Macroeconomic Modeling and Forecasting

1. Macroeconomic Modeling and Forecasting Nepal Rastra Bank Research Department Economic Analysis Division T.P.Kirala

2. Outline of the Presentation • Background • Meaning and Important of Forecasting • Need of Robust Model • Challenges and Solutions of Macro-forecasting. • Methods of Forecasting • Types of Macroeconomic Modeling • Modeling and Forecasting in the NRB. • Nepal MacroeonometricSectoral Modeling (NMESM)

3. Background • NRB’s one of the major tasks is forecasting of major macroeconomic variables • (Monetary policy formulation and policy suggestions to the government). • Economic Modelling helps forecasting these macroeconomic variables. • Use suit of models for robustness. • Take model outputs as basis but use judgment to finalize the forecast.

4. Forecasting in NRB Forecasts of Major Variables for FY2015/16

5. Meaning and importance of Modeling and Forecasting • Modeling: in understanding linkages and interaction of various sectors of the economy. • Forecasting: a statement about the future. • Future is uncertain/unpredictable • Forecasting gives probabilistic statement based on known value of present and past. • Important for policy makers: Formulate public policies and plans • For businesses: Formulate business plans • For general public: Formulate financial plans.

6. Need of Robust Model • Economies changes over time • Small changes, (e.g. technical progress), • Large changes (unanticipated shocks e.g. policy changes, regulation, political shift) • Economic Data are non-stationary

7. Forecast Error • Unanticipated shifts or structural breaks • Inaccurate data • Frequent revisions in data • Unpredictable variables • Incorrect models • Poor estimates of parameters

8. Challenges and Solutions • Main challenge: • Structural breaks • Unanticipated shocks • Non-stationarity of economic data • Solutions: • Correct model specification • Updating estimates • Adaptability of model to changes in economic

9. Methods of Forecasting • Guessing, informal model • Extrapolation • Leading Indicators (early warning signals) • Surveys • Time Series Models • Econometric systems models.

10. Types of Macroeconomic Modeling • Non-structural model (Time Series Model) • Primarily used for forecasting (Unconditional Forecasting) • Forecast quite well • Econometric Systems Models (Structural model) • Primarily used for policy analysis • Conditional Forecasting • Provide a framework for analysis

11. Non Structural Model (Time Series Models)

12. Recent Development of Non-structural model • Sim (1972)- Vector Autoregressive (VAR) model, • All variables are endogenous. • Data speak itself (data rather than theory), • Structural VAR (SVAR) to address the theory, variables ordering, cholesky decomposition.

13. Econometric Systems Models(Structural model) • Model consisting of several equations-simultaneous equation model. • The behavior of variables is simultaneously determined. • Simultaneous equation simulation model in policy analysis and forecasting.

14. Earlier structural models were in Keynesian tradition: • Focused on aggregate demand, • Short-term policy prescription, Economic stabilization. • Structural MEM flourished during fifties and sixties. • 1970s onwards, importance of large scale macoeonometric models started declining with Lucas Critique (1976).

15. Recent Development of Structural Model • Lucas Critique states that behavioral relationships estimated in one policy regime will not necessarily be stable in some other policy regime. • Varying structural parameter, • Unreliable for policy formulation. • Focus on the behavior of individual economic agents while modeling. • Developed micro-founded structural model • Eg. DSGE Model.

16. Modeling and Forecasting in the NRB • Non-structural macro-econometric modelings: • Individual attempts, VAR,SVAR,ARMA • Structural macro-econometric modelings: • Nepal Macro econometric Model (NMEM) • Dynamic Stochastic General Equilibrium Model (DSGE) • Nepal’s MacroecometricSecoral Model (NMESM)

17. Nepal Macroeconomic Modeling (NMEM) • Systematic development of MEM for policy analysis began only after 2005 with NMEM. • ADB first time developed this model for debt sustainability analysis. • MEM constructed on Keynesian income-expenditure framework. • NPC developed MDG consistent MEM (MDGcMEM) in 2012 • NPC developed LDC graduation MEM in 2014 (LDC graduation up to 2022) • The projection of GNI per capita is based on macroeconomic modeling.

