Bardone, L., and V. Reitano, “Italy in the Euro Area: The Adjustment Challenge”, in Marco Buti (ed) Italy in EMU: The Challenges of Adjustment and Growth (London: Palgrave)
Bassanetti, di Antonio, Martina Cecioni, Andrea Nobili, and Giordano Zevi, 2009, “The main recessions in Italy: a retrospective comparison”, Bank of Italy Occasional Paper 46.
Benes, J., K. Clinton, R. Garcia-Saltos, M. Johnson, D. Laxton, and T. Matheson, 2010, “The Global Financial Crisis and Its Implications for Potential Output”, Forthcoming IMF Working Paper.
Cerra, V., and S. C. Saxena, 2008, “Growth Dynamics: The Myth of Economic Recovery”, American Economic Review, Vol. 98 (1), pp. 439–457.
Codogno L., and Felici, 2008, “Assessing Italy’s Reform Challenges: What Do Growth Accounting and Structural Indicators Say?,” Rivista di Politica Economica, pp 43-118.
Cotis J.P., J. Elmeskov, and A. Mourougane, 2005, “Estimates of potential output: Benefits and pitfalls from a policy perspective,” in L. Rechling (ed) Euro area business cycle: Stylized facts and measurement issues, CEPR London.
Daveri F., and Jona-Lasinio, 2005, “Italy’s Decline: Getting the Facts Right,” Il Giornale degli Economisti e Annali di Economia.
Dew-Becker, J., and Gordon, 2009, The Role of Labor Market Changes in the Slowdown of European Productivity Growth, NBER Working Paper No. 13840.
European Commission, 2009, “The EU’s Response to Support the Real Economy During the Economic Crisis: An Overview of Member States’ Recovery Measures,” European Economy—Occasional Papers 51, Directorate-General for Economic and Financial Affairs.
Furceri D. and A. Mourougane, 2009, “The effect of financial crises on potential output: New empirical evidence from OECD countries,” OECD Economics Department Working Paper 699.
Haugh D., P. Ollivaud, and D. Turner, 2009, “The macroeconomic consequences of banking crises in OECD countries,” OECD Economics Department Working Paper 683.
International Monetary Fund, 2009, “What’s the Damage? Medium-Term Output Dynamics after Financial Crises”, World Economic Outlook, Chapter 4. Washington, DC: International Monetary Fund.
Sgherri, 2005, “Long-Run Productivity Shifts and Cyclical Fluctuations: Evidence for Italy,” IMF Working Papers 05/228, International Monetary Fund.
Appendix I. Features and Pitfalls of the HP Filter
Appendix II. A Multivariate Filter
Appendix III. A Production Function with Unobserved Stochastic Components10
On this point, see evidence in Gordon and Dew-Becker (2008).
Data is not available to examine the recovery to pre-crisis trend for other historical recessions.
While the HP filter imposes restrictions on the shape of the cyclical and trend component of real output, which may not hold after the crisis, the two multivariate unobserved component models have the merit of extracting long-term trends by exploiting additional information about short-run relationships, like the unemployment-inflation trade-off (in the case of the MV filter), or the productivity-capacity utilization relation (in the case of the PFA). The analytical underpinnings of a multivariate filter and a production function approach with unobserved stochastic components are reported in Appendix II and Appendix III, respectively.
Using a production function, such trend levels are extracted by taking into account the relationships between the cyclical components of output and unemployment, the link between cyclical productivity and cyclical hours worked, as well as the impact of the business cycle on labor supply dynamics. Estimates are carried out using real-time data and a Bayesian framework. In order to use sufficiently long quarterly frequency time series, a PFA must usually rely on low-quality data on capital stocks and hours worked, raising issues on whether the TFP component will be spuriously contaminated by measurement problems.
Because of the high volatility of the Solow residual, conditioning real-time output decomposition upon indicators of demand pressures in product and labor market provides smoother estimates of potential growth than unobserved component models relying on a production function approach.
The equations that are presented here are those used for the estimation of the potential output in “The Global Financial Crisis and Its Implications for Potential Output”, Forthcoming IMF Working Paper.
This appendix draws on Sgherri (2004).
In the model we have in mind, all the non-technological effects (e.g., non-constant returns to scale, imperfect competitions, and input reallocations) considered by Basu, Fernald, and Kimball (2004) and briefly discussed in Section II, do not operate in the long run, so that over long horizons, productivity is solely driven by technology. In particular, whenever a shock increases demand, the increase in production would mandate higher output per firm and would lead to increases in profits. This would spur entry and drive per firm output and profits down to zero. By the same token, in order for increasing returns to contribute to long-run productivity growth, firms should expand their scale of operation, thereby reducing unit costs forever. This is impossible, as scale economies would be reduced as new firms enter the market and per-firm output falls. Non-technological effects would, however, operate over the short run and would therefore be part of the cyclical component of the Solow residual.
Basu and Kimball (1997) show that if the sole cost of changing the workweek of capital is that workers need to be compensated for working at night, then one can use a single proxy for changes in both effort and capital utilization.
To maintain log-linearity, while enabling modeling the NAIRU, we use the first-order Taylor approximation for the employment rate, so that et =In(1 – ut) ≈ –ut.
By construction, demand and supply shocks are assumed to be orthogonal.
For a thorough exposition of the state space methodology, the reader may refer to Harvey (1989) and Kim and Nelson (1999). Estimation was carried out in Gauss 6.0.