Who am I?

1. Who am I? • Ludovic (‘Vic’) Dobbelaere • Member of the short-term forecasting team of the Federal Planning Bureau in Belgium • Contact details: • E-mail: LDO@plan.be • Phone: 5954 5334 (in Lesotho), +32 2 507 74 22 or +32 473 53 33 84 (in Belgium)

2. Who are you?

3. DAY 1General description of the model LudovicDobbelaereLDO@plan.be

4. Overview • Shifting forces behind economic growth • Modelling strategy • Specification of the model • Exogenous variables • Closure of the model • Model equations • GDP by expenditure • GDP by activity • Prices • Money • Tax revenue • Caveats • Use of the model at the MoFDP

5. Shifting forces behind economic growth • LHDA investment • Exports of goods and services • Net primary incomes from the rest of the world • Transfers from the SACU revenue pool • Net exports of goods and services

6. Modelling strategy • Model is mainly data-driven • Traditional economic theory • Aggregate demand is the main determinant of economic growth in the short run Lesotho: highly influenced by supply-side factors • Makes it difficult to disentangle trend and cycle • Traditional macro-econometric models for small open economies • Growth of relevant export markets and price competitiveness are key exogenous variables Lesotho: structure of exports has changed dramatically • Based on error correction mechanisms (ECM) Lesotho: difficult to establish long-term cointegrating relationships • Variables such as exports and LHDA-investment are exogenous

7. Specification of the model Exogenous variables (Estimated) behavioural equations Accounting identities MODEL Ad hoc equations

8. Value added by industry GDP GNI GNDI Domestic expenditure Imports Households’ disp. income Specification of the model:General overview (closure on imports) Net primary income Net current transfers Exports Fiscal Policy Lesotho Highlands Water Project

9. Specification of the model:Exogenous variables • Government expenditures and revenues • Public consumption (current and constant prices) • Public investment • Exception: tax revenue endogenous! • Value added agriculture • Export and export prices • Mining and quarrying • Textiles, clothing, footwear and leather • Water • Other • Balance of payments • Primary incomes • Transfers • South African CPI

10. Specification of the model:GDP by expenditure (constant prices) Private consumption Households’ disposable income (+)Consumer prices (-) Public consumption Private investment Private value added excl. agriculture (+) Public investment Stock building GDP Private value added (+) Exports Imports Final demand (+) (!only direct link when model is not closed on imports!)

11. Specification of the model:Households’ disposable income Constant wage share per industry Constant MI-share per industry Compensation of miners and other workers in RSA Compensation of employees Mixed income Households’ disposable income Transfersrest of the world  households Social benefits to households Income tax payable by individuals Transfers government households Constant implicit tax rate

12. Specification of the model:GDP by activity (constant prices) VA agriculture Exports MQ (+) VA mining and quarrying Exports MT (+) VA textiles... Other exports (+), private consumption (+), intermediate government consumption (+) VA other manufacturing VA Electricity GDP Total private VA excl.agriculture (+) VA Water Exports W (+) VA Construction Private & public investment (+) Private consumption (+), intermediate government consumption (+) VA Private services VA Public administration

13. Specification of the model:GDP by expenditure (deflators) Deflator VA oriented to domestic market (+), import prices (+) CPI Lesotho CPI SA  (1/1) consumption in SACPI Lesotho  (1/1) domestic cons Private consumption Public consumption CPI SA  (1/1) intermediate cons Deflator wages exogenous Private investment Import prices (1/1) Public investment GDP Stock building Exports Imports

14. Specification of the model:GDP by activity (deflators) VA agriculture SA CPI (1/1) VA mining and quarrying Deflator exports MQ (1/1) VA textiles... Deflator exports MT (1/1) VA other manufacturing SA PPI (1/1) VA Electricity GDP CPI Lesotho[-1] (1/1) VA Water Deflator exports W (1/1) VA Construction SA CPI (1/1) SA CPI (+),growth VA private services (+) VA Private services VA Public administration

