GDP FORECASTING WORKSHOP OCTOBER 2016 GDP FORECASTING PROCESS

Transcription

1 GDP FORECASTING WORKSHOP OCTOBER 2016 GDP FORECASTING PROCESS 19 October 2016

2 Outline Forecasting Process Forecasting GDP Calendar Year Indicator & Data Source Forecasting GDP Fiscal Year Issues faced during forecasting

3 FORECASTING PROCESS The Macroeconomic Committee is responsible for forecasting GDP. TheMacroeconomicCommitteeismadeupofseniorrepresentatives from the o Ministry of Economy (including Department of Strategic Planning Office, National Development & Statistics and Fiji Bureau of Statistics) o Ministry of Industry & Trade, o Ministry of Infrastructure & Transport, o Office of the Prime Minister, o Investment Fiji, o Fiji Revenue & Customs Authority and o The Reserve Bank of Fiji.

4 FORECASTING PROCESS Macroeconomic Committee (MC) Chaired by the Governor of RBF Approves the forecasts (GDP, Trade and BoP). Macroeconomic Technical Committee (MTC) Chaired by Deputy Secretary responsible for Strategic Planning, National Development & Statistics. Provides technical input towards the macroeconomic projections. A sub-technical committee consisting of members from RBF and Department of SPNDS collect data and prepare the forecasts that are presented to the MTC for endorsement before it goes to the MC.

5 Outline Forecasting Process Forecasting GDP Calendar Year Indicator & Data Source Forecasting GDP Fiscal Year Issues faced during forecasting

6 FORECASTING GDP CALENDAR YEAR Value Added Approach is used to forecast GDP and Forecasting is done bi- annually. The sub-technical team go through each line of the 21 sectors (under the 2011 base) to come up with the sector forecast. The latest GDP release by FBOS is used as the base for forecasting. E.g provisional GDP released by FBOS is used as the base for forecasting in R2, 2016 and R1, Forecast data from industry stakeholders is obtained which is used to grow the value addition (VA). o VA = Gross Output(GO) Intermediate Cost(IC). In most cases, due to the absence of IC data it is difficult to calculate VA. o Therefore, growth rates are used to grow previous years VA to get current years VA. The sub-technical team discuss and agree on the growth rate to be used as input in the final GDP worksheet; taking into account other information/developments. Add the lines up to get sector VA and sectors are added to get prices. the overall constant

7 Outline Forecasting Process Forecasting GDP Calendar Year Indicator & Data Source Forecasting GDP Fiscal Year Issues faced during forecasting

8 INDICATORS AND DATA SOURCE Sector Indicators Used Sources of Data Agriculture Production forecast for cane & non-cane crops and livestock Ministry of Agriculture, Fiji Sugar Corporation Forestry & Logging Fishing & Aquaculture Mining & Quarrying Manufacturing Native & exotic (pine/mahogany logging; nonwood forest products e.g masi, voivoi Marine Fishing on commercial basis includes tuna (yellow fin, big eye and albacore) and bycatch, freshwater fishing, beche-de-mer, aquaculture (marine & freshwater) Production forecast data for gold & bauxite and quarrying activities Other proxies used: Industrial Production Index Forecast Production data for food & beverages and non-food products Other proxies used: Industrial Production Index Department of Forestry, Tropik Wood Industries Limited & Fiji Hardwood Corporation Limited Department of Fisheries, Major Fishing Companies Vatukoula Gold Mines Limited, Xinfa Aurum Exploration (Fiji) Limited. Fiji Bureau of Statistics; Major Manufacturing Companies including Food & beverage manufacturing, garment, hardware, furniture, etc

9 INDICATORS AND DATA SOURCE Sector Indicators Used Sources of Data Electricity, gas, steam and air Forecast data for electricity production Fiji Electricity Authority conditioning supply Water supply, sewerage, Forecast data for water production waste management and remediation activities Water Authority of Fiji Construction Wholesale & Retail Value of work put-in-place Major construction projects Other proxies used: building permits issued; lending to real estate & construction sectors; domestic cement sales Sale of motor vehicles & motor vehicle parts. Forecast data for wholesaling and retailing activities. Other proxies used: new & second hand vehicle registration/sales; Net VAT; Wholesale & Retail Trade Survey (FBOS). Fiji Bureau of Statistics; Investment Fiji (projects registered & implemented); Town & Municipal Councils (permits issued); Pacific Cement & Tengy Cement Company Ltd. Fiji Bureau of Statistics (Quarterly Wholesale & Retail Trade Survey); Reserve Bank of Fiji (Retail Sales Survey)

10 INDICATORS AND DATA SOURCE Sector Indicators Used Sources of Data Transport & Storage Land Transport forecast stock of buses, taxis, rental & hire cars and heavy goods vehicles Accommodation & Food Services Air Transport forecast data for revenue, expenditure including intermediate costs, no. of passengers, revenue kilometre performed Water Transport inter island cargo landed & shipped; no. of passengers transported inter island within Fiji Warehousing & storage activities, cargo loaded & offloaded by air & sea; postal & courier activities. Room night sold; visitor arrivals; forecast data for beverage & food serving activities (restaurants) Land Transport Authority; Fiji Airways & Fiji Link; Maritime Safety Authority of Fiji; Air Terminal Services, Fiji Ports Corporation Ltd; Post Fiji; Major Courier Companies Fiji Bureau of Statistics; Retail Sales Survey; industry liaison with major tourism service providers including hotels & resorts and Fiji Airways

