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GDP FORECASTING PROCESS

19 October 2016

GDP FORECASTING WORKSHOP OCTOBER 2016

Forecasting Process

Outline

Issues faced during forecasting

Forecasting GDP – Calendar Year

Indicator & Data Source

Forecasting GDP – Fiscal Year

FORECASTING PROCESSThe Macroeconomic Committee is responsible for forecasting GDP.

The Macroeconomic Committee is made up of senior representativesfrom theo 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 ando The Reserve Bank of Fiji.

FORECASTING PROCESS

Macroeconomic Committee (MC)Chaired by the Governor of RBFApproves 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.

Forecasting Process

Outline





FORECASTING GDP – CALENDAR YEARValue 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 upwith the sector forecast.

The latest GDP release by FBOS is used as the base for forecasting. E.g. 2015 provisional GDPreleased by FBOS is used as the base for forecasting in R2, 2016 and R1, 2017.

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 GDPworksheet; taking into account other information/developments.

Add the lines up to get sector VA and sectors are added to get the overall GDP @ constantprices.

Forecasting Process

Outline





INDICATORS AND DATA SOURCESector Indicators Used Sources of DataAgriculture Production forecast for cane & non-cane crops

and livestockMinistry of Agriculture, Fiji SugarCorporation

Forestry & Logging Native & exotic (pine/mahogany logging; non-wood forest products e.g masi, voivoi

Department of Forestry, Tropik WoodIndustries Limited & Fiji HardwoodCorporation Limited

Fishing & Aquaculture Marine Fishing on commercial basis includestuna (yellow fin, big eye and albacore) and by-catch, freshwater fishing, beche-de-mer,aquaculture (marine & freshwater)

Department of Fisheries, Major FishingCompanies

Mining & Quarrying Production forecast data for gold & bauxite andquarrying activitiesOther proxies used: Industrial Production Index

Vatukoula Gold Mines Limited, XinfaAurum Exploration (Fiji) Limited.

Manufacturing Forecast Production data for food & beveragesand non-food productsOther proxies used: Industrial Production Index

Fiji Bureau of Statistics; MajorManufacturing Companies includingFood & beverage manufacturing,garment, hardware, furniture, etc

INDICATORS AND DATA SOURCESector Indicators Used Sources of DataElectricity, gas, steam and airconditioning supply

Forecast data for electricity production Fiji Electricity Authority

Water supply, sewerage,waste management andremediation activities

Forecast data for water production Water Authority of Fiji

Construction Value of work put-in-placeMajor construction projects

Other proxies used: building permits issued;lending to real estate & construction sectors;domestic cement sales

Fiji Bureau of Statistics; Investment Fiji(projects registered & implemented);Town & Municipal Councils (permitsissued); Pacific Cement & TengyCement Company Ltd.

Wholesale & Retail Sale of motor vehicles & motor vehicle parts.Forecast data for wholesaling and retailingactivities.Other proxies used: new & second hand vehicleregistration/sales; Net VAT; Wholesale & RetailTrade Survey (FBOS).

Fiji Bureau of Statistics (QuarterlyWholesale & Retail Trade Survey);Reserve Bank of Fiji (Retail SalesSurvey)

INDICATORS AND DATA SOURCESector Indicators Used Sources of DataTransport & Storage Land Transport – forecast stock of buses, taxis,

rental & hire cars and heavy goods vehicles

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 interisland within Fiji

Warehousing & storage activities, cargo loaded &offloaded – by air & sea; postal & courieractivities.

Land Transport Authority;Fiji Airways & Fiji Link;Maritime Safety Authority of Fiji;Air Terminal Services,Fiji Ports Corporation Ltd;Post Fiji;Major Courier Companies

Accommodation & FoodServices

Room night sold; visitor arrivals; forecast data forbeverage & food serving activities (restaurants)

Fiji Bureau of Statistics; Retail SalesSurvey; industry liaison with majortourism service providers includinghotels & resorts and Fiji Airways

INDICATORS AND DATA SOURCESector Indicators Used Sources of DataInformation &Communication

No of newspapers published;Television programming & broadcasting activities(revenue & expenditure);Radio broadcasting activities;Wired & wireless telecommunication activities(no. of registered users/revenue & expenditure);andData processing activities (broadband datausage)

