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An analysis of the informational content of New Zealand data releases: the importance of business opinion surveys

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Author Info
Troy Matheson (Reserve Bank of New Zealand)

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Abstract

We examine the informational content of New Zealand data releases using a parametric dynamic factor model estimated with unbalanced real-time panels of quarterly data. The data are categorised into 21 different release blocks, allowing us to make 21 different factor model forecasts each quarter. We compare three of these factor model forecasts for real GDP growth, CPI inflation, non-tradable CPI inflation, and tradable CPI inflation with real-time forecasts made by the Reserve Bank of New Zealand each quarter. We find that, at some horizons, the factor model produce forecasts of similar accuracy to the Reserve Bank’s forecasts. Analysing the marginal value of each of the data releases reveals the importance of the business opinion survey data – the Quarterly Survey of Business Opinion and the National Bank’s Business Outlook survey – in determining how factor model predictions, and the uncertainty around those predictions, evolves through each quarter.

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Publisher Info
Paper provided by Reserve Bank of New Zealand in its series Reserve Bank of New Zealand Discussion Paper Series with number DP2007/13.

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Length: 50 p.
Date of creation: Sep 2007
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Handle: RePEc:nzb:nzbdps:2007/13

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Find related papers by JEL classification:
E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  1. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May. [Downloadable!] (restricted)
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  2. Andrew Ang & Geert Bekaert & Min Wei, 2006. "Do macro variables, asset markets, or surveys forecast inflation better?," Finance and Economics Discussion Series 2006-15, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  3. David H. Small & Domenico Giannone & Lucrezia Reichlin, 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank. [Downloadable!]
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  4. Lloyd B. Thomas Jr., 1999. "Survey Measures of Expected U.S. Inflation," Journal of Economic Perspectives, American Economic Association, vol. 13(4), pages 125-144, Fall. [Downloadable!] (restricted)
  5. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January. [Downloadable!] (restricted)
  6. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  7. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  8. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
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