11 INDICATORS AND DATA SOURCE Sector Indicators Used Sources of Data Information & No of newspapers published; Fiji Times and Fiji Sun; Communication Television programming & broadcasting activities Fiji Directories Ltd; (revenue & expenditure); Radio broadcasting activities; Fiji TV; Fiji Broadcasting Commission; Wired & wireless telecommunication activities FINTEL; (no. of registered users/revenue & expenditure); TFL; and and Vodafone & Digicel Data processing activities (broadband data usage) Financial & Insurance Activities Central Banking activities (staff cost, capital formation less (amortisation & depreciation)); Commercial banking activities (Gross output, Intermediate costs); Pension Funding activities (FNPF Financials); and Life & non-life insurance activities Reserve Bank (Accounting Unit & Financial Institutions Group); FNPF; Insurance Annual Reports; and Major Insurance Companies

12 INDICATORS AND DATA SOURCE Sector Indicators Used Sources of Data Real Estate Activities Real estate activities with owned or leased Real Estate Licensing Board; properties & real estate activities on a fee or Major Real Estate Companies; contract basis (total sales turnover) Valuations Office Other proxies used: Real Estate Survey (RBF); lending to real estate sector Other Government Related Sectors including Public Administration & defence; health; education Segs 1 & 2 (wages & salaries) from Budget Estimates; No. of teachers, No. of student enrolments, No. of health workers (including doctors, nurses & caregivers) Ministry of Finance; Ministry of Education, Ministry of Health

13 Forecasting Cane & Sugar Scenario 1: FSC Direct Forecast Sugar & Cane EXAMPLE Scenario 2: SUB MTC FORECAST Sugar & Cane DIRECT FSC FORECASTS Cane Sugar TCTS Cane % Change Sugar % Change ,778, , ,095, , ,546, , ,610, , ,832, , (f) 1,844, , (f) 1,471, , (f) 2,000, , (f) 2,500, , SUB MTC FORECASTS Cane Sugar TCTS Cane % Change Sugar % Change ,778, , ,095, , ,546, , ,610, , ,832, , (f) 1,844, , (f) 1,371, , (f) 1,782, , (f) 2,138, Major Assumptions: 2016: The decline in cane and sugar production is based on revised FSC data. The sugar industry s production has been significantly affected this year by the cyclone and severe dry weather conditions prior to the cyclone. 2017: Cane production is expected to note a turnaround in For the 2017 season, FSC expects cane production to be around 2 million tonnes, however, the Sub-Technical Committee took a more conservative estimate of around 1.78 million tonnes.

14 Forecasting Cane & Sugar GDP Sheet EXAMPLE Constant Basic Price Values ($000) Percent Activity Base Weight As at 19/05/ (e) As at 19/05/ (f) As at 19/05/ (f) As at 19/05/ (f) As at 19/05/ (e) As at 19/05/ (f) As at 19/05/ (f) As at 19/05/ (f) AGRICULTURE , , , , , , SUBSISTENCE AGRICULTURE , , , , , , INFORMAL AGRICULTURE , , , , ,362 38, GENERAL GOVERNMENT 0.1 8, , , , ,355 10, FORMAL NON-GOVERNMENT AGRICULTURE , , , , , , Growing of rice 0.1 2, , Growing of taro , , Growing of cassava , , Growing of yam 0.1 3, , Growing of Kumala 0.1 4, , Growing of vegetables and melons, roots and tubers not elsewhere specified , , Growing of sugar cane , , Growing of bananas 0.1 3, , Growing of oleaginous fruits , , MANUFACTURING , , , , , , INFORMAL MANUFACTURING , , , , , , GENERAL GOVERNMENT 0.1 2, , , , ,939 2, FORMAL NON-GOVERNMENT MANUFACTURING , , , , , , Manufacture of food and beverages , , , , , , Manufacture of sugar , , , , , , Manufacture of cocoa, chocolate and sugar confectionery 0.2 7, , , , , ,

15 TRANS WH & RETAIL CONS ACCOM PUB ADMIN FINANCIAL EDU INFO & COM PROF HEALTH MINING FISH REAL EST ADMIN OTHER SER ARTS WATER FORESTRY ELECT MANU AGRI % Forecasting GDP an Example 2016 CONTRIBUTION TO GROWTH +2.4% Manufacture of sugar Production of sugarcane Source: Macroeconomic Technical Committee

16 MANU FINANCIAL TRANS FORESTRY WATER ARTS HEALTH WH & RETAIL CONS ACCOM AGRI INFO & COM PUB ADMIN EDU ELECT PROF ADMIN REAL EST MINING OTHER SER FISH % Manufacture of sugar FORECASTING GDP AN EXAMPLE 2017 CONTRIBUTION TO GROWTH +3.6% Production of sugarcane Source: Macroeconomic Technical Committee