Fiji Times and Fiji Sun;Fiji Directories Ltd;Fiji TV;Fiji Broadcasting Commission;FINTEL;TFL; andVodafone & Digicel

Financial & InsuranceActivities

Central Banking activities (staff cost, capitalformation less (amortisation & depreciation));Commercial banking activities (Gross output,Intermediate costs);Pension Funding activities (FNPF Financials); andLife & non-life insurance activities

Reserve Bank (Accounting Unit &Financial Institutions Group);FNPF;Insurance Annual Reports; andMajor Insurance Companies

INDICATORS AND DATA SOURCESector Indicators Used Sources of DataReal Estate Activities Real estate activities with owned or leased

properties & real estate activities on a fee orcontract basis (total sales turnover)Other proxies used: Real Estate Survey (RBF); lendingto real estate sector

Real Estate Licensing Board;Major Real Estate Companies;Valuations Office

Other Government RelatedSectors – including PublicAdministration & defence;health; education

Segs 1 & 2 (wages & salaries) from BudgetEstimates;No. of teachers,No. of student enrolments,No. of health workers (including doctors, nurses &caregivers)

Ministry of Finance; Ministry ofEducation, Ministry of Health

EXAMPLE

Scenario 1: FSC Direct ForecastSugar & Cane

DIRECT FSC FORECASTSCane Sugar TCTS Cane %

ChangeSugar % Change

2010 1,778,014 131,506 13.5 -20.9 -21.52011 2,095,741 166,669 12.6 17.9 26.72012 1,546,897 154,813 10.0 -26.2 -7.12013 1,610,404 179,870 9.0 4.1 16.22014 1,832,186 226,858 8.1 13.8 26.1

2015(f) 1,844,524 221,934 8.3 0.7 -2.52016(f) 1,471,000 163,444 9.0 -20.3 -26.42017(f) 2,000,000 250,000 8.0 36.0 53.02018(f) 2,500,000 312,500 8.0 25.0 25.0

Forecasting Cane & SugarScenario 2: SUB MTC FORECASTSugar & Cane

SUB MTC FORECASTSCane Sugar TCTS Cane %

ChangeSugar % Change

2010 1,778,014 131,506 13.5 -20.9 -21.52011 2,095,741 166,669 12.6 17.9 26.72012 1,546,897 154,813 10.0 -26.2 -7.12013 1,610,404 179,870 9.0 4.1 16.22014 1,832,186 226,858 8.1 13.8 26.1

2015(f) 1,844,524 221,934 8.3 0.7 -2.52016(f) 1,371,000 152,333 9 -25.7 -31.42017(f) 1,782,300 198,033 9 30.0 30.02018(f) 2,138,760 237641 9 20.0 20.0

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 bythe cyclone and severe dry weather conditions prior to the cyclone.

2017:Cane production is expected to note a turnaround in 2017. 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.

EXAMPLEForecasting Cane & Sugar – GDP Sheet

GDP @ Constant Basic Price Values ($000) Percent

Activity Base Weight 2013 2014

As at 19/05/16 2015(e)

As at 19/05/16 2016(f)

As at 19/05/16 2017(f)

As at 19/05/16 2018(f) 2013 2014

As at 19/05/16 2015(e)

As at 19/05/16 2016(f)

As at 19/05/16 2017(f)

As at 19/05/16 2018(f)

AGRICULTURE 8.2 497,078.5 485,396.6 494,676.4 442,479.4 460,915 478,228 6.8 -2.4 1.9 -10.6 4.2 3.8

SUBSISTENCE AGRICULTURE 2.7 154,503.9 153,296.9 153,910.1 154,525.7 155,144 155,764 0.8 -0.8 0.4 0.4 0.4 0.4

INFORMAL AGRICULTURE 0.7 38,203.9 37,905.5 38,057.1 38,209.3 38,362 38,516 0.8 -0.8 0.4 0.4 0.4 0.4GENERAL GOVERNMENT 0.1 8,715.8 10,053.6 10,355.2 10,355.2 10,355 10,355 12.8 15.3 3.0 0.0 0.0 0.0FORMAL NON-GOVERNMENT AGRICULTURE 4.8 295,654.9 284,140.7 292,354.1 239,389.2 257,054 273,593 11.0 -3.9 2.9 -18.1 7.4 6.4

