## _wp05228

## Source details

**Canonical URL:** [_wp05228](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05228.pdf)

## Other formats

- [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05228.pdf.md)
- [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05228.pdf.json)

---

### I. Introduction
- Objective: assess the role of long-run shifts in the underlying rate of technological change in accounting for Italy’s 1990s productivity slowdown using unobserved stochastic components and Kalman filter techniques within a production function framework.
- Context and motivation:
  - Standard Real Business Cycle (RBC) models emphasize exogenous stochastic changes in total factor productivity (TFP) as the driving force of cyclical movements.
  - Recent literature questions the pure technology-driven RBC hypothesis and stresses separating productivity shifts from exogenous technological change versus non-technological forces.
  - Imperfect knowledge about the permanent nature of trend productivity shocks can bias real-time expectations; incorporating learning about trend shifts improves historical fit.

### II. The Puzzle: What’s Behind the 1990s Productivity Slowdown?
- Stylized facts and key statistics:
  - Italy’s annual GDP growth averaged almost 6 percent in the 1960s, but fell below 2 percent in the 1990s.
  - Annual TFP growth: averaged 1.2 percent in the 1980s, 0.6 percent over the 1990s, with a deceleration to 0.2 percent after 1995.
  - Unemployment rate fell to 8.1 percent in 2004Q2 (seasonally adjusted).
  - Employment ratio remained at 56 percent (noted as the lowest in the euro area).
  - Capital accumulation contribution: fluctuated just above 1 percentage point annually since the 1980s.

- Hypotheses evaluated:
  - Mismeasurement of factor quality changes:
    - Quality adjustments in capital (IT vs non-IT) account for some 0.1 percent of TFP growth; insufficient to explain late-1990s fall.
    - Labor composition and skill adjustments remove a sizeable part of the Solow residual but do not overturn evidence of a productivity slowdown.
    - Compositional and quality adjustments leave TFP explaining less than 1/4 of output growth in one study; capital compositional changes explain roughly another 0.1 percent of the deceleration.
    - “Embodied” technological change from IT accumulation was a major contribution to TFP in services; net of embodied change, the slowdown would have been larger.
  - Variable factor utilization:
    - True input services are more cyclical than measured inputs, making the Solow residual spuriously cyclical.
    - If hiring labor becomes cheaper at the margin, firms may reduce observed hours per worker and unobserved productivity, contributing to the observed decline in labor productivity.
  - Distortions and market imperfections:
    - High tax ratios, heavy regulatory burden and bureaucratic red tape, limited resource reallocation across sectors, and a large share of small and medium-size enterprises may have hampered productivity and been misattributed to the Solow residual.

- Scope of the paper:
  - Focuses on disentangling (iii) structural behavior and (iv) cyclical comovements using a multivariate unobserved component model of the production function to separate permanent trend shifts from cyclical fluctuations.

### III. Use of Unobserved Components for Growth Accounting
- Business cycle properties and empirical regularities (Italy, sample period):
  - Business cycle periodicity assumed between 6 quarters and 8 years.
  - Italian business cycle average duration: 4½ years (vs. 5-year euro area).
  - Standard deviation of output: 1.35 percent (Italy); 0.84 percent (euro area); 1.34 percent (United States).
  - Average trough-to-peak expansions: 13 quarters; average recessions: 9 quarters.
  - Most severe recession dated 1974Q1–1975Q3; other notable recessions: 1963Q4–1965Q1, 1980Q2–1983Q1, 1969Q1–1972Q4; recessions 1990Q1–1993Q3 and 1976Q4–1977Q4 had smaller amplitude.
  - Cross-correlations (band-pass filtered):
    - Employment, average hours, capacity utilization, and labor productivity are strongly procyclical.
    - Employment, labor force, and participation lag the cycle; capacity utilization and labor productivity are coincident.
    - Hours per employee lead the cycle by about one quarter.
    - Unemployment rate weakly countercyclical and lags the cycle by one year.
  - Recent (post-2001) downturn: productivity responses indicate greater idling capacity compared with 1992–93 recession; labor productivity dropped sharply with declines in average hours and spare capacity carrying the adjustment burden.