17 Outline Forecasting Process Forecasting GDP Calendar year Indicator & Data Source Forecasting GDP Fiscal Year Issues Faced During Forecasting

18 FORECASTING GDP FISCAL YEAR With the Government s adoption of the August-July fiscal year from this year, GDP forecasts are also required to be presented in fiscal year terms, so as to be used in the National Budget process. The Forecasting team used a statistical method to redistribute the Calendar year GDP into fiscal year terms. For sectors that use indicators with a clear seasonal trend (such as visitor arrivals, manufacturing, cane & sugar production, construction and wholesale & retail trade), past 3-year trend in the indicator was used to redistribute for the actual years and the same weight was applied for the forecasting years some examples in next few slides. Other sectors are equally distributed. The Sub Technical team continues to look for ways to make the fiscal year forecasting a better process.

19 CONSTRUCTION % p Aug14 - Jul e Aug15 - Jul16 Calender Year f Formal non-government construction redistributed using the quarterly value of work put-in-place survey by FBOS (past 3 year trend); - Qtr. 3 & 4 are usually higher; Informal construction activity redistributed equally Aug16- Jul17 Fiscal year f 7.2 Aug17- Jul f Aug18- Jul19 $ VALUE OF WORK PUT-IN-PLACE Q1 Q2 Q3 Q4 2019f For the fiscal year ended Jul-17, 43.2% of the contribution comes from the calendar year-16 while 56.8% from the calendar year

20 WHOLESALE & RETAIL TRADE % Calender Year Fiscal year p Aug14 - Jul e Aug15 - Jul f Aug16- Jul f Aug17- Jul f Aug18- Jul f WRT redistributed using the quarterly WRT survey by FBOS; Past 3 year trend in WRT Qtr. 4 usually has the highest sales turnover For the fiscal year ended Jul-17, 42.6% of the contribution comes from the calendar year-16 while 57.4% from the calendar year-17 1,200,000 1,000, , , , ,000 0 WHOLESALE & RETAIL TRADE (SALES INCOME) Q1 Q2 Q3 Q

21 ACCOMMODATION & FOOD SERVICES % Calender Year Fiscal year p Aug14 - Jul e Aug15 - Jul f Aug16- Jul f Aug17- Jul f Aug18- Jul f Used the trend in visitor arrivals (past 3 year average) for the short term accommodation activities category; and Food & beverage serving activities category has been equally distributed For the fiscal year ended Jul-17, 44.6% of the contribution comes from the calendar year-16 while 55.4% from the calendar year Visitor Arrivals (calendar year) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

22 OTHER CATEGORIES Manufacturing redistributed using the quarterly IPI series by FBOS. Past 3 year trend in Manufacturing IPI Qtr. 3 usually is the highest. Fishing & aquaculture Considering the cyclical nature of fish production for the marine fishing on a commercial basis (including yellow fin, big eye, albacore and tuna like species) used past 3 year quarterly growth trend and applied the weights to the forecast data provided by the Department of Fisheries for the forecasting years; other categories including subsistence & informal fishing; marine & freshwater aquaculture and beach-demer categories have been distributed equally. Agriculture - Growing of sugarcane category redistributed using the actual monthly cane production for the past 3 years and applied the weights for the forecasting years; Other categories including non-cane agriculture, livestock and subsistence categories redistributed equally. Electricity redistributed using actual monthly electricity generated by the FEA and applied the weights to the forecast data provided by the FEA for the forecasting years; Source: Macroeconomic Technical Committee

23 OTHER CATEGORIES Water & sewerage category redistributed using the quarterly IPI data for water; Forestry & logging redistributed using the past 3 year trend in pine log and mahogany production data provided by TWIL and FHCL; Mining & quarrying redistributed using the quarterly IPI for mining & quarrying; Financial & insurance activities category other monetary intermediation category redistributed using the monthly loans & advances by the commercial banks; other categories redistributed equally; All Other sectors including real estate; education; health; public administration; information & communication; etc were distributed equally. Source: Macroeconomic Technical Committee

24 Outline Forecasting Process Forecasting GDP Calendar year Indicator & Data Source Forecasting GDP Fiscal Year Issues faced during forecasting

25 ISSUES FACED DURING FORECASTING Problems Obtaining reliable and timely forecasts from various stakeholders. Solutions/Current Practices Emphasise the importance of forecasts and statistics to the stakeholders. Improve liaison by organising regular industry meetings and visits. The variance between actual growth released by FBOS and the Macroeconomic Committee forecast can be high if data required is delayed. The lack of timely data and limited information on forward-looking indicators. Due to delays in the release of data for certain indicators (where there can be lags of up to 2 years) the forecasting team resorts to the use of assumptions. The assumption used for certain sectors/industries, may contradict the actual. As a result, the outcomes for certain sectors may deviate markedly from the forecasts. To reduce the variance between the actual and forecasts, regular meetings and discussions takes place with FBOS to align the indicators as well as with other stakeholders. Various partial indicators for each sector are used as proxies including exports/imports/lending data, etc. Additionally, liaison with other relevant stakeholders and Ministries is carried out on a regular basis.

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