Growing of rice 0.1 2,495.6 1,589.4 1621.2 1540.2 1648.0 1730.4 34.1 -36.3 2.0 -5.0 7.0 5.0Growing of taro 0.7 51,388.5 36,601.5 36601.5 25621.0 30745.2 33819.8 45.5 -28.8 0.0 -30.0 20.0 10.0Growing of cassava 0.3 21,085.8 21,410.3 22480.8 17984.7 19783.1 20772.3 -23.7 1.5 5.0 -20.0 10.0 5.0Growing of yam 0.1 3,431.8 2,081.8 2185.9 1748.7 2011.0 2212.2 -15.8 -39.3 5.0 -20.0 15.0 10.0Growing of Kumala 0.1 4,330.3 4,083.4 4287.5 4501.9 4727.0 4963.4 -9.3 -5.7 5.0 5.0 5.0 5.0Growing of vegetables and melons, roots and tubers not elsewhere specified 0.4 20,707.1 20,758.2 20965.8 19917.5 20515.0 21130.4 0.4 0.2 1.0 -5.0 3.0 3.0

Growing of sugar cane 1.2 44,214.6 50,334.6 50687.0 37674.7 48977.1 58772.5 4.1 13.8 0.7 -25.7 30.0 20.0Growing of bananas 0.1 3,227.1 6,468.9 6792.3 6113.1 6724.4 7396.8 39.1 100.5 5.0 -10.0 10.0 10.0Growing of oleaginous fruits 0.1 13,529.4 10,067.9 10571.3 8457.1 7611.3 7230.8 76.2 -25.6 5.0 -20.0 -10.0 -5.0MANUFACTURING 14.1 838,213.8 855,457.0 867,667.2 859,358.1 898,255 935,395 5.9 2.1 1.4 -1.0 4.5 4.1INFORMAL MANUFACTURING 3.0 174,332.3 174,109.7 175,676.7 177,257.8 178,853 180,463 0.4 -0.1 0.9 0.9 0.9 0.9GENERAL GOVERNMENT 0.1 2,694.5 2,853.3 2,938.9 2,938.9 2,939 2,939 -9.7 5.9 3.0 0.0 0.0 0.0FORMAL NON-GOVERNMENT MANUFACTURING 11.0 661,187.0 678,494.0 689,051.5 679,161.3 716,463 751,993 7.5 2.6 1.6 -1.4 5.5 5.0

Manufacture of food and beverages 5.2 306,995.6 318,823.8 324,521.6 304,029.4 330,229 354,254 7.1 3.9 1.8 -6.3 8.6 7.3Manufacture of sugar 1.0 81,319.8 92,517.7 90,204.7 61,915.6 80,490.3 96,588.4 88.3 13.8 -2.5 -31.4 30.0 20.0Manufacture of cocoa, chocolate and sugar confectionery 0.2 7,364.3 7,009.4 7,149.6 7,292.6 7,438.4 7,587.2 -41.1 -4.8 2.0 2.0 2.0 2.0

-0.5

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-0.3

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0.4

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2016 CONTRIBUTION TO GROWTH

Source: Macroeconomic Technical Committee

Manufacture of sugar

Forecasting GDP – an Example

Production of

sugarcane

+2.4%

-0.5

-0.3

-0.1

0.1

0.3

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FORECASTING GDP – AN EXAMPLE2017 CONTRIBUTION TO GROWTH


Manufacture of sugar

Production of sugarcane

Forecasting Process

Outline

Forecasting GDP – Calendar year


Issues Faced During Forecasting


FORECASTING GDP – FISCAL YEARWith 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 tobe used in the National Budget process.

The Forecasting team used a statistical method to redistribute the Calendaryear GDP into fiscal year terms.

For sectors that use indicators with a clear seasonal trend (such as visitorarrivals, manufacturing, cane & sugar production, construction and wholesale& retail trade), past 3-year trend in the indicator was used to redistribute forthe 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 yearforecasting a better process.

CONSTRUCTION

%

• Formal non-Government construction –redistributed using the quarterly value of workput-in-place survey by FBOS (past 3 year trend); -Qtr. 3 & 4 are usually higher;

• Informal construction activity redistributedequally.