- Production function and unobserved components (model structure):
  - Flexible Cobb-Douglas production function with variable capacity utilization C in (0,1]:
    - Y = A * L^(β) * (C K)^(1−β)  (log-linearized as y = a + (1−β)k + βl + c).
  - Factor inputs decomposed additively into permanent (superscript *) and cyclical (superscript c) components; capital stock assumed fully permanent.
  - Labor decomposed into working-age population (wpop), participation ratio (pr), unemployment rate (u), and average hours per employee (h); each with permanent and cyclical parts.
  - Measurement equation for real output and transition equations for permanent and transitory components; reference cycle imposed as an AR(2) driven by industrial production index (ip).
  - Permanent component trends follow random walk processes with stochastic drifts; P matrix determines persistence (e.g., if 0<P1,1<1 then TFP growth converges back to steady-state rate *κ1).
  - Shocks to transitory and permanent components assumed i.i.d. and orthogonal.
  - State-space representation enables Kalman filter and smoother for maximum likelihood estimation and signal extraction.

### IV. Estimation Results (selected numeric findings and diagnostics)
- Data and sample: quarterly data 1960:1–2004:2; Kalman filters start in 1970:2. Three specifications estimated:
  1. Fixed labor income share, no explicit control for capacity utilization.
  2. Control for ISAE survey-based capacity utilization in transition for cyclical TFP.
  3. Control for capacity utilization and freely estimated labor income share.

- Key estimated coefficients, diagnostics, and exact reported figures:
  - φ1 = 1.276 (.065) in Technology model; φ2 = -0.407 (.041).
  - τa0 = .631 (.077) in Technology model; τa0 = .306 (.081) when controlling for capacity utilization.
  - τa1 = -.156 (.074) in Technology model; τa1 = -.014 (.023) when controlling for capacity utilization.
  - 1−β = .337 (restricted) in first two models; estimated 1−β = .386 (.178) in third model.
  - σ_εψip = .009 (.001) across specifications.
  - Log-likelihoods: 3075.86 (Technology), 3091.30 (Technology + Capacity Utilization), 3092.55 (Technology + Capacity Utilization + Estimated Labor Share).
  - SEE: 0.93% (Technology), 0.86% (Tech + Cap. Util.), 0.85% (Tech + Cap. Util. + Est. share).
  - One-step-ahead predictive state: ∆y*_{t+1|t} = 1.25% (potential growth); y^c_{t+1|t} = -1.00% (output gap); NAIRU_{t+1|t} = 8.5%.
  - AR(5) test p-values indicate AR residual issues in first specification (AR(5) = 3.36 [0.01]**), improved when controlling for capacity utilization.
  - Predictive standard errors at sample end: 0.9 percent; uncertainty split ~ two-thirds disturbance uncertainty and one-third parameter uncertainty.

- Principal empirical findings:
  - The rate of technological change has been on a downward slope for decades and declined further over the 1990s.
  - Potential growth recovered from end-1993 to end-2001: rising from an annual rate of 0.7 percent at end of the 1992–93 recession to over 2 percent before the current slowdown.
  - Structural behavior: potential growth and TFP growth differ markedly while comoving over the cycle; capital deepening rate stable over time at around 1 percent.
  - At sample end: potential growth estimated around 1¼ percent; output below potential by 1 percent.
  - Controlling for capacity utilization substantially reduces the role of technology in explaining cyclical fluctuations:
    - Without capacity control: a 1 percent increase of output above potential raises productivity by over 0.6 percent; variance of cyclical component of TFP over seven times variance of permanent component and accounts for almost all cyclical variation in output.
    - With capacity utilization control: contemporaneous correlation of cyclical productivity with the business cycle halves and variance drops dramatically (insignificant), implying short-run fluctuations in TFP are largely captured by variable factor utilization.
  - Unemployment rate found weakly countercyclical: falls by 0.04 as output rises 1 percent above potential.
  - Short-run variations in labor participation and average hours broadly a-cyclical.

- Role of productivity shocks (impulse responses and variance decomposition):
  - Permanent productivity shocks:
    - A positive technology shock generates a negligible long-run decline in hours growth; potential growth response always positive.
  - Transitory productivity shocks:
    - Evidence points to nontechnological factors as main driver of positive comovements between hours and output.
  - Variance decompositions:
    - Employment explains around 40 percent of long-run movements in growth.
    - Labor participation accounts for around 30 percent of shifts in potential growth.
    - The Solow residual absorbs approximately 60 percent of the cyclical variation in real GDP.
    - Implication: changes in employment growth respond little to business cycle fluctuations, which are largely associated with transitory nontechnological shifts such as variable factor utilization.

