This is my handwritten summary. It contains: - all the mandatory chapters from the book - some self-written bonus explanations to clarify difficult matters. - tips and tricks from the tutorial sessions. I also made sure that what was written in the lecture slides corresponded with the summary so th...
Introduction to Econometrics
J.M. Wooldridge, Summary by Merijn
, To all those that read my summary. The book I have summarized here, plain
simply sucks. It is hard to read, difficult to understand and is very presumptuous
in its reasoning.
That’s why you’ll find a lot of extra parts in this summary, or restructurings of
chapters or even completely new paragraphs just to explain things.
Only then, was I able to create a concise and understandable summary for
myself and hopefully for you too.
Have fun while reading and excuse me for profanity here and there, it’s
sometimes just a really difficult mess 😊
,2 CONTENTS
3 The simple regression model...............................................................................................................6
3.1 Defining the simple regression model..........................................................................................6
3.1.1 Proving Exogeneity................................................................................................................6
3.1.2 Proving how exogeneity works..............................................................................................8
2.4 Units of measurement and functional form.................................................................................9
3.1.3 The effect of changing units of measurement on OLS statistics.............................................9
3.1.4 Incorporating nonlinearities in simple regression..................................................................9
3.1.5 So why the hell do we still call this a linear regression?......................................................10
4 Multiple regression analysis: estimation...........................................................................................10
4.1 Why go for multiple regression?.................................................................................................10
4.2 How multiple regression works..................................................................................................10
11 OLS and time-series data.................................................................................................................12
11.4 Using highly persistent time series in regression analysis.........................................................12
4.2.1 When something is strongly dependent..............................................................................12
4.2.2 Strong and weak dependence.............................................................................................13
4.2.3 Tricks to solve the issues with high persistence...................................................................13
18 Advanced time series topics............................................................................................................13
18.2 Testing for Unit Roots...............................................................................................................13
4.2.4 The Dickey Fuller test...........................................................................................................13
4.3 Spurious regression....................................................................................................................14
4.4 Cointergration and Error correction models...............................................................................14
4.5 Forecasting.................................................................................................................................14
4.5.1 Understanding how forecasting errors work.......................................................................14
4.5.2 Two types of predicting the future: Martingale and Exponential Smoothing......................15
4.5.3 Types of regression models used for forecasting.................................................................15
4.5.4 One-Step-Ahead Forecasting and its forecast interval.........................................................16
4.5.5 Granger causality.................................................................................................................16
4.5.6 Comparing one-step-ahead forecasts..................................................................................17
4.5.7 Multi-step-ahead forecasts..................................................................................................17
4.5.8 Forecasting trending, seasonal and integrated processes...................................................19
13 Pooling cross sections across time: Panel Data methods.................................................................19
4.6 Obtaining pooled cross-sectional data by adding time dummies...............................................19
4.6.1 The intuition........................................................................................................................19
, 4.6.2 Using the Chow test.............................................................................................................20
4.7 Difference-in-difference estimators............................................................................................22
4.7.1 The intuition........................................................................................................................22
4.7.2 Obtaining the variable through a regression analysis..........................................................22
4.7.3 How this relates to natural experiments..............................................................................24
4.8 Two-period panel data analysis..................................................................................................24
4.8.1 Pooled OLS and OVB............................................................................................................24
4.8.2 Controlling for time-unspecific factors................................................................................25
4.9 Policy analysis with two-period data..........................................................................................26
4.10 Differencing with more than two time periods.........................................................................26
4.10.1 Using a Chow test on this type of differencing..................................................................26
5 Advanced panel data methods..........................................................................................................28
5.1 Fixed effects estimation..............................................................................................................28
5.1.1 How this estimation works..................................................................................................28
5.1.2 Including dummies in fixed estimator regression................................................................28
5.1.3 To first-difference or to demean?........................................................................................29
5.1.4 What happens to fixed effects with unbalanced panels......................................................29
5.2 Random effects models..............................................................................................................29
5.2.1 The intuition........................................................................................................................29
5.2.2 The composite error term and serial correlation.................................................................30
5.2.3 Implications of using the theta............................................................................................31
5.2.4 To random effects or not to random effects........................................................................31
5.3 The correlated random effects approach...................................................................................31
9 More specification on data issues.....................................................................................................32
9.2 Using proxy variables for unobserved explanatory variables.....................................................32
9.4 Measurement errors...................................................................................................................33
5.3.1 Measurement errors in the dependent variable..................................................................33
5.3.2 Measurement errors in the independent variable...............................................................33
15 Instrumental Variables Estimation and 2SLS....................................................................................35
5.4 Identifying the instrumental variable (IV)...................................................................................35
5.4.1 Finding the right IV..............................................................................................................35
5.4.2 How the IV works.................................................................................................................35
5.4.3 Using instrumental variables to infer...................................................................................36
5.4.4 What happens when you use a bad instrumental variable..................................................36
5.5 Using IV in multiple regression...................................................................................................36
5.6 Two stage least squares..............................................................................................................37
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