For the fiscal year ended Jul-17, 43.2% of thecontribution comes from the calendar year-16 while56.8% from the calendar year-17

9.012.9

16.6

10.3

5.0 5.0

11.315.1

12.8

7.25.0

0.0

5.0

10.0

15.0

20.0

2014p Aug14 -Jul15

2015e Aug15 -Jul16

2016f Aug16-Jul17

2017f Aug17-Jul18

2018f Aug18-Jul19

2019f

Calender Year Fiscal year

0.0

20000.0

40000.0

60000.0

80000.0

100000.0

120000.0

140000.0

2013 2014 2015

Q1 Q2 Q3 Q4

$ VALUE OF WORK PUT-IN-PLACE

WHOLESALE & RETAIL TRADE

%

• WRT – redistributed using the quarterly WRTsurvey by FBOS;

• Past 3 year trend in WRT – Qtr. 4 usually has thehighest sales turnover


1.2

4.25.0

3.3 2.8 2.82.9

4.74.0

3.0 2.8

0.0

2.0

4.0

6.0

2014p Aug14 -Jul15

2015e Aug15 -Jul16

2016f Aug16-Jul17

2017f Aug17-Jul18

2018f Aug18-Jul19

2019f


0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

2013 2014 2015

Q1 Q2 Q3 Q4

WHOLESALE & RETAIL TRADE (SALES INCOME)

ACCOMMODATION & FOOD SERVICES

%

• Used the trend in visitor arrivals (past 3 yearaverage) for the short term accommodationactivities category; and

• Food & beverage serving activities category hasbeen equally distributed


3.4

7.5 8.4

4.76.2

4.76.2 7.1

5.3 4.7 4.7

02468

10

2014p Aug14 -Jul15

2015e Aug15 -Jul16

2016f Aug16-Jul17

2017f Aug17-Jul18

2018f Aug18-Jul19

2019f


0

20000

40000

60000

80000

100000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2013 2014 2015 2016

Visitor Arrivals (calendar year)

OTHER CATEGORIES


• Manufacturing – redistributed using the quarterly IPI series by FBOS. Past 3 year trend inManufacturing IPI – Qtr. 3 usually is the highest.

• Fishing & aquaculture – Considering the cyclical nature of fish production – for the marinefishing 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 dataprovided by the Department of Fisheries for the forecasting years; other categoriesincluding subsistence & informal fishing; marine & freshwater aquaculture and beach-de-mer categories have been distributed equally.

• Agriculture - Growing of sugarcane category – redistributed using the actual monthly caneproduction for the past 3 years and applied the weights for the forecasting years; Othercategories including non-cane agriculture, livestock and subsistence categories redistributedequally.

• Electricity – redistributed using actual monthly electricity generated by the FEA and appliedthe weights to the forecast data provided by the FEA for the forecasting years;

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 mahoganyproduction data provided by TWIL and FHCL;

• Mining & quarrying – redistributed using the quarterly IPI for mining & quarrying;

• Financial & insurance activities category – other monetary intermediation categoryredistributed using the monthly loans & advances by the commercial banks; othercategories redistributed equally;

• All Other sectors including real estate; education; health; public administration;information & communication; etc were distributed equally.

Forecasting Process

Outline


Forecasting GDP – Calendar year



ISSUES FACED DURING FORECASTINGProblems Solutions/Current Practices

Obtaining reliable and timely forecasts from variousstakeholders.

Emphasise the importance of forecasts and statistics tothe stakeholders. Improve liaison by organising regularindustry meetings and visits.

The variance between actual growth released by FBOSand the Macroeconomic Committee forecast can behigh if data required is delayed.

To reduce the variance between the actual and forecasts,regular meetings and discussions takes place with FBOSto align the indicators as well as with other stakeholders.

The lack of timely data and limited information onforward-looking indicators. Due to delays in the releaseof data for certain indicators (where there can be lags ofup to 2 years) the forecasting team resorts to the use ofassumptions. The assumption used for certainsectors/industries, may contradict the actual. As aresult, the outcomes for certain sectors may deviatemarkedly from the forecasts.

Various partial indicators for each sector are used asproxies – including exports/imports/lending data, etc.

Additionally, liaison with other relevant stakeholders andMinistries is carried out on a regular basis.

THANK YOU

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