### V. Concluding Remarks and Policy Implications
- Summary of conclusions:
  - Decomposing long-run technological change from nontechnological forces is essential to understanding productivity growth.
  - The rate of technological change in Italy has been declining for several decades and slowed further in the 1990s.
  - Adjusting the Solow residual for cyclical factors removes much of the observed residual in the 1992–93 recession, but the adjustment reinforces evidence of a further slowdown in trend TFP over the 1990s.
  - Major source of potential growth variation associated with changes in employment growth, highlighting structural nature of labor market dynamics.
  - Technology shocks do not generate the strong positive comovement between GDP and hours predicted by standard RBC models; shifts in factor utilization explain the bulk of business cycle fluctuations and induce positive short-run shifts in both output and hours.

- Policy implications and research priorities:
  - Address factors preventing further employment growth and those constraining factor efficiency.
  - Stagnant and procyclical productivity growth may indicate a negative and persistent demand shock within an economy featuring sustained wage moderation.
  - Enduring sluggishness in factor efficiency may reflect distortions in product markets, inadequate competition in key sectors, and overhead costs; literature links such frictions to worse long-term productivity performance.
  - Further research needed to assess whether market frictions can explain the persistent productivity slowdown observed over recent decades.

*Source: IMF staff paper excerpt, "Business Cycle Variance Decomposition" (chapter/section text supplied).*

### References..............................................................................................................

### _wp05228 - References..............................................................................................................

### Tables
- 1A. Growth Accounting: Period Averages ..............................................................................19
- 1B. Growth Accounting: Adjusting for Factor Quality Changes ............................................20
- 1C. Growth Accounting: Adjusting for Compositional and Factor Quality Changes .............21
- 2.    Italy’s Business Cycle Properties (1963q1–2001q2) ........................................................22
- 3.    Real-Time Estimates from Unobserved Component Models (1960q1–2004q2) .............23

### Figures
- 1.    Italy: Labor Force Statistics ..............................................................................................24
- 2A. Adjusting TFP for Compositional and Quality Changes in Capital..................................25
- 2B. Adjusting TFP for Compositional and Quality Changes in Labor....................................25
- 3A. Adjusting TFP for Cyclical Factors ..................................................................................26
- 3B. Potential and Cyclically Adjusted TFP Growth ................................................................26
- 4.   One-Step Ahead Forecast Errors Controlling for Capacity Utilization .............................27
- 5.   Response to Cholesky One S.D. Innovations to Permanent Components .........................28
- 6.   Response to Cholesky One S.D. Innovations to Transitory Components .........................29
- 7.   Potential Growth Variance Decomposition .......................................................................30

*Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05228.pdf*

### 8.   Business Cycle Variance Decomposition ..........................................................................30

### 8.   Business Cycle Variance Decomposition ..........................................................................30

### I. Introduction
- Standard Real Business Cycle (RBC) models emphasize exogenous stochastic changes in total factor productivity (TFP) as the driving force of cyclical movements in macro aggregates.
- Recent literature questions the technology-driven RBC hypothesis, arguing for the separation of productivity shifts associated with exogenous technological change from productivity shifts induced by non-technological forces.
- Imperfect knowledge about the permanent nature of trend productivity shocks can cause real-time expectations to overemphasize slackness or inflationary pressures; incorporating learning about trend shifts improves models’ historical fit.
- This paper’s goal: assess the role of long-run shifts in the underlying rate of technological change in accounting for Italy’s 1990s productivity slowdown using unobserved stochastic components and Kalman filter techniques within a production function framework.

### II. The Puzzle: What’s Behind the 1990s Productivity Slowdown?
H3: Stylized Facts
- Italy’s annual GDP growth averaged almost 6 percent in the 1960s, but fell below 2 percent in the 1990s.
- Breaking down GDP growth into labor, capital, and TFP contributions shows the slowdown over 1960–2001 is explained almost completely by the decline in TFP growth (Solow residual).
- Annual TFP growth: averaged 1.2 percent in the 1980s, 0.6 percent over the 1990s, with a deceleration to 0.2 percent after 1995.
- Employment and average hours per employee historically dragged on growth; over the last decade (relative to the paper’s sample) reforms increased employment ratio and labor participation.
  - Unemployment rate fell to 8.1 percent in 2004Q2 (seasonally adjusted).
  - Employment ratio remained at 56 percent (noted as the lowest in the euro area).
- Capital accumulation contributed reliably to growth; since the 1980s capital’s contribution fluctuated just above 1 percentage point annually.

