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Overview

• Review of the linear regression model: estimation and hypothesis testing; • Dynamic regression models: distributed lag and autoregressive models; • Non-stationarity and testing for unit roots; • Modelling long-run relationships: cointegration; • Modelling volatility: univariate ARCH and GARCH models; • Regression analysis using panel data.

Assessment Strategy

Threshold c- to c+ (50-59%): Satisfactory performance. No major omissions or inaccuracies in the deployment of information/skills. Some grasp of theoretical/conceptual/practical elements. Integration of theory/practice/information present intermittently in pursuit of the assessed work's objectives. Knowledge of key areas/principles only. Weaknesses in understanding of some areas. Limited evidence of background study. Answer inadequately focused on task and with some irrelevant material and poor structure. Arguments presented but lack coherence. Minor factual/computational errors. Lacking original interpretation.

Good B- to B+ (60-69%): Good performance. Most of the relevant information accurately deployed. Good grasp of theoretical/conceptual/practical elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Evidence of the use of creative and reflective skills. Understands most but not all concepts/issues. Evidence of background study. Focused answer with good structure. Arguments presented coherently. Mostly free of factual errors. Some limited original interpretation. Well known links between topics are described. Problems addressed by existing methods/approaches. Good presentation with accurate communication

Excellent standard: 70+ An outstanding performance, exceptionally able. The relevant information accurately deployed. Excellent grasp of theoretical/conceptual/practice elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Strong evidence of the use of creative and reflective skills.

Learning Outcomes

• Critically evaluate academic literature with an empirical content in economics, finance and business.

• Demonstrate a critical awareness of the implicit assumptions and the limitations underlying empirical models based on financial and business data.

• Demonstrate an understanding of recent developments in time series econometrics, and their implications for modelling the returns and volatility of financial assets.

• Demonstrate an understanding of the key theoretical underpinnings and practical applications of econometrics for financial and business analysts.

• Design, estimate and evaluate econometric models to financial and business data sets using the Stata econometrics software package.