) 2.5 G D P d e fl. P C E d e fl. C P I a ll 2 1.5 1 0.5 0 -0 . 5 -1 -1 . 5 -2 1965 1970 1975 1980 1985 1990 1995 2000 7 One proposal: Estimate DSGE model in data-rich environment Boivin-Giannoni (2005) | Generalize observation equation to: | Xt = ΛSt + et z z z Xt = potentially large vector of data series [e.g. Δlog(GDP defl.), Δlog(PCE defl.) ] St = latent state vector of model economy (satisfies restrictions imposed by model) et = series-specific component not related to model concept Inflation: Which series? Quarterly inflation (demeaned) 2.5 G D P d e fl. P C E d e fl. C P I a ll E s t . in fla t io n 2 1.5 1 0.5 0 -0 . 5 -1 -1 . 5 -2 1965 1970 1975 1980 1985 1990 1995 2000 8 Estimate a similar DSGE model in data-rich environment | Results (Boivin-Giannoni, 2005): z Sources of BC fluctuations depend a lot on data set considered • E.g., if Δlog(GDP defl.) = π • π largely explained by markup shocks • If π is estimated from several indicators • markup shocks irrelevant • Investment shocks matter even more when consider large data set • But labor supply shocks matter too Conclusion | | | | | PST: Very nice paper Emphasizes role of intertemporal disturbances as sources of BC fluctuations z Are important when combined with rigidities and when model confronted with financial data Main contribution: z Explain where relevance of intertemporal shocks comes from z Explain differences with Hall and CKM Suggestion: z In comparing models, do not change as many things at the same time Important message (in my view) z Be careful not to omit relevant information in estimating model! 9