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ARE UNIT ROOT TESTS USEFUL IN THE DEBATE OVER THE (NON)STATIONARITY OF HOURS WORKED?

Published online by Cambridge University Press:  23 October 2013

Amélie Charles ,
Olivier Darné  and
Fabien Tripier
Amélie Charles
Affiliation:
Audencia Nantes School of Management
Olivier Darné*
Affiliation:
LEMNA, University of Nantes
Fabien Tripier
Affiliation:
CLERSE, University of Lille 1 and CEPII
*
Address correspondence to: Olivier Darné, IEMN-IAE, Chemin de la censive du Terte, BP 52231, 44322 Nantes Cedex 3, France; e-mail: [email protected].
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Abstract

The performance of unit root tests on simulated series is compared, using the business-cycle model of Chang et al. [Journal of Money, Credit and Banking 39(6), 1357–1373 (2007)] as a data-generating process. Overall, Monte Carlo simulations show that the efficient unit root tests of Ng and Perron (NP) [Econometrica 69(6), 1519–1554 (2001)] are more powerful than the standard unit root tests. These efficient tests are frequently able (i) to reject the unit-root hypothesis on simulated series, using the best specification of the business-cycle model found by Chang et al., in which hours worked are stationary with adjustment costs, and (ii) to reduce the gap between the theoretical impulse response functions and those estimated with a Structural VAR model. The results of Monte Carlo simulations show that the hump-shaped behavior of data can explain the divergence between unit root tests.

Keywords

Unit Root TestDSGE ModelsHours WorkedStructural VAR
Type
Articles
Information
Macroeconomic Dynamics , Volume 19 , Issue 1 , January 2015 , pp. 167 - 188
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