H3: Hypotheses Explaining the Slowdown
- Mismeasurement of factor quality changes:
  - Quality adjustments in capital (IT vs non-IT) appear to account for some 0.1 percent of TFP growth; not sufficient to explain the late-1990s fall.
  - Labor composition and skill adjustments remove a sizeable part of the Solow residual but do not overturn the evidence of a productivity slowdown.
  - Compositional and quality adjustments leave TFP explaining less than 1/4 of output growth in one study; capital compositional changes explain roughly another 0.1 percent of the deceleration.
  - “Embodied” technological change from IT accumulation was a major contribution to TFP in services; net of embodied change, the slowdown would have been larger.
- Variable factor utilization:
  - True input services are more cyclical than measured inputs; measured productivity (Solow residual) can be spuriously cyclical.
  - If hiring labor becomes cheaper at the margin, firms may reduce observed hours per worker and unobserved productivity, contributing to the observed decline in labor productivity.
- Distortions and market imperfections:
  - High tax ratios, heavy regulatory burden and bureaucratic red tape, limited resource reallocation across sectors, and a large share of small and medium-size enterprises may have hampered productivity and been misattributed to the Solow residual.

- The paper limits focus to dimensions (iii) structural behavior and (iv) cyclical comovements, using a multivariate unobserved component model of the production function to disentangle permanent trend shifts from cyclical fluctuations.

### III. The Pieces: Use of Unobserved Components for Growth Accounting
H3: Business Cycle Properties and Empirical Regularities
- Business cycle periodicity assumed between 6 quarters and 8 years (NBER definition); Baxter-King band-pass filter referenced.
- Italian business cycle average duration: 4½ years (vs. 5-year euro area).
- Standard deviation of output estimated at 1.35 percent (Italy), compared to 0.84 percent (euro area) and 1.34 percent (United States).
- Average trough-to-peak expansions: 13 quarters; average recessions: 9 quarters.
- Most severe recession dated 1974Q1–1975Q3; other notable recessions: 1963Q4–1965Q1, 1980Q2–1983Q1, 1969Q1–1972Q4; recessions 1990Q1–1993Q3 and 1976Q4–1977Q4 had smaller amplitude.
- Italy’s cycle highly synchronized with euro area; larger fluctuations in 1970s attributed to reliance on imported oil (share of energy-related imports declined by around 40 percent during the 1980s).
- Cross-correlations (band-pass filtered) show employment, average hours, capacity utilization, and labor productivity are strongly procyclical. Employment, labor force, and participation lag the cycle; capacity utilization and labor productivity are coincident. Hours per employee lead the cycle by about one quarter. Unemployment rate weakly countercyclical and lags the cycle by one year.
- Recent (post-2001) downturn: productivity responses indicate greater idling capacity compared with 1992–93 recession; labor productivity dropped sharply during the recent slowdown with declines in average hours and spare capacity carrying the adjustment burden.

H3: Production Function and UC Model Specification
- Flexible Cobb-Douglas production function with variable capacity utilization C in (0,1]:
  - Y = A * L^(β) * (C K)^(1−β)  (log-linearized in paper as equation (2): y = a + (1−β)k + βl + c)
- Additive decomposition of factor inputs into permanent (superscript *) and cyclical (superscript c) components; capital stock assumed fully permanent.
- Labor (l) decomposed into working-age population (wpop), participation ratio (pr), unemployment rate (u), and average hours per employee (h); each has permanent and cyclical parts.
- Measurement equation for real output and transition equations for permanent and transitory components formulated; reference cycle imposed as a second-order autoregressive process driven by industrial production index (ip).
- Permanent component trends follow random walk processes with stochastic drifts; P matrix determines persistence (e.g., if 0<P1,1<1 then TFP growth converges back to steady-state rate *κ1).
- Shocks to transitory and permanent components assumed i.i.d. and orthogonal (demand vs supply shocks).
- State-space representation allows Kalman filter and smoothing for maximum likelihood estimation and signal extraction.

