The B.E. Journal of Macroeconomics
Editor-in-Chief: Abraham, Arpad / Cavalcanti, Tiago
Ed. by Carceles-Poveda , Eva / Debortoli, Davide / Kambourov, Gueorgui / Lambertini, Luisa / Pavoni, Nicola / Ruhl, Kim
2 Issues per year
IMPACT FACTOR increased in 2014: 0.389
5-year IMPACT FACTOR: 0.406
SCImago Journal Rank (SJR) 2014: 0.610
Source Normalized Impact per Paper (SNIP) 2014: 0.518
Impact per Publication (IPP) 2014: 0.419
Volume 14 (2014)
Volume 13 (2013)
Volume 12 (2012)
Volume 11 (2011)
Volume 10 (2010)
Volume 9 (2009)
Volume 8 (2008)
Volume 7 (2007)
Volume 5 (2005)
Volume 4 (2004)
Volume 3 (2003)
Volume 2 (2002)
Most Downloaded Articles
- Comparing Wealth Effects: The Stock Market versus the Housing Market by Case, Karl E./ Quigley, John M. and Shiller, Robert J.
- Monetary and Macroprudential Policy Rules in a Model with House Price Booms by Kannan, Prakash/ Rabanal, Pau and Scott, Alasdair M.
- Who Gets the Credit? And Does It Matter? Household vs. Firm Lending Across Countries by Beck, Thorsten/ Büyükkarabacak, Berrak/ Rioja, Felix K. and Valev, Neven T.
- The Effects of the Great Recession on Central Bank Doctrine and Practice by Bernanke, Ben S.
To Pool or to Aggregate? Tests with a Dynamic Panel Macroeconometric Model of Australian State Labor Markets
Citation Information: The B.E. Journal of Macroeconomics. Volume 7, Issue 1, ISSN (Online) 1935-1690, DOI: 10.2202/1935-1690.1365, January 2007
- Published Online:
We construct a dynamic error correction model of the Australian labor market using a macroeconomic panel across seven states from 1972:3 to 1999:1. Medium-run equilibrium estimates support a real wage-productivity gap and an unemployment gap. The dynamic short-run estimates support expectations-augmented Phillips curves for wages and prices, and demand-led employment growth. We compare three procedures pooled, aggregate and mean group estimates. Considerable heterogeneity existed across states in the pooled procedure, and state-level variables had a significant impact in the aggregate procedure. Out-of-sample aggregate forecasting for the pooled, aggregate and mean group procedures suggests that the pooled one performs best.