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Estimating the OLS Model: Introduction to Financial Economics Research Methods

This lecture provides an introduction to estimating the OLS Model in financial economics research. Topics covered include basic statistics, the OLS regression, and analyzing the OLS model.

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Estimating the OLS Model: Introduction to Financial Economics Research Methods

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  1. 178.280 Research Methods in Financial Economics Lecture 2- Estimating the OLS Model

  2. Introduction Tough guys don’t do math. Tough guys fry chicken for a living. -- Jaime Escalante (Stand and Deliver, 1988).

  3. Administration Office Hours Computer Labs Readings: Chapter 1, 2 Simple OLS Model Objectives this week- Find Eviews on the computer system Practice working with data. Estimate a simple OLS Model Outline

  4. Administration • Tutorials and Labs start this week. • Signup sheets are still available. • Note: Labs are compulsory • Web site: • http://www.massey.ac.nz/~bjmoyle/mu/teach.html • WebCT • Office hours will be announced this week.

  5. We use statistical tools in this paper. but it is not a course in statistics. We estimate the value of certain parameters. E.g. mean, regression-coefficient. We signal our uncertainty about the parameter with ‘spreads’. Examples Variance Standard Deviation Standard error This forms basis of hypothesis tests. Basic Stats

  6. Recap • The main difference between statistics and other maths, is answers will have 2 dimensions • In normal algebra, variables combine to produce an explicit solution. • In statistics, we think in 2 dimensions • What we think the value of something is • How confident we are in that estimate

  7. The OLS regression measures the relationship between 2 (or more) variables. One variable is the dependentvariable (Y). One variable is the explanatory variable (X). We estimate the relationship with the coefficient B1. We measure the uncertainty about this estimate with its standard-error. The OLS Regression

  8. Aside • OLS uses a least squaresmethod. • It minimises the sum of the square of the residuals. • The estimate of Y consists of • Constant • Slope (beta)

  9. The model has a deterministic part. That part of the value of Y we can successfully predict Stochastic part That part of Y we can’t explain or predict. The stochastic part contains Measurement errors Missing variables Genuine random effects Non-linearities in the relationship. Analysing the OLS Model

  10. General Equation Dependent Variable Explanatory Variable Constant Stochastic portion Deterministic portion

  11. What is B1? • How much Y changes, if we change X (all other things constant). • It is also the slope of the regression line. • Hence, it is dY/dX • Note • The slope changes if the units we measure X and Y change. • The slope approximates a derivative.

  12. Eviews Tool Bar • The Eviews window has: • Tool bar • Top-pane (for manipulating variables) • Lower Scroll-bar (output may appear there) Top Pane Scroll Bar

  13. Data New pane opens

  14. The data file is trade.wf1 We will estimate an ‘Absorption Model’ for NZ. This uses National Income Accounting procedures. Y=C+I+G+X-M Hence Y-C-I-G=X-M Add/Substract TX from both sides (Y-TX-C)-I+(TX-G)=(X-M)+TX-TX Lab Introduction

  15. Absorption Model • Note- Y-TX-C equals Savings (S) • So • (S-I)+(TX-G)=(X-M) • Net Private Savings + Net Public Savings = Current Account Balance • Net National Savings (NNS) = CAB • Implication: Economies with low savings rates (deficits) will run trade deficits.

  16. C- Household consumption on final goods and services I- Investment G- Government Spending on goods and services TX- Taxes X- Exports M- Imports (removed for double-counting purposes). S- Household Income, less Consumption Definitions

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