Jean Palate David de Antonio Liedo Licensed under the EUPL (http://ec.europa.eu/idabc/eupl). RESEARCH & DEVELOPMENT The last updated version of the software can be downloaded here STATISTICS (NBB) http://www.cros-portal.eu/content/jdemetra
LINK NKIN ING G TECHNO NOLOG OGY Y IN A RE REAL AL-TIM IME E FO FORE RECA CAST STING ING ENVIRO IRONMENT ENT Monitoring the macro economy in T echn chnology logy can can help … real time and detect etectin ing tur turning ning poin po ints ts requires certain skills and intuition Red Bull Racing Chief Technical Officer Adrian Newey Sebastian Vettel driving for Red Bull Racing in 2010. Source: Mark Thompson/Getty Images AsiaPac Photographer: Andrew Hoskins at British Grand Prix
LINK NKIN ING G TECHNO NOLOG OGY Y IN A RE REAL AL-TIM IME E FO FORE RECA CAST STING ING ENVIRO IRONMENT ENT Monitoring the macro economy in T echno chnology logy can can help … real time and detect etectin ing tur turning ning po poin ints ts requires certain skills and intuition EA12 GDP Chain linked volumes (2010), million euro 2,450,000 2,400,000 2,350,000 2,300,000 2011Q3 - Humans have limited ed 2,250,000 2,200,000 capaci acity ty to process 2,150,000 information and interprete it. 2,100,000 2,050,000 2,000,000 2000Q1 2000Q4 2001Q3 2002Q2 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1 2012Q4 2013Q3 2014Q2 - Confir nfirma matio tion bias s is pervasive in macroeconomic forecasting.
LINK NKIN ING G TECHNO NOLOG OGY Y IN A RE REAL AL-TIM IME E FO FORE RECA CAST STING ING ENVIRO IRONMENT ENT Monitoring the macro economy in T echno chnology logy can can help … real time and detect etectin ing tur turning ning po poin ints ts requires certain skills and intuition EA12 GDP Chain linked volumes (2010), million euro 2,450,000 2,400,000 2,350,000 2,300,000 2011Q3 - Humans have limited ed 2,250,000 2,200,000 capaci acity ty to process 2,150,000 information and interprete it. 2,100,000 2,050,000 2,000,000 2000Q1 2000Q4 2001Q3 2002Q2 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1 2012Q4 2013Q3 2014Q2 - Confir nfirma matio tion bias s is pervasive in macroeconomic forecasting.
THE FO FORE RECA CAST STING ING RA RACE Since January 2014, international organizations’ offici ficial l commun unica icatio tions ns have been in line with the widesp espread ead believe eve that the recession is over “The euro area is turning the corner from recession to recovery” 21 January 2014; World Economic Outlook “Recovery gaining ground“ 25 February 2014; Winter Forecasts “Euro area’s economic recovery gradually taking hold, albeit at a slow and uneven pace “ 28 February 2014; Draghi “Growth becoming broader - based” 5 May 2014; Sprint Forecasts “Economic activity is projected to continue to recover as confidence improves further” May 2014; “Euro Area” in OECD Economic Outlook, Volume 2014 Issue 1 , OECD Publishing However, some doubts start to appear “ Lack of evidence of sustained improvement of economic “The recovery is losing momentum “ activity “ 11 June 2014 ; EABC Dating Committee 22 September 2014; Draghi “The Demise of Wishful Thinkers“ (3 October 2014; Philippe Weil, Chair of the EABC Dating Committee; Conference in honor of André Sapir)
KEY QUESTIO STIONS Which models produce better forecasts? When? Are model (a) forecasts always better than those of model (b) or only under certain information assumptions?