H3: Estimation Results (summary from Table 3 and Figures)
- Models estimated on quarterly data 1960:1–2004:2; Kalman filters start in 1970:2. Three specifications:
  1. Fixed labor income share, no explicit control for capacity utilization.
  2. Control for ISAE survey-based capacity utilization in transition for cyclical TFP.
  3. Control for capacity utilization and freely estimated labor income share.
- Key numeric estimates and diagnostics (selected, exact reported figures):
  - φ1 = 1.276 (.065) in Technology model; φ2 = -0.407 (.041).
  - τa0 = .631 (.077) in Technology model; τa0 = .306 (.081) when controlling for capacity utilization.
  - τa1 = -.156 (.074) in Technology model; τa1 = -.014 (.023) when controlling for capacity utilization.
  - 1−β = .337 (restricted) in first two models; estimated 1−β = .386 (.178) in third model.
  - σ_εψip = .009 (.001) across specifications.
  - Log-likelihoods: 3075.86 (Technology), 3091.30 (Technology + Capacity Utilization), 3092.55 (Technology + Capacity Utilization + Estimated Labor Share).
  - SEE: 0.93% (Technology), 0.86% (Tech + Cap. Util.), 0.85% (Tech + Cap. Util. + Est. share).
  - One-step-ahead predictive state: ∆y*_{t+1|t} = 1.25% (potential growth); y^c_{t+1|t} = -1.00% (output gap); NAIRU_{t+1|t} = 8.5%.
  - AR(5) test p-values indicate AR residual issues in first specification (AR(5) = 3.36 [0.01]**), improved when controlling for capacity utilization.
- Main empirical findings:
  - The rate of technological change has been on a downward slope for decades and declined further over the 1990s (Figure 3A).
  - Potential growth recovered from end-1993 to end-2001: rising from an annual rate of 0.7 percent at end of the 1992–93 recession to over 2 percent before the current slowdown (Figure 3B).
  - Structural behavior: potential growth and TFP growth differ markedly while comoving over the cycle; capital deepening rate stable over time at around 1 percent.
  - At sample end: potential growth estimated around 1¼ percent; output below potential by 1 percent. Predictive standard errors of 0.9 percent, with about two-thirds due to disturbance uncertainty and one-third due to parameter uncertainty.
  - Controlling for capacity utilization substantially reduces the role of technology in explaining cyclical fluctuations in real output:
    - Without capacity control: a 1 percent increase of output above potential raises productivity by over 0.6 percent; variance of cyclical component of TFP over seven times variance of permanent component and accounts for almost all cyclical variation in output.
    - With capacity utilization control: contemporaneous correlation of cyclical productivity with the business cycle halves and variance drops dramatically (insignificant), implying short-run fluctuations in TFP are largely captured by variable factor utilization.
  - Unemployment rate found weakly countercyclical: falls by 0.04 as output rises 1 percent above potential.
  - Short-run variations in labor participation and average hours broadly a-cyclical.

H3: Role of Productivity Shocks (Impulse Responses and Variance Decompositions)
- Permanent productivity shocks:
  - A positive technology shock generates a negligible long-run decline in hours growth; potential growth response always positive (Figure 5).
- Transitory productivity shocks:
  - Evidence points to nontechnological factors as main driver of positive comovements between hours and output (Figure 6).
- Variance decompositions:
  - Bulk of permanent variation in output driven by shifts in labor trends:
    - Employment explains around 40 percent of long-run movements in growth.
    - Labor participation accounts for around 30 percent of shifts in potential growth (Figure 7).
  - The Solow residual absorbs approximately 60 percent of the cyclical variation in real GDP (Figure 8).
  - Implication: changes in employment growth respond little to business cycle fluctuations, which are largely associated with transitory nontechnological shifts such as variable factor utilization.

### IV. Concluding Remarks
- Decomposing long-run technological change from nontechnological forces is essential to understanding productivity growth.
- Empirical results for Italy:
  - The rate of technological change has been declining for several decades and slowed further in the 1990s.
  - Adjusting the Solow residual for cyclical factors removes a sizable part of the observed residual in the 1992–93 recession, but the adjustment reinforces evidence of a further slowdown in trend TFP over the 1990s.
  - Major source of potential growth variation associated with changes in employment growth, highlighting structural nature of labor market dynamics.
  - Technology shocks do not generate the strong positive comovement between GDP and hours predicted by standard RBC models; shifts in factor utilization explain the bulk of business cycle fluctuations and induce positive short-run shifts in both output and hours.
- Policy implications:
  - Importance of addressing factors preventing further employment growth and those constraining factor efficiency.
  - Stagnant and procyclical productivity growth may indicate a negative and persistent demand shock within an economy featuring sustained wage moderation.
  - Enduring sluggishness in factor efficiency may reflect distortions in product markets, inadequate competition in key sectors, and overhead costs; literature links such frictions to worse long-term productivity performance.
  - Further research needed to assess whether market frictions can explain the persistent productivity slowdown observed over recent decades.

