1. Dr. Félicien USENGUMUKIZA Senior Lecturer at National University of Rwanda UNITED NATIONS ECONOMIC COMMISSION FOR AFRICA Sub regional Office for Eastern Africa (SRO – EA) Kigali, March 13 TH 2010

2.  Introduction  Needs submitted by respective EAC Central Banks  Proposed training module for the EAC Central Banks  Training’s Methodology  Expected outcomes  Outline and lecture plan for the proposed training program  Conclusion  Recommendations

3. NEEDS SUBMITTED BY RESPECTIVE EAC CENTRAL BANKS

4.  The below proposed training modules in Modelling and Forecasting is a compilation of Needs submitted by the five EAC Central Banks,  These modules are proposed to be used during the short training programme of staff from EAC Central Banks as recommended by the last MAC meeting held in Kigali on May 2009.

5. Due to lack of enough qualified staff in econometric analysis, BRB has suggested to organize training in two phases: enrichment of the training and capacity building. Enrichment of the training should focus on overview of the theory related to economic and statistic analysis. This would be concentrated on: Inflation forecasting, Monetary aggregates, exchange rate and banking liquidity.

6. For capacity building, BRB would suggest to organize an internal training before the joint training within EAC. This would facilitate Burundi’s team to be on same page with their colleagues of the EAC Central Banks. To this end, some topics have been identified for the internal training:  Introduction to statistical analysis  Single and multiple regression models  Introduction to stationarity, unit roots and cointegration

7. The central Bank of Kenya (CBK) formulated its needs in training with detailed topics and their justification:  Basic Econometrics : linear regression analysis, system of equations,  Time series econometrics : stationarity/unit roots analysis, testing unit roots, conintegration analysis,  Cross-section and survey methodology,  Panel Econometrics: Basic panel data analysis, dynamic panel, nonstationary panel,  Macroeconometric modelling: Building a macro model, DSGE models.

8.  BNR provided a detailed and comprehensive program which can be constitute a model of the final training module.  Apart providing contents of the proposed topics, BNR provided also the description of proposed training, the aims and the expected outcomes.

9.  TOPIC 1:Introduction - Financial Modelling & Forecasting Techniques  TOPIC 2:Model building with the Classical Linear Regression Model  TOPIC 3:Univariate Time Series Modelling and Forecasting  TOPIC 4:Multivariate Models  TOPIC5: Unit Root & Cointegration in Modelling Long-run Relationships  TOPIC 6:Modelling and Forecasting Volatility  TOPIC 7:Conducting Empirical Research in Banking & Finance  Computer Workshops (Hand-on Exercises using EViews) (For each topic, it is foreseen a computer workshop).

10. The needs formulated by Bank of Uganda are more specific and manage to be more focusing. The below provided topics have been identified as priority of Bank of Uganda:  Data exploration methods  Conditional Error Correction Models under the ARDL approach  Granger Causality Tests in Conditional Error-Correction Models (CECM) under the ARDL approach  Multiple Equation Analysis – Dealing with systems of equations (Solving estimated systems of equations), calibrating system of equations, forecasting using systems of equations and performing single and multivariate simulations

11.  Structural VAR models and their application in Central banking  Bayesian VAR models,  Fan charts (Win Solve)  Macro econometric modelling  Forecasting using macroeconomic models and linear stochastic models (AR,MA and ARMA/ARIMA models)  Seasonality tests in economic time series  Structural breaks and model selection: tests for structural breaks, Empirical evidence on structural breaks and their implications for an analysis for NAIRU, technology and monetary policy shocks.  Panel Data Econometrics: Unit root tests, cointegration tests.

12. Bank of Tanzania formulated Needs which are divided into two groups. Approaches to Forecasting and Econometrics training: 1. 0. Approaches to Forecasting 1.1. Simple and Naive Methods 1.2. Model Based Forecasting 1.2.1 Macroeconomic Model Building 1.2.2 Numerical Analysis and Forecasting 1.2.2.1 Numerical Simulations 1.2.2.1.1 Fun charts projections 1.2.3 Econometric Forecasting

13. 2.0. Econometrics Training Needs 2.1 Data Analysis 2.1.1 Unit root tests, co-integrating tests, etc. 2.2 Estimating Structural Models 2.2.1 Two stage least square estimation and multiple equations estimations. 2.2.2 Generalized Methods of Moments (GMM) 2.2.3 Forecasting with Structural Models 2.3 Time Series Econometrics 2.3.1 Univariate Time Series Analysis 2.3.2 Structural Vector Autoregression (SVAR) 2.3.3 Co-integration and Vector Error Correction Models (VECM). 3.0. State Space Models 3.1 Kalman Filtering Techniques

14. PROPOSED TRAINING MODULE FOR THE EAC CENTRAL BANKS

15.  To reach the objective of the training, its methodology should based on:  Formal training, practical exercises, computer-based simulations and the frequent use of case studies based on real-life business situations.  The topics should be designed to be practical for attendees and their workplace.  The contribution of Participants should highly encouraged especially in terms of identifying their areas of interest to be addressed.  The lecturer would be available throughout the entire course for additional guidance if required.

