Macroeconometric Modeling, Forecasting, and Policy Analysis Using EViews
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Monday |
What will you learn and implement?Model, as a tool to answer certain questions History and generations of macroeconometric modeling Theoretical framework and stylized facts of macroeconometric modeling Types of macroeconometric models (structural, data-driven, hybrid, long-term vs short-term, quarterly vs annual, etc.) Structure of macroeconometric models -blocks and their interactions Types of data Time series data. Nature and frequency Data collection and processing/transformations Working with Data in EViews How to set up an EViews Work file. How to input data to EViews How to conduct a graphical analysis in EViews Descriptive statistics and tests in EViews |
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Tuesday |
What will you learn and implement?Least squares estimation in EViews The assumptions of least squares and hypothesis testing Normality test Serial correlation test Heteroskedasticity test Functional form test Stability test To test linear restrictions. Violating the Assumptions of Least squares Autocorrelation Heteroskedasticity Multicollinearity Misspecification Correct the standard errors in case of heteroskedasticity and/or serial correlation. |
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Wednesday |
What will you learn and implement?Nonstationarity and Unit root Unit root tests in EViews Making nonstationary variable stationary by differencing Order of integration De-trending trend stationary variables to make them stationary. Why is testing for cointegration important: long-run estimation versus spurious regression. Error correction Residual-based test for cointegration. How to estimate FMOLS, DOLS, and CCR in EViews How to conduct the Engle-Granger test in EViews Single equation-based test for cointegration How to estimate Auto Regressive Distributed Lags (ARDL) model in EViews How to conduct the ARDL Bounds test in EViews System of equations-based test for cointegration How to estimate Vector Autoregressive (VAR) and Vector Error Correction (VEC) models in EViews How to conduct the Johansen test in EViews Error correction modeling and short-run estimation General to specific (or David Hendry) modeling approach. Brief information about Autometrics - a machine learning modeling algorithm |
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Thursday |
What will you learn and implement?Estimating the behavioral equations of macroeconometric model: Theory-driven approach - structural models Data-driven approach - statistical models Combined approach - hybrid models Corrections for outliers and structural breaks Building a protype macroeconometric model in EViews: Estimation and testing for behavioral equations: private consumption, government expenditure, and private investments Putting behavioral equations and identities together to make a model. The algorithms for solving models: Newton versus Gauss-Seidel versus Broyden. Types for simulation and solutions for dynamics Checking the validity and consistency of the completed model. Checking statistical coherence of the model through statistical tests Evaluating the predictive ability of the model: in-sample Evaluating the predictive ability of the model: out-of-sample forecasting/projection Calibration - making adjustments to the model to improve it. Add factors. Updating the model: data and equations. |
Friday |
What will you learn and implement?Understanding inputs and outputs of the model in EViews Exogenous and endogenous variables, their switch Designing scenarios - how to create scenario inputs in EViews. How to solve the model for a given scenario using different solve options. Making what-if simulations using a policy or exogenous variable to assess the effect of a policy Making iterative simulations using a policy or exogenous variable to rich a policy target set Options for reporting the simulation results: level versus growth rate in deviations. Interpretations and policy implications |