Practical Econometrics for Managerial Decision Making

INTRODUCTION
This Practical Econometrics for Managerial Decision Making training course focuses on performing high-level multivariate econometric analysis using a range of business and economic data – with an emphasis on design, analysis, and drawing sound inferences to support strategic and operational decision-making. It’s no longer specialty PhDs with $10,000 software packages doing high-end econometrics.

OBJECTIVES
At the end of this training course, you will learn to:
• Design-produce an original research study
• Collect and format various types of data
• Perform different models of multivariate econometric analyses
• Analyze detailed statistical output from econometric model-software
• Draw inferences to support high-level managerial decision-making
• Write a detailed, yet succinct, executive summary of research findings

COURSE OUTLINE
DAY 1
• Model Design, Hypotheses, Variables, Structure, Outcomes
• Quantitative and Qualitative Inputs
• Applications: From Wall Street to Marketing to Production to Consumer Behavior
• Software Options
• Linking Models and Confirmation Metrics

DAY 2
• Cross Sectional Samples
• Time Series Sequences
• Longitudinal Tracking
• Pooled Cross-Sectional Aggregation
• Primary Data Costs and Acquisition
• Secondary Data Costs and Acquisition
• Descriptive Outcomes vs. Predictive Outcomes
• Dummy Variables / Indicators / Surrogates
DAY 3
• Targeted Outcomes Determine Input Formations
• Single-Variable vs. Multi-Variable Descriptors and Predictors
• Punctuated Trending vs. Real-time Fluidity
• Static Formations vs. Dynamic-Changing-Active Learning Models
• Correlation and Association vs. Cause-And-Effect
• Building Real Models for Delegates’ Firms, Industries, Markets
DAY 4
• Categorizing Decision Areas and Coordinating Data Availability
• Micro-economic vs. Macro-economic Decisions
• Indicators, Lagged Variables, Barometers / Bellwethers
• Running Several Rounds of Differing Regressions
• Managing Databases of Targeted Variables
• Problems of Multi-collinearity
• Problems of Autocorrelation
DAY 5
• Drawing Inferences Rather than Conclusions
• Caveats of Explaining Variance
• Individual and Team Presentations and Discussion-Interaction-Critique
• Confidence Intervals in Econometric Forecasts
• Packaging Analysis-results for Optimum Explanation
• Problems with Overreach from Statistical Outputs
• Personal-Managerial Bias Impacts Interpretation
• Distilling Data Output into Actionable Intel
• Disseminating Data Output for Maximum Decision Making Impact