BUSINESS FORECASTING BU MBA 4th Semester

Syllabus

4.8.2.: BUSINESS FORECASTING

Module 1                                                                                                            10 Hours

Introduction to Forecasting: Introduction, Role of Forecasting in Business, Steps in Forecasting and Methods of Forecasting. Correlation: Partial and Multiple Correlation. Regression Analysis: Multiple Regression Analysis, Testing the Assumptions of Regression: Multicollinearity, Heteroscedasticity and Autocorrelation.

Module 2                                                                                                            10 Hours

Demand Analysis: An Overview; Significance of Demand Analysis and Forecasting, Determinants of Demand, Elasticity of Demand, Revenue and Profit of a Firm Estimation of Demand, Forecasting Demand, Selecting a Forecasting Technique, Purpose of Forecast, Type of Users

Module 3                                                                                                            10 Hours

Marketing Research: Marketing Research Techniques, Consumer Surveys, Consumer Clinics and Focus Groups Market Experiments in Test Stores, Statistical Estimations, Variable Identification, Time Series and Cross-Sectional Data Collection, Specification of the Model, Estimation of the Parameters, Interpretation of Regression Statistics, Time Series Regression; Forecasting with Regression Model: Unconditional Forecasting, Forecasting with Serially Correlated Errors, Conditional Forecasting

Module 4                                                                                                            10 Hours

Time Series Analysis: Smoothing and Extrapolation of Time Series, Simple Extrapolation Models, Smoothing and Seasonal Adjustment; Properties of Stochastic Time Series: Characterizing Time Series: The Autocorrelation Function, Stationarity, Random Walk, Cointegrated Time Series; Linear Time Series: Moving Average Models, Autoregressive Models, Mixed Autoregressive and Moving Average Models, Homogeneous Non-Stationary Processes: ARIMA Models, Box-Jenkins Methodology, Specification of ARIMA Models, SARIMA, ARMAX Mode

Module 5                                                                                                            10 Hours

Forecasting with Time Series Models: Computing a Forecast, The Forecast Error, Properties of ARIMA Forecasts, Causality, Exogeneity, VAR, Impulse Response Functions, Volatility Measurement, Modeling and Forecasting: The ARCH Process, The GARCH Process

Module 6                                                                                                            10 Hours

Qualitative Forecasting Techniques: Survey and Opinion Polling Techniques, Exponential Smoothing and Other Advanced Techniques, Barometric Techniques, Leading, Lagging and Coincident Economic Indicators, Diffusion and Composite Indexes, Accuracy of Forecast, Short Run Forecast, Long Term Forecast, Use of Software Packages for Forecasting.