*Source: IMF staff paper excerpt, "Business Cycle Variance Decomposition" (chapter/section text supplied).*

### References

### _wp05228 - References

### References list (as provided in the source)
- Abbott, T. A., Z. Griliches, and J. Hausman, 1998, “Short Run Movements in Productivity: Market Power versus Capacity Utilization,” in Practicing Econometrics: Essays in Method and Application, ed. By Zvi Griliches (Cheltenham, England: Elgar). 
- Agresti, A. M., and B. Mojon, 2001, “Some Stylised Facts on the Euro Area Business Cycle,” ECB Working Paper No. 95 (Frankfurt: European Central Bank). 
- Alcalá, F., and I. Sancho, 2004, “Output Composition and the US Output Volatility Decline,” Economics Letters, Vol. 82, pp. 115-20.  
- Apel, M. and P. Jansson, 1999, “A Theory-Consistent System Approach for Estimating Potential Output and the NAIRU,” Economics Letters, Vol. 64, pp. 271-275.  
- Banca d’Italia, 2004, Concorrenza ed Efficienza delle Imprese Italiane negli Anni Novanta (unpublished, Rome: Bank of Italy). 
- Bassanetti, A., M. Iommi, C. Jona-Lavinio, and F. Zollino, 2004, “La Crescita dell'Economia Italiana negli Anni Novanta tra Ritardo Tecnologico e Rallentamento della Produttivitá,” Temi di Discussione No. 539 (Rome: Bank of Italy). 
- Basu, S., 1996, “Cyclical Productivity: Increasing Returns or Cyclical Utilization?” Quarterly Journal of Economics, Vol. 111, pp. 719-51. 
- Basu, S., Fernald, J., and M. S. Kimball, 2004, “Are Technology Improvements Contractionary?” NBER Working Paper No. 10592 (Cambridge, Massachusetts: National Bureau of Economic Research). 
- Basu, S., and M. S. Kimball, 1997, “Cyclical Productivity with Unobserved Input Variation,” NBER Working Paper No. 5915 (Cambridge, Massachusetts: National Bureau of Economic Research). 
- Baxter, B., and R. G. King, 1999, “Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series,” The Review of Economics and Statistics, Vol. 81, pp. 575-93. 
- Blanchard, O., and J. Simon, 2001, “The Long and Large Decline in U.S. Output Volatility,” Brookings Papers on Economic Activity: 1, pp. 135–64. (Washington: Brookings Institution).  
- Brandolini, A., and P. Cipollone, 2001, “Multifactor Productivity and Labor Quality in Italy, 1981-2000,” Temi di Discussione No. 422 (Rome: Bank of Italy). 
- Bugamelli, M., and A. Rosolia, 2004, “Produttivitá e Concorrenza Estera,” (unpublished, Rome: Bank of Italy). 
- Busato, F., and B. Chiarini, 2004, “Market and Underground Activities in a Two Sector Dynamic Equilibrium Model,” Economic Theory, Vol. 23, pp. 831-61. 
- Camba-Mendez, G., and D. Rodriguez-Palenzuela, 2001, “Assessment Criteria for Output Gap Estimates,” ECB Working Paper No. 54 (Frankfurt: European Central Bank). 
- CBO, 1995, “CBO’s Method for Estimating Potential Output,” CBO Memorandum (Washington: Congressional Budget Office). 
- Cipollone, P., and A. Guelfi, 2004, “Il Valore dei Contratti Flessibili,” (unpublished, Rome: Bank of Italy). 
- Clark, P.K., 1987, “The Cyclical Component of U.S. Economic Activity,” Quarterly Journal of Economics, Vol. 102, pp. 797-814. 
- Clark, P.K., 1989, “Trend Reversion in Real Output and Unemployment,” Journal of Economics, Vol. 40, pp. 15-32. 
- Conesa, J. C., C. Díaz-Moreno, and J. E. Galdón-Sánchez, 2001, “Underground Economy and Aggregate Fluctuations,” Spanish Economic Review, Vol. 3, pp. 41-53.  
- Conway, P. and B. Hunt, 1997, “Estimating Potential Output: A Semi-Structural Approach,” Bank of New Zealand Discussion Paper, No. 97/9 (Wellington: Reserve Bank of New Zealand). 
- Cooley, T. F., and E. C. Prescott, 1995, “Economic Growth and Business Cycles,” in Frontiers of Business Cycle Research, ed. by T. F. Cooley (Princeton: Princeton University Press). 