An, Sungbae and Schorfheide, Frank (2007) Bayesian analysis of DSGE models. Econometric Reviews 26, 113–172.CrossRefGoogle Scholar
Blanchard, Olivier J. and Quah, Danny (1989) The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79, 655–673.Google Scholar
Campbell, John Y. and Perron, Pierre (1991) Pitfalls and opportunities: What macroeconomists should know about unit roots. NBER Macroeconomics Annual 6, 141–201.CrossRefGoogle Scholar
Canova, Fabio and Sala, Luca (2009) Back to square one: Identification issues in DSGE models. Journal of Monetary Economics 56, 431–449.CrossRefGoogle Scholar
Chang, Yongsung, Doh, Taeyoung, and Schorfheide, Frank (2007) Non-stationary hours in a DSGE model. Journal of Money, Credit and Banking 39 (6), 1357–1373.CrossRefGoogle Scholar
Chari, V.V., Patrick, J. Kehoe and McGrattan, Ellen R. (2007) Are Structural VARs with Long-Run Restrictions Useful in Developing Business Cycle Theory? Staff report 364, Federal Reserve Bank of Minneapolis.CrossRefGoogle Scholar
Chari, V.V., Patrick, J. Kehoe and McGrattan, Ellen R. (2008) Are structural VARs with long-run restrictions useful in developing business cycle theory? Journal of Monetary Economics 55, 1337–1352.CrossRefGoogle Scholar
Christiano, Larry J. and Eichenbaum, Martin (1992) Current real business cycle theories and aggregate labor market fluctuations. American Economic Review 82, 430–450.Google Scholar
Christiano, Larry J., Eichenbaum, Martin, and Vigfusson, Robert (2004) What Happens after a Technology Shock? Working paper 9819, NBER.CrossRefGoogle Scholar
Cogley, Timothy and Nason, James M. (1995) Output dynamics in real-business-cycle models. American Economic Review 84, 492–511.Google Scholar
Cooley, Thomas F. and Dwyer, Mark (1998) Business cycle analysis without much theory: A look at structural VARs. Journal of Econometrics 83, 57–88.CrossRefGoogle Scholar
DeJong, David N., Nankervis, John C., Savin, N.E., and Whiteman, Charles H. (1992) The power problems of unit root tests in time series with autoregressive errors. Journal of Econometrics 53, 323–343.CrossRefGoogle Scholar
Dickey, David A. and Fuller, Wayne A. (1981) Likelihood ratio statistics for autoregressive time series with unit root. Econometrica 49, 1057–1072.CrossRefGoogle Scholar
Dupaigne, Martial, Fève, Patrick, and Matheron, Julien (2007) Some analytics on bias in DSVARs. Economics Letters 97, 32–38.CrossRefGoogle Scholar
Elliott, Graham, Rothenberg, Thomas J., and Stock, James H. (1996) Efficient tests for an autoregressive unit root. Econometrica 64, 813–836.CrossRefGoogle Scholar
Erceg, Christopher J., Guerrieri, Luca, and Gust, Christopher (2005) Can long-run restrictions identify technology shocks? Journal of the European Economic Association 3, 1237–1278.CrossRefGoogle Scholar
Faust, Jon and Leeper, Eric M. (1997) When do long-run identifying restrictions give reliable results? Journal of Business and Economic Statistics 15, 345–353.Google Scholar
Patrick, Fève and Guay, Alain (2009) The response of hours to a technology shock: A two-step structural VAR approach. Journal of Money, Credit and Banking 41, 987–1013.Google Scholar
Fout, Hamilton B. and Francis, Neville R. (in press) Imperfect transmission of technology shocks and the business cycle consequences. Macroeconomic Dynamics.Google Scholar
Francis, Neville and Ramey, Valerie A. (2005) Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited. Journal of Monetary Economics 52, 1379–1399.CrossRefGoogle Scholar
Francis, Neville and Ramey, Valerie A. (2009). Measures of per capita hours and their implications for the technology-hours debate. Journal of Money, Credit and Banking 41, 1071–1097.CrossRefGoogle Scholar
Galí, Jordi (1999) Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271.CrossRefGoogle Scholar
Galí, Jordi and Rabanal, Paul (2004) Technology Shocks and Aggregate Fluctuations: How Well Does the Real Business Cycle Model Fit Postwar U.S. Data? Working paper 10636, NBER.CrossRefGoogle Scholar
Gil-Alana, Luis and Moreno, Antonio (2009) Technology shocks and hours worked: A fractional integration perspective. Macroeconomic Dynamics 13 (5), 580–604.CrossRefGoogle Scholar
Gorodnichenko, Yuriy and Ng, Serena (2010) Estimation of DSGE models when the data are persistent. Journal of Monetary Economics 57, 325–340.CrossRefGoogle Scholar
Haldrup, Niels and Jansson, Michael (2006) Improving size and power in unit root testing. In Mills, Terence C. and Patterson, Kerry (eds.), Palgrave Handbook of Econometrics, Vol. 1: Econometric Theory, pp. 252–277. Basingstoke, Hants., UK: Palgrave Macmillan.Google Scholar
Hansen, Gary D. (1985) Indivisible labor and the business cycle. Journal of Monetary Economics 16, 309–327.CrossRefGoogle Scholar
Hansen, Gary D. (1997) Technological progress and aggregate fluctuations. Journal of Political Economy 16, 1005–1023.Google Scholar
King, Robert G., Plosser, Charles I., Stock, James H., and Watson, Mark W. (1991) Stochastic trends and economic fluctuations. American Economic Review 81, 819–940.Google Scholar
Kwiatkowski, Denis, Phillips, Peter C.B., Schmidt, Peter and Shin, Yongcheol (1992) Testing the null hypothesis of stationary against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics 54, 159–178.CrossRefGoogle Scholar
Kydland, Finn E. and Prescott, Edward C. (1982) Time to build and aggregate fluctuations. Econometrica 50, 1345–1370.CrossRefGoogle Scholar
Lindé, Jesper (2005) Estimating New-Keynesian Phillips curves: A full information maximum likelihood approach. Journal of Monetary Economics 52, 1135–1149.CrossRefGoogle Scholar
MacKinnon, James G. (1991) Critical values for cointegration tests. In Engle, Robert F. and Granger, Clive W. J. (eds.), Long-run Economic Relationships, Readings in Cointegration, pp. 267–276. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
Nelson, Charles R. and Charles I. Plosser (1982) Trends and random walks in macroeconomic time series. Journal of Monetary Economics 10, 139–162.CrossRefGoogle Scholar
Ng, Serena and Perron, Pierre (1995) Unit root tests in ARMA models with data-dependent methods for the selection of the truncation lag. Journal of the American Statistical Association 90, 268–281.CrossRefGoogle Scholar
Ng, Serena and Perron, Pierre (2001) Lag length selection and the construction of unit root tests with good size and power. Econometrica 69 (6), 1519–1554.CrossRefGoogle Scholar
Perron, Pierre and Ng, Serena (1996) Useful modifications to unit root tests with dependent errors and their local asymptotic properties. Review of Economic Studies 63, 435–465.CrossRefGoogle Scholar
Pesavento, Elena and Rossi, Barbara (2005) Do technology shocks drive hours up or down? A little evidence from an agnostic procedure. Macroeconomic Dynamics 9, 478–488.CrossRefGoogle Scholar
Pesavento, Elena and Rossi, Barbara (2006) Small-sample confidence intervals for multivariate impulse response functions at long horizons. Journal of Applied Econometrics 21, 1135–1155.CrossRefGoogle Scholar
Phillips, Peter C.B. and Perron, Pierre (1988) Testing for unit root in time series regression. Biometrika 75, 347–353.CrossRefGoogle Scholar
Prescott, Edward C. (1986) Theory ahead of business cycle measurement. Carnegie Rochester Conference Series on Public Policy 25, 11–44.CrossRefGoogle Scholar
Ravenna, Federico (2007) Vector autoregressions and reduced form representations of DSGE models. Journal of Monetary Economics 54, 2048–2064.CrossRefGoogle Scholar
Vougas, Dimitrios V. (2007) GLS detrending and unit root testing. Economics Letters 97, 222–229.CrossRefGoogle Scholar
Whelan, Karl T. (2009) Technology shocks and hours worked: Checking for robust conclusions. Journal of Macroeconomics 31, 231–239.CrossRefGoogle Scholar