LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Dimitrios Bakas; Theodore Panagiotidis; Gianluigi Pelloni (2013)
Publisher: The Rimini Centre for Economic Analysis
Types: Book
Subjects: Unemployment, Sectoral Shifts, Employment Fluctuations, Dynamic Panel Data, Parameter Heterogeneity, Cross-Sectional Dependence
jel: jel:E32, jel:C33, jel:R23, jel:E24, jel:J21
This paper re-examines Lilien’s sectoral shifts hypothesis for U.S. unemployment. We employ a monthly panel that spans from 1990:01 to 2011:12 for 48 U.S. states. Panel unit root tests that allow for cross-sectional dependence reveal the stationarity of unemployment. Within a framework that takes into account dynamics, parameter heterogeneity and cross-sectional dependence in the panel, we show that sectoral reallocation is significant not only at the aggregate level but also at the state level. The magnitude and the statistical significance of the latter as measured by Lilien’s index increases when both heterogeneity and cross-sectional dependence are taken into account.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. For an extensive survey see Gallipoli and Pelloni (2008).
    • 2. In the wake of Long and Plosser (1987) and Blanchard and Katz (1992) much attention in this field has been paid to multivariate settings such as the VAR of Campbell and Kuttner (1996) or VAR-GARCH-M of Pelloni and Polasek (1999; 2003) or the semiparametric spatial auto-regressive set up of Basile et al. (2012).
    • 4. There is wide variation across papers in the choice of variables included in z˜t . Here, the vector of aggregate variables z˜t is exactly the same as the vector zt . For a full discussion of the issue see Gallipoli and Pelloni (2008).
    • 5. In older days this decomposition would have been introduced to capture the money surprises of Lucas misperception model (Lucas, 1972; 1973).
    • 6. Caporale et al. (1996) follow a similar approach.
    • 7. Chudik and Pesaran (2013) provide evidence on the estimation of heterogeneous panel data models with lagged dependent variable and show that the CCEMG estimator continues to be valid asymptotically when dealing with dynamics.
    • 8. Bond and Eberhardt (2009) provide evidence that both the CCEMG estimators and the AMG approach perform very well and with similar results in recent Monte Carlo studies.
    • 9. We exclude from our analysis the no-adjoining states of Alaska and Hawaii.
    • 10. All sectoral series were seasonally adjusted using Eviews Census X12 program.
    • Abraham, K. G. and L. F. Katz (1986), “Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances?” Journal of Political Economy, 94, 507-522. (p. 3, 8)
    • Arellano, M. and S. Bond (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, 58, 277-97. (p. 3, 4, 9, 19)
    • Arellano, M. and O. Bover (1995), “Another Look at the Instrumental Variable Estimation of ErrorComponents Models,” Journal of Econometrics, 68, 29-51. (p. 4)
    • Baltagi, B. (2008), Econometric Analysis of Panel Data, Chichester, UK: John Wiley & Sons. (p. 5)
    • Baltagi, B. H., G. Bresson, and A. Pirotte (2007), “Panel Unit Root Tests and Spatial Dependence,” Journal of Applied Econometrics, 22, 339-360. (p. 5, 6)
    • Banerjee, A., M. Marcellino, and C. Osbat (2004), “Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data,” Econometrics Journal, 7, 322-340. (p. 6)
    • Basile, R., A. Girardi, M. Mantuano, and F. Pastore (2012), “Sectoral Shifts, Diversification and Regional Unemployment: Evidence from Local Labour Systems in Italy,” Empirica, 39, 525-544. (p. 11)
    • Blanchard, O. J. and L. F. Katz (1992), “Regional Evolutions,” Brookings Papers on Economic Activity, 23, 1-76. (p. 11)
    • Blundell, R. and S. Bond (1998), “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, 87, 115-143. (p. 4, 9, 19)
    • Bond, S. and M. Eberhardt (2009), “Cross-Section Dependence in Nonstationary Panel Models: a Novel Estimator,” MPRA Paper 17692, University Library of Munich, Germany. (p. 1, 4, 11, 19, 19)
    • Campbell, J. R. and K. N. Kuttner (1996), “Macroeconomic Effects of Employment Reallocation,” Carnegie-Rochester Conference Series on Public Policy, 44, 87-116. (p. 11)
    • Caporale, T., K. Doroodian, and M. R. M. Abeyratne (1996), “Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances? A Vector Autoregression Approach,” Applied Economics Letters, 3, 127-130. (p. 11)
