NEEDS ASSESSMENT ON training in MODELLING

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NEEDS ASSESSMENT ON training in MODELLING
FORECASTING FOR EAC CENTRAL BANKS
UNITED NATIONS ECONOMIC COMMISSION FOR AFRICA Sub
regional Office for Eastern Africa (SRO EA)

• Dr. Félicien USENGUMUKIZA
• Senior Lecturer at National University of Rwanda

Kigali, March 13TH 2010
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Presentation Outline

• Introduction
• Needs submitted by respective EAC Central Banks
• Proposed training module for the EAC Central
  Banks
• Trainings Methodology
• Expected outcomes
• Outline and lecture plan for the proposed
  training program
• Conclusion
• Recommendations

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NEEDS SUBMITTED BY RESPECTIVE EAC CENTRAL BANKS
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NEEDS SUBMITTED BY RESPECTIVE EAC CENTRAL BANKS

• 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.

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1. Banque de la republique du Burundi (BRB)

• 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.

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1. Banque de la republique du Burundi (Contd)

• For capacity building, BRB would suggest to
  organize an internal training before the joint
  training within EAC. This would facilitate
  Burundis 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

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2. Central Bank of Kenya (CBK)

• 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.

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3. National bank of Rwanda (BNR)

• 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.

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3. National bank of Rwanda (BNR).

• 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).

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Bank of Uganda

• 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

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Bank of Uganda (Contd)

• 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.

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Bank of Tanzania
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
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Bank of Tanzania (Contd)

• 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

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PROPOSED TRAINING MODULE FOR THE EAC CENTRAL BANKS
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Trainings methodology

• 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.

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Expected programme outcomes

• Upon successful completion of training programme,
  participants should be able to
• 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.
• Demonstrate application of univariate time series
  modelling and forecasting using ARMA models

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Expected programme outcomes.

• 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.

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Outline and lecture plan for the proposed
training programe

• 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

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Outline and lecture plan.

• 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,

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Outline and lecture plan.

• 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

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Outline and lecture plan.

• 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.

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Outline and lecture plan.

• 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

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Outline and lecture plan.

• TOPIC 5 Modelling and Forecasting Volatility
  (Contd)
• 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

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Outline and lecture plan.

• 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

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Outline and lecture plan.

• Topic 7 Panel Econometrics
• Basic panel data analysis
• One-way error components
• Two way error components
• Testing hypotheses
• Dynamic panel
• Nonstationary panel

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Conclusion

• 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

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Conclusion (Contd)

• 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

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Recommendations

• 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.

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Recommendations (Contd)

• 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.

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Thank you