16.  Upon successful completion of training programme, participants should be able to: A. Apply and explain the standard procedures for model-building in economics and finance, including the empirical testing of finance models and forecasting of financial variables, which are central to policy making in Central Banks and for EAC economies. B. Demonstrate application of univariate time series modelling and forecasting using ARMA models;

17. C. Show the application of multivariate modes, with emphasis on VAR models as well as finance models that feature simultaneous equations; D. Test for unit root and cointegration in modelling long-run relationships in finance; E. Discuss and demonstrate the main techniques used in modelling and forecasting volatility, with emphasis on the class of ARCH models and extensions such as GARCH, GARCH-M, EGARCH and GJR formulations.

18.  TOPIC 1:Basic Econometrics  A brief overview of the classical linear regression model;  Diagnostic testing, including parameter stability;  Violations of the CLRM assumptions  General-to-specific modelling  Applications and examples  Generalized Methods of Moments (GMM)  Case Study: Use of E Views on Model building with the CLRM

19.  TOPIC 2: Univariate Time Series Modelling and Forecasting  Standard models of stochastic processes (white noise, moving average and autoregressive processes);  ARMA processes and building ARMA models;  Forecasting in econometrics with application to some EAC Countries.  Case Study: E Views: estimation of a ARMA model, Forecasting of inflation by using an ARMA model,

20.  TOPIC 3:Multivariate Models  Estimation techniques for simultaneous equations models  Vector autoregressive (VAR) models  Causality testing  Impulse responses and variance decompositions  Structural VAR models and their application in Central banking  Bayesian VAR models  Case study: Use of E Views on Multivariate Modelling and forecasting: 1. Identification of monetary policy transmission mechanism 2. Inflation forecasting

21.  TOPIC 4: Unit Root & Cointegration in Modelling Long-run Relationships  Stationarity and unit root testing  Cointegration: Engle-Granger and Johansen techniques  Equilibrium correction or error correction models  Seasonality tests in economic time series  Structural breaks and model selection: tests for structural breaks, Empirical evidence on structural breaks and their implications for an analysis for monetary policy shocks. Use here RATS for example.  Case Study: Estimation of Money demand, test of stability of money multiplier.

22.  TOPIC 5:Modelling and Forecasting Volatility  Non-linearity in financial time series  The class of ARCH models  Generalised ARCH (GARCH) models  Extensions to the basic GARCH model such as GARCH-M, EGARCH and GJR (TGARCH) formulations  Volatility forecasting using GARCH-type models

23.  TOPIC 5: Modelling and Forecasting Volatility (Cont’d)  Approaches to Forecasting  Simple and Naive Methods  Model Based Forecasting  Macroeconomic Model Building  Numerical Analysis and Forecasting  Numerical Simulations  Fun charts projections  Case Study: Modelling and Forecasting Volatility; Fun charts to have projections on inflation

24.  Topic 6: Cross-Section and survey methodology  How to conduct surveys  Data coding and entry  Binary choice models  linear probability model  logit and probit model  Multinomial choice models  Multinomial logit/probit  Conditional logit  Nested logit  Sample selection and truncated models  Heckit model  Tobit

25.  Topic 7: Panel Econometrics  Basic panel data analysis  One-way error components  Two way error components  Testing hypotheses  Dynamic panel  Nonstationary panel

26.  The reality found in EAC Central Banks confirms that the training in Modelling and Forecasting is for great necessity.  The heads of research department in respective EAC Central banks are welcoming the initiative proposed by UNECA of providing such kind of training and manifested interest to attend and to benefit from this training in order to improve the used methodology in terms of modelling and forecasting in macroeconomic and financial analysis

27.  As the EAC is deepening and widening its regional integration, the harmonisation of macroeconomic and financial analysis will facilitate to eliminate gaps observed in interpretation of national and regional economy  Each central bank provided its priorities in terms of training based on its own realities. Because of diversification in terms of needs, it was not easy to consider all needs provided by every individual Bank. To this end, a common training module has been formulated based on general and common needs. However, the specific needs may be considered in an individual local training which may be organized exclusively for the concerned Central Bank

28.  Besides the common training formulated in the present report, BRB needs a particular training as summarized in their needs. Due to lack of high qualified staff in macroeconometrics, the specific training would start from : introduction to statistic analysis and introduction to the use of software applied in econometric analysis. The training would be provided by a local expert in order to avoid high costs of an expatriate.  The common training would be more focusing rather than theoretical. The case study of each participating country would provide a good example of practice of the theory.

29.  As the Modelling and Forecasting Program is not a particularity of Central Banks alone, it is recommended to involve other staff from other institutions concerned by the topics (e.g. Ministries of Finance, Institutes of Statistics, etc.)  Due to the importance and the complexity of the training in Modelling and Forecasting, this kind of training should be organized periodically in order to make sure that the previous training has produced positive results.