15. Specification of the model:Tax revenue Based on implicit tax rates • income tax payable by individuals /(wages + mixed income) • income tax payable by corporations /(gross operating surplus)[-1] • taxes on goods and services /(intermediate government consumption +domestic private consumption – households’ own produce) • taxes on international trade and transactions/(exports of diamonds, semi-precious and precious stones)

16. Caveats • No employment data • Could improve wage and households’ disposable income forecasts • Development of households’ disposable income should be checked • Does not seem to be in line with evolution of private consumption • Probably related to lack of capital income data and bad registration of transfers from SA • Price block is rudimentary • Requires more information on formation of domestic prices

17. Use of the model at het MoFDP • Tools developed within the DIMMoL-project: • Financial Programming Framework (FP) • Accounting framework • Computable General Equilibrium model (CGE) • Designed to investigate specific topics, e.g. impact of HIV • Macro-econometric model (MM) • Designed as a complement to the FP • Exogenous variables taken from FP • Output MM can be taken into account in FP • Does not provide as much details as the FP • Allows to take second-round effects into account • Should be able to deliver a credible story behind the forecast • Can also be used to investigate the impact of a shock on one of the exogenous variables (scenario analysis)

18. DAY 2 (part I)Preparing the database

19. Copy necessary files to your PC • Copy “Eviews_FY”-folder to your hard disk • Structure of the folder: Contains EViews-files Contains Excel-files used to set up the database Contains Excel-files relating to exogenous variables Contains Excel-files in which output is presented under the form of graphs and tables

20. Getting started to update the database • Folder EViews_FY\Database\Source contains batch-file (copy_files.bat) that copies the necessary files from the server (Dimsrv Mark’s office) to your PC • Most files of Financial Programming are Excel2007-files (*.xlsx, *.xlsm)  use computer that can read those files • Structure of the Database folder: • Mon: contains monetary data (CPI, M1, M2 and MQ) • NatAc: contains data from national accounts • SA: contains RSA data (CPI, PPI, bond rates)

21. Overview of variable names • See Annex 2 in Manual

22. Updating the database in Excel • Sheet “Moddata” contains the data that will be exported to EViews • Colours in “Moddata” refer to the sheet where the series comes from • File from which the sheets are taken can be found in the sheet “Source” • Change the name of an existing sheet (e.g. “Nacts”  “Nacts2”) • Copy the new version of “Nacts” from MF_NA.xlsx • Replace in all references in “Moddata” “Nacts2” by “Nacts” • Delete the sheet “Nacts2” (beware of #REF!)

23. Import data into EViews • Make sure you have saved the Excel-file containing the data as an Excel2003-file (*.xls) • In EViews: • Create a new Workspace, then File – Import – Read • Execute model.prg up to loaddata.prg • Consequently, EViews calculates derived data (com.prg) and provides labels to the series (namelabel.prg)

24. DAY 2 (part II)Estimating the equations of the model and compiling the model

25. Estimating an equation in EViews + unit root testing • In an interactive way: • Open existing workfile with variables • Click Object – New Object – Equation and type the equation • Using a program • Execute est.prg • Interactively ‘playing’ with equations is possible after execution of est.prg. When playing with equations, do not forget to look at the residuals • Unit root testing can be done in EViews by clicking on a series and then choose for View – unit root testing (note that variable ‘resid’ cannot be subject to unit root testing, so first genr other series that equals ‘resid’

26. Compiling the model in EViews • First write the model in a txt-file • Equations have to be estimated previously in EViews • Identities are defined directly in the txt-file • Compile the model using the command “model_name.merge”

27. DAY 3 (part I)Exercise

28. Exercise: adding variables to the model • Suppose we want to split up value added for “other manufacturing” (sector MO in the model) into “food and beverages” and “other manufacturing” • What changes should be made in the database (Excel)? • What do we have to change in loaddata.prg? • Do we have to add series to generate in com.prg? • Changes to be made in namelabel.prg are straightforward • Changes to be made in est.prg depend on the type of equation these series get. • How do you introduce all this when you compile the model (model.txt)?