- De Masi, P., 1997, “IMF Estimates of Potential Output: Theory and Practice,” IMF Working Paper No. 97/177 (Washington: International Monetary Fund). 
- Edge, R., T. Laubach, and J. Williams, 2004, “Learning and Shifts in Long-Run Productivity Growth,” Federal Reserve Board Finance and Economics Discussion Series No. 2004-21 (Washington: The Federal Reserve Board). 
- Erceg, C., L. Guerrieri, and C. Gust, 2005, “Can Long-Run Restrictions Identify Technology Shocks?” Journal of the European Economic Association, forthcoming.  
- Estevão, M., 2004, “Why is Productivity Growth in the Euro Area So Sluggish?,” IMF Working Paper No. 04/200 (Washington: International Monetary Fund). 
- European Commission, 2004, “EPC Report on Potential Output and the Output Gap,” Economic Policy Committee, Report No. EPC/ECFIN/056/04 (Brussels: European Commission). Available at: http://europa.eu.int/comm/economy_finance/epc/documents/2004/output_march2004_en.pdf. 
- Fabiani, S., and R. Mestre, 2001, “A System Approach for Measuring the Euro Area NAIRU,” ECB Working Paper No. 54 (Frankfurt: European Central Bank). 
- Fernald, J., 2004, “Trend Breaks, Long Run Restrictions, and the Contractionary Effects of a Technology Shock,” Federal Reserve Bank of Chicago Working Paper No. 20 (Chicago: Federal Reserve Bank of Chicago). 
- Francis, N., and R. Valerie, 2004, “A New Measure of Hours per Capita with Implications for the Technology-Hours Debate,” (unpublished; San Diego: University of California). 
- Galì, J., 2005, “Trends in Hours, Balanced Growth, and the Role of Technology in the Business Cycle,” Federal Reserve Bank of St. Louis Review, Vol. 87, pp. 459-86. 
- Galì, J., 2004, “On the Role of Technology Shocks as a source of Business Cycles: Some New Evidence,” Journal of the European Economic Association, Papers and Proceedings, Vol. 2, pp. 372-80. 
- Galì, J., and P. Rabanal, 2004, “Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Postwar U.S. Data?,” in NBER Macroeconomics Annual, pp. 225-88 (Cambridge, Massachusetts: National Bureau of Economic Research).  
- Giorno, C., P. Richardson, D. Roseveare, and P. van den Noord, 1995, “Estimating Potential Output, Output Gaps and Structural Budget Balances,” OECD Economics Department Working Paper No. 152 (Paris: Organization for Economic Cooperation and Development). 
- Gordon, R.J., 1997, ”The Time-Varying NAIRU and Its Implications for Economic Policy,” Journal of Economic Perspectives, Vol. 11, pp. 11-32. 
- Greenwood, J., Z. Hercowitz, and P. Krussell, 1997, “Long-Run Implications of Investment-Specific Technological Change,” American Economic Review, Vol. 87, pp. 342-62.  
- Harvey, A.C., 1989, Forecasting, Structural Time Series and the Kalman Filter (Cambridge, England: Cambridge University Press). 
- Harvey, A.C., and A. Jäger, 1993, “Detrending, Stylized Facts and the Business Cycle,” Journal of Applied Econometrics, Vol. 8, pp. 231-47. 
- Hercowitz, Z., 1998, “The ‘Embodiment’ Controversy: A Review Essay,” Journal of Monetary Economics, Vol. 41, pp. 217-24. 
- IMF, 2002, “New Estimates of Potential Output,” Country Report No. 02/232 (Washington: International Monetary Fund). 
- IMF, 2004, “Why is Productivity Growth in the Euro Area so Sluggish?” Country Report No. 04/235 (Washington: International Monetary Fund).  
- IMF, 2005, “Underlying Trends and Cyclical Fluctuations: Pieces of the Same Puzzle,” Country Report No. 05/41 (Washington: International Monetary Fund).  
- ISAE, 2001, Rapporto ISAE—Priorità Nazionali: Intenzioni e Risultati della Regolamentazione (Rome: Istituto di Studi e Analisi Economica).  
