Six Leading Indexes Of New Zealand Employment
This paper constructs six leading indexes of New Zealand employment and compares their short term forecasting performance. Forecasting New Zealand employment is particularly difficult owing to the volatility of the data and the short sample size of available time series. These restrictions make leading indexes especially appealing. The paper has two aims. The first is to construct an effective forecasting tool. The second is to evaluate leading indexes constructed using different methods available in the literature. The results show that an index constructed using the traditional NBER method dominates in terms of forecasting performance. The results also suggest that increasing the dataset does not strengthen the index and that exogenously determining the weights of component series can add to forecasting performance.
|Date of creation:||Sep 2007|
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- Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
- Edda Claus, "undated". "Constructing NEO: A Near-term Employment Outlook," Working Papers-Department of Finance Canada 2001-07, Department of Finance Canada.
- Edda Claus & Iris Claus, 2002. "How many jobs? A leading indicator model of New Zealand employment," Treasury Working Paper Series 02/13, New Zealand Treasury.
- Harding, Don & Pagan, Adrian, 2002.
"Dissecting the cycle: a methodological investigation,"
Journal of Monetary Economics,
Elsevier, vol. 49(2), pages 365-381, March.
- Don Harding & Adrian Pagan, 2000. "Disecting the Cycle: A Methodological Investigation," Econometric Society World Congress 2000 Contributed Papers 1164, Econometric Society.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. Full references (including those not matched with items on IDEAS)
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