18. Macroeconometric Modeling in NRB • NRB has been involved in upgrading and updating the NMEM since 2011. • ADB assisted for upgrading the NMEM • Upgradation consists of enhancing NMEM with monetary and external sectors (real, monetary, price, external, fiscal) • n-equations, n-identities, n-variables. • Upgrading is a continuous process. • In the process, Nepal’s MacroeconmicSectoral Model is under construction phase.

19. Dynamic Stochastic General Equilibrium (DSGE) Model • A Prototype DSGE Model for Nepal: by Jagjit S. Chadhay, University of Kent, in August 2011. • It is to be written in MATLAB code. • MATLAB with DYNARE has much facilitated for solving DSGE model at present. • Capacity enhancement for developing DSGE model is underway.

20. Methodology of Macro econometric Model Building • Specify behavioral equation • Check the diagnostic statistics (R2,t,DW) • Form model including equations and accounting identities • Simulate model to obtain historical simulation • Estimate Mean Absolute Percentage Error (MAPE) to check the forecasting ability of endogenous variables. • Obtain out-sample values of exogenous/policy variables based on future outlook. • Simulate model to obtain baseline forecast • Scenario analysis for partial effect. • Macroeconometric modeling in NRB is designed in Eveiws which has option for policy simulation.

21. Nepal Macroeconomic Sectoral Model (NMESM) • Data range: 1975 to 2014 • Six blocks: Real, Fiscal, Monetary, Externaland Price • Develop a baseline forecast for 6 periods ahead. • Following partial equilibrium approach • Supply shock are analyzed. • Economy recovery will start at the end of 2016.

22. Price Block

24. Monetary Block OLS Estimation Results of Monetary Block • LOG(M2/CPI) = -20.12 + 1.78*LOG(YPR) + 0.14*DUM_M2 + 0.50*AR(1)…………. …………(4) (-43.22) (48.64) (3.37) (5.40) R2= 0.99 DW=2.41 • LOG(CC/CPI) = -16.41 + 1.38*LOG(YPR) - 0.001*TB91 + 0.12*DUM_CC +0.49*AR(1)…… (5) (-34.04) (36.60) (-0.15) (2.57) (3.62) R2=0.99 DW=2.18 • LOG(TD/CPI) = -19.47 + 2.05*LOG(YPR) - 0.19*DUM_TD +0.52*AR(1)………………. ………..(6) (-36.19) (48.57) (-4.20) (8.29) R2=0.99 DW=2.59 Here, M2 is broad monetary aggregate, CPI=consumer price index, YPR=Real GDP, CC=Currency in circulation, TD=Time deposit, AR(1)=autoregressive of order 1. TB91= 91 days’ treasury bill rate, LOG=Natural log.

26. Fiscal Block Functional Form of Fiscal Block • LOG(CUSTOM) = -0.69 + 0.86*LOG(IMPORT) + 0.17*DUM_CUSTOM + 0.57 AR(1)…………………………… (7) (-3.36) (46.00) (3.36) (4.13) R2=0.99 DW=1.98 • D(VAT) = -33 + 0.07*D(YPRC) + 6531.59*DUM_VAT ………………………………………………………………(8) (-1.98) (18.62) (7.70) R2=0.96 Dw=2.12 • LOG(INC) = -9.56 + 1.42*LOG(NGDP) + 0.09*DUM_INC + 0.87 MA(1)… ………………………………………(9) (-26.30) (47.82) (2.19) (40.25) R2=0.99 DW=1.27 • D(EXISE) = -145.02 + 0.13*D(IGDP) + 5707.35*DUM_EXISE …………………..…………………………………(10) (-1.14) (9.09) (12.58) R2=0.93 DW=2.0 • LOG(OTHER) = 4.78 + 0.12*@TREND + 1.08*DUM_OTHER + 0.86 AR(1) ..…………………………………..(11) (5.10) (3.70) (8.24) (9.26) R2=0.95 DW=1.92 • LOG(GCE) = 0.49 + 0.96*LOG(GCE(-1)) + 0.29*DUM_GCE …..……………………………………………………(12) • (2.77) (51.89) (4.22) R2=0.98 DW=1.96 • LOG(GRE) = 0.17 + 0.99*LOG(GRE(-1)) + 0.39*DUM_GRE …… .……………………………………… …..…… (13) (3.55) (201.42) (10.50) R2=0.99 DW=2.15 • LOG(GPE) = 13.89 + 0.24*DUM_GPE + 0.97 AR(1) ………………..…………………………………………………(14) (3.76) (2.49) (57.14) R2=0.98 Dw=1.81 • LOG(GFG) = 49.80 + 0.35*DUM_GFG + 0.98AR(1) ………………..……………………………………………….. (15) (0.18) (5.21) (47.10) R2=0.98 DW=2.16