KEY QUESTIO STIONS Which models produce better forecasts? When? Are model (a) forecasts always better than those of model (b) or only under certain information assumptions? Re-definition of the “forecast horizon” concept
A Real-Time Forecasting Evaluation Library Features Simulates forecasting scenarios using real-time data • availability (users can define the release calendar in a simple manner) Check whether a new model yields statistically significant • gains in forecasting accuracy with respect to alternatives Robust quantification of forecast accuracy as a function of • This is work in progress the information available (i.e. “ forecast horizon ”). Many measures of forecast accuracy and possibility to • perform analysis by subsamples You are the pilot C Getty Images Think about the most suitable forecasting model • Estimate the model and assess its in-sample fit • Before using your model out-of-sample , use • our “simulator” to become aware of the risks Photo: Urban Events
A Real-Time Forecasting Evaluation Library Features Simulates forecasting scenarios using real-time data • availability (users can define the release calendar in a simple manner) Check whether a new model yields statistically significant • gains in forecasting accuracy with respect to alternatives Robust quantification of forecast accuracy as a function of • This is work in progress the information available (i.e. “ forecast horizon ”). Many measures of forecast accuracy and possibility to • perform analysis by subsamples You are the pilot Think about the most suitable forecasting model • Estimate the model and assess its in-sample fit • Before using your model out-of-sample , use • our “simulator” to become aware of the risks ( α =5%) Good luck! •
A Real-Time Forecasting Evaluation Library 1. WHAT IS J DEMETRA ( J D) +
A Real-Time Forecasting Evaluation Library 1. WHAT IS J DEMETRA ( J D) + 2. FORECAST EVALUATION
A Real-Time Forecasting Evaluation Library 1. WHAT IS J DEMETRA ( J D) + 2. FORECAST EVALUATION AN EXAMPLE Defining the calendar Recursive estimation Real-Time simulation 4. NEXT STEPS
J DEMETRA+ is Pure Java software Mainly (>95%) based on libraries written by Research & Development (NBB) • Complete control • High-performance (compared to Matlab …) • No economic cost for the user: Open Access software licensed under the EUPL • (http://ec.europa.eu/idabc/eupl) It has been designed for extension (today you will see the proof) • J DEMETRA+ provides many useful services Primary goal remains seasonal adjustment (TRAMO-SEATS and X12). Externalities: temporal disaggregation (Chow-Lin, Fernandez, Litterman), benchmarking (Denton, Cholette ), Outliers detections, chain linking, etc… On-going: Multivariate models (SUTSE, DFM, BVAR) Dynamic access to different sources: Excel, Txt, SAS, Databases… Rich graphical components Storage of current work through workspace… Graphical interface based on NetBeans International Cooperation Maintenance partly ensured by the Bundesbank Support of the SA Center of Excellence (INSEE, ONS, ISTAT, EUROSTAT…)
Related Literature: Evaluating forecasts on the basis of pseudo out-of- sample exercises is standard practice. Tricky to have realistic simulations: Real-time Real-time data Some examples for euro area GDP (instead of revised) publication schedule Camacho M. and G. Pérez-Quirós (2010) YES YES De Antonio Liedo (2014) «Nowcasting Belgium» YES YES Angelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011) stylized NO Banbura and Modugno (2014) stylized NO Kuzin, Marcelino and Schumacher (2011) stylized NO
Related Literature: Evaluating forecasts on the basis of pseudo out-of- sample exercises is standard practice. Tricky to have realistic simulations: Real-time Real-time data Some examples for euro area GDP (instead of revised) publication schedule Camacho M. and G. Pérez-Quirós (2010) YES YES De Antonio Liedo (2014) «Nowcasting Belgium» YES YES Angelini, Camba-Mendez, Giannone, Reichlin and Rünstler (2011) stylized NO Banbura and Modugno (2014) stylized NO Kuzin, Marcelino and Schumacher (2011) stylized NO We propose an efficient framework to simulate the publication calendar, and to some limited extent, the real-time data. Forecast accuracy testing and visualization
Example: How to perform a real-time forecasting simulation ?
1) Just introduce the publication delay for each series ... 2) Decide when to update your forecasts (e.g. in this example, the days when GDP flash, employment and industrial production are released)
3) next, specify your model: SUTSE, DFM, BVAR … In this example, a dynamic factor model in state-space form à la Banbura and Modugno (2014) or Camacho and Pérez-Quirós (2010) β t α t 𝑞 𝑞 1 1 T T + u β,t β t−𝑞 β t T T β t−1 11 12 11 12 u α,t = + ⋯ + State Equation α t−𝑞 α t α t−1 𝑞 𝑞 1 1 T 21 T 22 T 21 T 22 Measurement = Z α t − Λ β t y t + ξ t = Λ π β t π π t + ξ t Equation 7 × 1 7 × 1 1 × 1 7 × 1 1 × 1 7 × 1 6 × 1 6 × 1 1 × 1 6 × 1 Charles, Maggi, Palate and De Antonio (2015)
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