Automated Short-Run EconomicForecasts(ASEF).ppt

29 March 2006 Nicolas Stoffels Automated Short-Run Economic Forecasts (ASEF) Bank of Canada October 25-26, 2007 Nicolas Stoffels nicolas.stoffels@snb.ch Introduction Motivation Global forecasts at the SNB ASEF Framework Application to U.S. GDP growth Summary Motivation ??…policymakers are well advised to follow two principles familiar to navigators throughout the ages: First, determine your position frequently. Second, use as many guides or landmarks as are available.?? B. Bernanke, March 2006 Releases of the official value of GDP and other national accounts data are published late ? Global economic developments have to be assessed from numerous and more timely indicators Incorporate higher-freqency indicators into statistical, quantitative forecasting models? e.g. Pain and Sédillot (2003, 2005), Ingenito and Trehan (1996) … Two main Goals: 1. Exploit the considerable amount of conjunctural information available before release of official national accounts data2. Build an automated process for an efficient day-to-day use ? SNB Project: Automated procedure to select and run optimal, indicator models for short-term forecasting of the international economy SNB’s global forecasts cycle Lexicon Hard indicators:Quantitative data (e.g. industrial production, retail sales …) Soft indicators:Qualitative data (e.g. Business / consumer surveys…) Main-Bridge equation:Forecast equation for the target variable (quarterly frequency) Auxiliary variables:High-frequency indicators that help to forecast regressors in the Main-Bridge equation Mini-Bridge equation:Forecast equation for monthly indicators used as regressors in the Main-Bridge equation Structure ASEF Stage 1: Indicator selection Finds the ?dominant‘ predictors Ranks high-frequency indicators according to their in-sample fit Tests for potentially useful combination of forecasts (encompassing). Modified Diebold-Mariano t-test. ASEF Stage 1: Indicator selection Estimation of K bivariate distribu