28. Real Sector Supply Side Equations • LOG(YAR) = 10.33+ 0.12*LOG(YAI/PDY) + 0.008*YA/YAE + 0.38*YAGYFI + 0.05*DUM_YAR ………………(16) (124.13) (4.83) (18.31) (9.32) (3.48) R2=0.99 DW=1.77 • LOG(YIR) = 4.33 + 0.27*LOG(YIE) + 0.12*LOG(YII/PDY) + 0.34*LOG(YAR+YSR) + 0.08*DUM_YIR………… (17) (9.88) (19.0) (4.04) (7.64) (4.81) R2=0.99 DW=1.93 • LOG(YSR)=3.86+0.10*LOG(YSI/PDY)+0.91*LOG(YSE) + 0.16*LOG(YS/YSE) + 0.02*DUM_YSR + 0.87AR(1)….(18) (1.82) (2.69) (3.25) (3.15) (2.53) (12.24) R2=0.99 DW=1.49 • LOG(YFINR) = 7.04 + 0.09*@TREND +0.85*AR(1) ………………………………………………………………… (19) (22.65) (7.63) (9.04) R2=0.99 DW=1.65 • LOG(YNITR) = 6.03 + 0.40*LOG(GTAX) + 0.75 AR(1) ……………………………………………………………. (20) (16.51) (8.12) R2=0.99 DW=1.99 Demand Side Equations • LOG(YPRC) = 152.31 + 1.15*LOG(YPR) + 0.036*LOG(BREM) + 0.025*DUM_YPRC + 0.89*AR(1) ……………….(21) (0.04) (3.50) (1.86) (1.73) (103.42) R2=0.99 DW=1.61 • LOG(YPUC) = 0.38 + 0.80*LOG(GTRV) + 0.14*LOG(GCE) + 0.33*MA(1) ………………………………………….(22) (2.21) (22.14) (2.89) (2.51) R2=0.99 DW=1.96 • LOG(YPRCA) = 0.91 + 0.27*LOG(GCE) + 0.62*LOG(MPS) + 0.11*DUM_YPRCA + 0.72*AR(1) …………………..(23) (1.37) (2.58) (11.40) (1.73) (6.77) R2=0.99 DW=1.67 • LOG(YPUCA) = 308.68 + 0.04*LOG(GFG) + 0.34*LOG(GFL) + 0.17*DUM_YPUCA + 0.90 AR(1) ……………. ….(24) (1.58) (4.30) (3.24) (39.65) R=0.99 DW=1.76

30. External Sector

31. Future direction in macroeconometric modeling activities • Modeling should focus on stability (internal and external) objective of the NRB. • In addition to point forecast, range of forecast is also equally likely. (Fan Chart presentation) • Forecast using time series modeling in case high frequency data (quarterly forecast). • Think developing a small size prototype model using important equations from each of the sectors. • Concerned division feedback for baseline assumptions. • Exclude social sector block; not NRB’s concern.

32. Thank You Very Much!