29. DAY 3 (part II)Determining exogenous variables

30. Preparing exogenous variables in Excel • Is done in more or less the same way as constructing the database • Colours refer to sheets from which the data come • Important difference: • Level of exogenous variables appears in sheet “FP” • Growth rates of the same variables appear in sheet “Exovar” • Growth rates of exogenous variables are used to avoid level shifts in first year of forecast • EViews reads growth rates from Exo1.xls and then appends them to the existing series in the database

31. DAY 3 (part III)Running a baseline simulation

32. Baseline simulation • Once exogenous variables are defined, the model is simulated by the solve.model_name– command Make sure this command is preceded by model1.solveopt(o=g) to make EViews use the Gauss-Seidel algorithm to solve the model • Results are written to base_gr.xls • Another Excelfile (Tables&Graphs_base.xlsx) is linked to base_gr.xls and provides graphs and tables with respect to the results of the model simulation

33. DAY 4 (part I)Scenario analysis

34. Defining a permanent shock:levels, growth rates etc.

35. Scenario analysis with the macromodel • Every shock should get a number • Define shock in Excelfile Scennumber.xls. Exo1.xls (containing the exogenous variables of the baseline) should also be opened as it is linked to Scennumber.xls • After defining the shock, the program scen_xls.prg can be run. !!The number of the shock should be introduced as a program argument • The simulation of the shock is done in the same way as that of the baseline scenario • Results are saved in scennumber.wf1 and are written to scen_tbl.xls in the Results-folder

36. Comparing the scenario to the baseline • Open scen_tbl.xls, Tables&Graphs_base.xlsx and Tables&Graphs_scen.xlsx (that is linked to both other files) together in Excel • The second column of Tables&Graphs_scen.xlsx provides the results of the scenario in deviations (in %, unless otherwise mentioned) from the baseline • Do not forget to verify in the comparison of the exogenous variables that the shock is the same as the one you programmed in Scennumber.xls

37. Scenario 1:Shock of 10% on all export items • Typical example of external shock affecting volumes with only limited effect on prices • Affects VA in all sectors and almost all expenditures except exogenous ones • Effect on income follows from increase in value added • Prices are mainly affected through deflator of private services • Mind composition effects on deflators (also illustrates importance of regular update of base year in national accounts)

38. Scenario 2:Shock of 10% on wages of Basotho in SA • Can be summarised as a shock on households’ disposable income in combination to a shock on net primary incomes from RoW • Effects on consumption oriented sectors are more important • Effect on GNDI is more important than effect on GDP • Illustrates difference between households’ disposable income for national or domestic consumption

39. Scenario 3:Shock of 10% on government investment • Example of internal shock with very limited second round effects • Only direct effect on construction sector • Limited effect on income and other sectors in the economy • Despite slight positive impact on prices, negative impact on GDP-deflator (artificial) • Return in terms of higher tax receipts is minimal • Model cannot give an idea about the positive (long-term) effects on production due to lower capital stock

40. Scenario 4:Shock of 10% on government consumption • Example of internal shock with small so-called import leak as government consumption has less imported content than other expenditure categories • Results in important effect on GDP, which also illustrates the weight of government consumption in Lesotho GDP • Return in terms of extra tax receipts amounts to roughly 25% of extra expenditures (in part because government also pays taxes on intermediate consumption)

41. Scenario 5:Shock of 10% on international (foreign) prices • Export prices remain unchanged: note that they can reflect both domestic (price-setting) and foreign (pricing to the market) prices • Wages of Basotho workers in SA were also adjusted as the price shock is supposed to be reflected in wages • Upward effect on prices is somewhat more important than downward effect on volumes  kind of inflation that makes government revenues increase • Trading losses generate important difference between GDP at constant prices and real GDI

42. Exercise: Scenario 6 • Define the following shock in scen6.xls • Total government expenditure cut of 1 billion Maloti: • Expenditure cut of 10% in wages, should be a price shock • Expenditure cut of 10% in intermediate consumption • Rest of the amount should be saved on government investment

