Mariana Rizk 6th Eurostat Colloquium on Modern Tools for Business - PowerPoint PPT Presentation

Mariana Rizk 6th Eurostat Colloquium on Modern Tools for Business Cycle Analysis: The Lessons from Global Economic Crisis Luxembourg 2010

� Egypt’s economic growth 4% over FY2002/03-FY2004/05 � 7% over FY2005/06- ◦ FY2007/08 manufacturing, construction, trade, communications and ◦ transportation sectors; Suez Canal and tourism sectors � In FY2008/09 and the first half of FY2009/10 with financial crisis, the growth rate plunged to 4.7% slowdown in the domestic economy and external sectors ◦ � National accounts of Egypt compiled in annual and quarterly frequencies annual GDP data start in 1980’s ◦ quarterly GDP data is published since FY 2001/02 ◦ need for a higher-frequency measure ◦ � use of temporal disaggregation to transform the quarterly series into a monthly series

� Two strands: 1. Purely mathematical or time series model � smoothing technique of Boot, Feibes and Lisman (1967) � ARIMA model of Wei and Stram (1990) 2. Methods that make use of related indicators observed at the desired high frequency � static regression-based method of Chow and Lin (1971) and its variants by Fernández (1981) and Litterman (1983) � include a dynamic component such as Salazar et al. (1997), Santos Silva and Cardoso (2001), Di Fonzo (2003) and Proietti (2006)

� Estimation of quarterly GDP from annual data Developed Countries: ◦ � Quillis (2005): Spain � Chen (2007): USA � Hall and McDermott (2007): New Zealand Developing Countries: ◦ � Abeysinghe and Rajaguru (2004): China and the ASEAN4 countries (Indonesia, Malaysia, Philippines and Thailand) using the Chow-Lin methodology or one of its variants � Estimation of monthly GDP from quarterly data Bruno et al. (2005): United States, Japan, Germany, ◦ France, United Kingdom, Italy, Canada and the Euro area, by disaggregating the quarterly GDP out-of-sample forecasting procedure ◦

� Ismail et al. (2006) annual nominal GDP over 1982-2005 � quarterly ◦ quarterly indicators: Suez Canal receipts, Brent petroleum ◦ price, nominal exports and imports, M1 stock, nominal exchange rate, discount interest rate, and a trend variable Chow-Lin estimates are superior to Fernandez and ◦ Litterman based on RMSE and mean absolute deviations � Moursi et al. (2006) annual GDP over 1981-2005 � monthly real GDP series ◦ Litterman (1983) methodology ◦ monthly indicators: Brent petroleum price, real exports ◦ and imports, real Suez Canal receipts, real M1 stock, real quasi-money and the real exchange rate deflated the nominal indicators using the wholesale price ◦ index (WPI)

� Quarterly real GDP � monthly real GDP � Set of related series is chosen with some considerations: 1. monthly indicators are compiled initially in real terms to avoid any intermediate estimation errors in deflating the indicators WPI series is not published since 2007 and the CPI � series has computational breaks. 2. indicators represent the economic activity in the most dynamic sectors of the economy, such as industry, mining, tourism, Suez Canal 3. the sample includes the recent global crisis period

Methodology Methodology � Chow and Lin (1971) adopted a hypothetical relationship between the unobserved target monthly series and one or more observed monthly indicators = β + = β + y X u y X u m m m q q q � Applying GLS estimation to the quarterly regression, obtain a BLUE solution for the coefficient vector [ ] − 1 ˆ − − 1 1 ′ ′ ′ ′ β = ( ) ( ) X CV C X X CV C y GLS q m q q m q � The key assumption of Chow and Lin is that u m follows a stationary AR(1) process without drift: = ρ + ε u u ρ < 1 − 1 t t t

u Methodology Methodology m � Therefore, Chow-Lin estimates are reliable only if is stationary or ◦ u m GDP and related indicators are cointegrated ◦ � The most commonly used approach to test this condition is to test the stationarity and cointegration in the low frequency (Abeysinghe and Rajaguru in 2004 and Bruno et al in 2005)

Data Data � Quarterly GDP data over 2001:Q3-2010:Q1 � 16 monthly indicators in different sectors of the economy Energy (Production & Consumption): Oil and natural gas ◦ consumption, Oil extractions, petroleum products and NG production, Electric energy production, Electric energy consumption, Electric consumption of the industrial sector, Electric consumption for household and commercial uses Transportation/Internal Trade: No. of railway ◦ passengers, Railway passengers times distance travelled, Railway cargo, Railway cargo times distance travelled Suez Canal: Total net cargo of Suez Canal, Net cargo of ◦ Oil ships, Net cargo of Non-oil ships Tourism: Tourist nights ◦ Financial Transactions: Value of messages executed via ◦ SWIFT in domestic transfers, Value of Automated Clearing House cheques