- ISAE, 2003, Rapporto ISAE—Priorità Nazionali: Dimensioni Aziendali, Competitività, Regolamentazione (Rome: Istituto di Studi e Analisi Economica). 
- ISAE, 2004, Rapporto ISAE—Priorità Nazionali: Dimensioni Aziendali, Competitività, Regolamentazione (Rome: Istituto di Studi e Analisi Economica). 
- Kim, C., and C. R. Nelson, 1999, State-Space Models with Regime Switching (Cambridge, Massachusetts: The MIT Press). 
- King, R. G., and S. T. Rebelo, 1999, “Resuscitating Real Business Cycles,” in Handbook of Macroeconomics: Volume 1B, ed. by J.B. Taylor and M. Woodford, pp. 928-1002 (Amsterdam: Elsevier). 
- Kydland, F. E., and E. C. Prescott, 1982, “Time to Build and Aggregate Fluctuations,” Econometrica, Vol. 50, pp. 1345-70. 
- Kuttner, K. N., 1994, “Estimating Potential Output as a Latent Variable,” Journal of Business and Economic Statistics, Vol. 12, pp. 361-68. 
- Lansing, K., 2000, “Learning about a Shift in Trend Output: Implications for Monetary Policy and Inflation,” Federal Reserve Bank of San Francisco Working Paper No. 2000-16 (San Francisco: Federal Reserve Bank of San Francisco). 
- McConnell, M., and G. Perez-Quiros, 2000, “Output Fluctuations in the United States: What has Changed since the Early 1980’s,” American Economic Review, Vol. 90, pp. 1464–76.  
- Musso, A., T. Proietti, and T. Westermann, 2002, “Estimating Potential Output and the Output Gap for the Euro Area: A Model-Based Production Function Approach,” EUI Working Paper No. 2002/9 (Florence: European University Institute). 
- OECD, 2003, OECD Economic Surveys 2003: Italy, Vol. 2003/13 (Paris: Organization for Economic Cooperation and Development). 
- Ramey, V. A., and M. D. Shapiro, 1998, “Costly Capital Reallocation and the Effects of Government Spending,” Carnegie-Rochester Conference Series on Public Policy, Vol. 48, pp. 145-94. 
- Roberts, J. M., 2001, “Estimates of the Productivity Trend Using Time-Varying Parameter Techniques,” Contributions to Macroeconomics, Vol. 1, pp. 1-27. 
- Rotemberg, J. J., and M. Woodford, 1991, “Markups and the Business Cycle,” NBER Macroeconomics Annual (Cambridge, Massachusetts: National Bureau of Economic Research). 
- Rotemberg, J. J., and M. Woodford, 1995, “Dynamic General Equilibrium Models with Imperfectly Competitive Product Markets,” in Frontiers of Business Cycle Research, ed. by Thomas F. Cooley (Princeton: Princeton University Press). 
- Rünstler, G., 2001, “The Information Content of Real-Time Output Gap Estimates: An Application to the Euro Area,” ECB Working Paper No. 182 (Frankfurt: European Central Bank). 
- Solow, R. M., 1957, “Technological Change and the Aggregate Production Function,” Review of Economics and Statistics, Vol. 39, pp. 312-20. 
- Stock, J., and M. Watson, 2002, “Has the Business Cycle Changed and Why?” NBER Macroeconomics Annual 2002 (Cambridge, Massachusetts: National Bureau of Economic Research). 
- Stock, J., and M. Watson, 2000, “Business Cycle Fluctuations in US Macroeconomic Time Series,” in Handbook of Macroeconomics, Volume I, ed. by B. Taylor and M. Woodford (Amsterdam: Elsevier). 
- Timmer, M.P., G. Ypma, and B. van Ark, 2003, "IT in the European Union: Driving Productivity Divergence?” (unpublished; Groningen, The Netherlands: Groningen Growth and Development Centre). Available at http://www.ggdc.net/pub/online/gd67(online).pdf
- Zezza, R., 2004, “A Measure of Output Gap for Italy through Unobserved Component Models,” (unpublished, Rome: Bank of Italy).

*Content derived from the document titled "_wp05228 - References".*

---


_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05228.pdf_