43. DAY 5 (part I)A theoretical sidewalk

44. Estimation of an error correction mechanism (ECM)Example for private consumption • Two step Engle-Granger method • Long-term equation is generally “theoretical” relationship • log(CHDO) = c(1)*(t<1999) + c(2)*(t>1998) + log(YDHD/PCHD) • Coefficient of real disposable income is forced to be equal to 1 • Test for unit root in residuals • If unit root  no cointegration implying LT-equation cannot be used • Rejection of null hypotheses of unit root  short-run equation can be estimated • First calculate “long-run values” (CHDO_L, using “Forecast” in EViews) • Short-run equation • dlog(CHDO) = c(1)*(t=1988) + c(2)*(t=1999) + c(3)*dlog(YDHD/PCHD) – c(4)*(log(CHDO(-1))-log(CHDO_L(-1))) • The higher the value of c(4), the quicker CHDO will return to its long-term value

45. Using add factors in a model • Add factors can be used to modify the spontaneous outcome of an equation in the model • Two types of add factors can be used • additive add factor (~differences) dlog(Y-Y_ADD) = a + b*dlog(X) Positive values for Y_ADD will raise Y, negative values will lower it • multiplicative add factor (~growth rates) dlog(Y/Y_ADD) = a + b*dlog(X) Values bigger than 1 for Y_ADD will raise Y and vice versa • As in scenario analyses, add factors can be used to introduce a temporary or a permanent correction • Add factors can be generated automatically by EViews (within the model-object, under Proc), but then they are additive or multiplicative depending on the specification of the equation

46. DAY 5 (part II)Interpreting the baseline scenario

47. Taking a look at the exogenous variables • Government • Growth of government consumption (constant prices) decelerates heavily compared to recent years • Growth of public investment slows down in FY09/10 and recovers afterwards (also due to LHDA) • Massive decline in SACU-revenues in FY10/11 • Exports • Exports of diamonds and textiles are strongly affected by crisis in FY09/10, but return to pre-crisis growth afterwards • Other export categories resist better • Households’ disposable income • Quite well underpinned by transfers and social benefits • Inflation goes down, reducing difference between growth in nominal and real terms

48. GDP by activity at constant prices • Primary industries • downturn in FY09/10 limited thanks to relative stability of agricultural sector (around 75% of total) • Increasing importance of mining and quarrying in coming years • Secondary industries • Textiles sector (almost half of secondary industries) heavily affected by world trade crisis • Other manufacturing mainly reacts to intermediate public consumption and other exports • Construction sector mainly victim of lower public investment • Water sector on stable growth path • Growth electricity result of business cycle in private industries • Tertiary industries • Private services mainly determined by intermediate government consumption as private consumption growth remains quite stable • VA government mainly result of wage growth at constant prices

49. GDP by expenditure at constant prices • Public consumption • Quite stable growth profile in FY09/10 and FY10/11 despite bumpy profile of intermediate consumption • Afterwards negative growth • Private consumption • Roughly stable growth in line with disposable income (and add factor for FY09/10 • Investment • Public investment grows stronger than in the past • Private investment growth remains below growth rates from FY03/04 to FY07/08. Investment rate stabilises around historical average • Imports • Very close to results of equation (compare MDO and MDOBIS)

50. Economic growth(growth of GDP at constant prices) • Assessed on the basis of value added • Downturn FY09/10 due to all industries • Recovery FY10/11 mainly related to performance of secondary industries (manufacturing in particular) • Lower growth rates afterwards result of lower growth in tertiary industries (effect of intermediate government consumption on private services) • Assessed on the basis of expenditures • Crisis FY09/10 strange enough hardly visible in net-exports due to almost zero growth of investment • Recovery FY10/11 again not visible in net-exports as imports react to acceleration government investment • Lower growth rate FY11/12 results from decline government consumption • Lower investment growth and higher export growth cancel each other out in FY12/13