Oil and natural gas consumption Oil extractions, petroleum products and natural gas production Electric energy production 17000 22000 4.00E+07 21000 16000 3.60E+07 20000 15000 19000 3.20E+07 14000 18000 2.80E+07 17000 13000 16000 2.40E+07 12000 15000 2.00E+07 11000 14000 10000 13000 1.60E+07 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 Electric energy consumption Electric consumption of the industrial sector Electric consumption for household and commercial uses 3.20E+07 10000000 1.40E+07 1.30E+07 9000000 2.80E+07 1.20E+07 1.10E+07 8000000 2.40E+07 1.00E+07 7000000 9.00E+06 2.00E+07 8.00E+06 6000000 7.00E+06 1.60E+07 5000000 6.00E+06 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 No. of railway passengers Railway passengers times distance trav elled Railway cargo 130000 1.60E+07 3500 120000 1.40E+07 3000 110000 100000 1.20E+07 2500 90000 1.00E+07 2000 80000 70000 8.00E+06 1500 60000 50000 6.00E+06 1000 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 Railway cargo times distance travelled Total net cargo of Suez Canal Net oil cargo of Suez Canal 1300000 260000 44000 1200000 240000 40000 1100000 220000 1000000 36000 200000 900000 180000 32000 800000 160000 700000 28000 140000 600000 24000 120000 500000 400000 100000 20000 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 Net non-oil cargo of Suez Canal Tourist nights Value of messages executed via SWIFT in domestic transfers 220000 40000 4.0E+09 36000 200000 32000 3.0E+09 180000 28000 160000 24000 2.0E+09 20000 140000 16000 120000 1.0E+09 12000 100000 8000 0.0E+00 80000 4000 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 Value of Automated Clearing House Cheques 1.60E+08 1.40E+08 1.20E+08 1.00E+08 •Structural breaks/vulnerability to shocks 8.00E+07 6.00E+07 •Coverage of the variable 4.00E+07 2002 2003 2004 2005 2006 2007 2008 2009

ADF t-Stat (AIC) DF-GLS test statistic (MAIC) First GLS First Detrended difference detrended difference series has has a unit series has a has a unit Null Hypothesis: a unit root root unit root root Energy (Production & Consumption) Oil and natural gas consumption -2.04 -2.30 -1.38 -6.43*** Oil extractions, petroleum products and NG production -2.52 -4.74*** -2.05 -4.74*** Electric energy production -1.17 -2.66* 0.04 -0.04 Electric energy consumption -2.99 -2.95** -0.16 -0.04 Electric consumption of the industrial sector -3.49* -3.63*** -0.85 -0.47 Electric consumption for household and commercial uses 0.48 -1.26 -0.17 0.35 Transportation/Internal Trade No. of railway passengers -0.53 -7.67*** -0.93 -0.72 Railway passengers times distance travelled -0.68 -6.86*** -0.77 -0.21 Railway cargo -2.26 -4.51*** -1.88 -5.07*** Railway cargo times distance travelled -2.58 -4.53*** -1.65 -4.18*** Suez Canal Total net cargo of Suez Canal -2.74 -2.83* -1.26 -0.86 Net cargo of Oil ships 0.93 -5.63*** -1.29 -0.94 - Net cargo of Non-oil ships 4.43*** -3.83*** -0.18 -0.44 Tourism Tourist nights -3.18 -3.70*** -1.57 -0.81 Financial Transactions Value of messages executed via SWIFT in domestic transfers 2.28 0.98 0.01 -1.68* Value of Automated Clearing House cheques -2.29 -5.42*** -1.03 0.18 Real GDP -2.65 -1.26 -1.08 0.63

Temporal Disaggregation Temporal Disaggregation � Two stages: Estimation and evaluation of simple statistics of the low- ◦ frequency GLS regression: 2001:Q3-2007:Q4 Out-of-sample forecasting to judge the predictive power ◦ of the indicator choices (Bruno et al, 2005): 2008:Q1- 2010:Q1 � The quality of the forecast will be assessed in two ways: the estimated disaggregation is used to extrapolate the ◦ whole forecast period without changing the low- frequency estimation period the estimated disaggregation is used to extrapolate three ◦ months ahead of GDP, rolling through the period 2008:M1-2010:M3 � The extrapolated values of monthly GDP are aggregated and compared to the observed value of real GDP using the root mean squared errors (RMSE)