    • Chow, G. C. (1960), “Tests of Equality Between Sets of Coefficients in Two Linear Regressions,” Econometrica, 28, 591-605. (p. 5)
    • Chudik, A. and M. H. Pesaran (2013), “Common Correlated Effects Estimation of Heterogenous Dynamic Panel Data Models with Weakly Exogenous Regressors,” Globalization and Monetary Policy Institute Working Paper 146, Federal Reserve Bank of Dallas. (p. 11)
    • De Serres, A., S. Scarpetta, and C. De La Maisonneuve (2002), “Sectoral Shifts in Europe and the United States: How They Affect Aggregate Labour Shares and the Properties of Wage Equations,” OECD Economics Department Working Papers 326, OECD Publishing. (p. 2)
    • Driscoll, J. C. and A. C. Kraay (1998), “Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data,” The Review of Economics and Statistics, 80, 549-560. (p. 1, 1, 3, 3, 9, 9, 9, 19, 19)
    • Eberhardt, M. and F. Teal (2010), “Productivity Analysis in Global Manufacturing Production,” Economics Series Working Papers 515, University of Oxford, Department of Economics. (p. 1, 4)
    • Fuerst, T. S. (1992), “Liquidity, Loanable Funds, and Real Activity,” Journal of Monetary Economics, 29, 3-24. (p. 3)
    • Gallipoli, G. and G. Pelloni (2008), “Aggregate Shocks vs Reallocation Shocks: an Appraisal of the Applied Literature,” Working Paper Series 27-08, Rimini Centre for Economic Analysis. (p. 3, 11, 11)
    • Hansen, L. P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, 50, 1029-54. (p. 4)
    • Im, K. S., M. H. Pesaran, and Y. Shin (2003), “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, 115, 53-74. (p. 5, 6, 7)
    • Keane, M. P. (1991), “Sectoral Shift Theories of Unemployment: Evidence from Panel Data,” Institute for empirical macroeconomics discussion paper, no. 28, Federal Reserve Bank of Minneapolis. (p. 2)
    • Keane, M. P. and E. S. Prasad (1996), “The Employment and Wage Effects of Oil Price Changes: A Sectoral Analysis,” The Review of Economics and Statistics, 78, 389-400. (p. 2)
    • Kiviet, J. F. (1995), “On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models,” Journal of Econometrics, 68, 53-78. (p. 3)
    • Lilien, D. M. (1982), “Sectoral Shifts and Cyclical Unemployment,” Journal of Political Economy, 90, 777-793. (p. 1, 2, 3, 3, 3, 4, 4, 8, 8, 8, 8, 8, 9, 10)
    • Long, J. B. and C. I. Plosser (1987), “Sectoral vs. Aggregate Shocks in the Business Cycle,” American Economic Review, 77, 333-36. (p. 11)
    • Lucas, R. E. (1972), “Expectations and the Neutrality of Money,” Journal of Economic Theory, 4, 103-124. (p. 11)
    • Lucas, R. E. (1973), “Wage Inflation and the Structure of Regional Unemployment: Comment,” Journal of Money, Credit and Banking, 5, 382-84. (p. 11)
    • Lucas, R. E. (1990), “Liquidity and Interest Rates,” Journal of Economic Theory, 50, 237-264. (p. 3)
    • Medoff, M. H. (1984), “Employment Risk, Diversification, and Unemployment,” The Quarterly Journal of Economics, 106, 1341-65. (p. 1, 1, 1, 2)
    • Neumann, G. R. and R. H. Topel (1991), “Employment Risk, Diversification, and Unemployment,” The Quarterly Journal of Economics, 106, 1341-65. (p. 1, 1, 1, 2, 2)
    • Nickell, S. J. (1981), “Biases in Dynamic Models with Fixed Effects,” Econometrica, 49, 1417-26. (p. 3)
    • O'Connell, P. G. J. (1998), “The Overvaluation of Purchasing Power Parity,” Journal of International Economics, 44, 1-19. (p. 6)
    • Pelloni, G. and W. Polasek (1999), “Intersectoral Labour Reallocation and Employment Volatility: A Bayesian Analysis using a VAR-GARCH-M model,” Discussion Papers 99/4, Department of Economics, University of York. (p. 11)
    • Pelloni, G. and W. Polasek (2003), “Macroeconomic Effects of Sectoral Shocks in Germany, The U.K. and, The U.S. A VAR-GARCH-M Approach,” Computational Economics, 21, 65-85. (p. 7, 11)
    • Pesaran, M. H. (2004), “General Diagnostic Tests for Cross Section Dependence in Panels,” IZA Discussion Papers 1240, Institute for the Study of Labor (IZA). (p. 5)
    • Pesaran, M. H. (2006), “Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure,” Econometrica, 74, 967-1012. (p. 1, 4, 8, 9, 19, 19, 19, 19)
    • Pesaran, M. H. (2007), “A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence,” Journal of Applied Econometrics, 22, 265-312. (p. 5, 6, 6, 7)
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article