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Econometrics-book.aux
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Econometrics-book.aux
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\relax
\@writefile{toc}{\contentsline {part}{I\hspace {1em}The basics}{7}}
\@writefile{toc}{\contentsline {chapter}{\numberline {1}How to best use this book}{9}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {2}What is econometrics?}{11}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {3}Estimators and their purpose}{13}}
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\@writefile{toc}{\contentsline {section}{\numberline {3.1}Chapter mission statement}{13}}
\@writefile{toc}{\contentsline {section}{\numberline {3.2}The goal of this chapter}{13}}
\@writefile{toc}{\contentsline {section}{\numberline {3.3}What is an estimator, and why should we care?}{13}}
\@writefile{lof}{\contentsline {figure}{\numberline {3.1}{\ignorespaces The estimation process.\relax }}{14}}
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\@writefile{toc}{\contentsline {section}{\numberline {3.4}Models}{15}}
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\newlabel{eq:Estimators_modelWageExperienceExample}{{3.2}{16}}
\@writefile{lof}{\contentsline {figure}{\numberline {3.2}{\ignorespaces Left: the normal model for IQ. Right: the linear model between experience and wages, with the error terms $\epsilon _i$ indicated as vertical deviations from the straight line.\relax }}{17}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3.3}{\ignorespaces A normal distribution for IQ, with mean 100 and variance 100. The blue area represents the probability of obtaining an observation more extreme than 120.\relax }}{18}}
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\@writefile{toc}{\contentsline {section}{\numberline {3.5}Sampling distributions}{18}}
\newlabel{eq:Estimators_wageParentalEducationEstimand}{{3.3}{19}}
\@writefile{toc}{\contentsline {section}{\numberline {3.6}Good properties of an estimator}{20}}
\@writefile{toc}{\contentsline {section}{\numberline {3.7}The central limit theorem}{20}}
\newlabel{sec:Estimators_CLT}{{3.7}{20}}
\@writefile{toc}{\contentsline {section}{\numberline {3.8}Econometrics: GM conditions}{20}}
\@writefile{toc}{\contentsline {part}{II\hspace {1em}Cross sectional data: useful and important}{21}}
\@writefile{toc}{\contentsline {chapter}{\numberline {4}Ordinary Least Squares: what is it, and when to use it?}{23}}
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\newlabel{chap:OLS}{{4}{23}}
\@writefile{toc}{\contentsline {chapter}{\numberline {5}How to make conclusions - an introduction to hypothesis testing}{25}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {6}How to interpret regression results}{27}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {7}Testing a model - does it work?}{29}}
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\@writefile{toc}{\contentsline {section}{\numberline {7.1}Hypothesis tests}{29}}
\@writefile{toc}{\contentsline {section}{\numberline {7.2}Replicate data generation}{29}}
\@writefile{toc}{\contentsline {chapter}{\numberline {8}Testing the Gauss-Markov assumptions, and what to do if they are violated}{31}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {9}Instrumental variables: allowing inference in difficult circumstances}{33}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {10}Monte Carlo: How to test the quality of an estimator}{35}}
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\@writefile{toc}{\contentsline {part}{III\hspace {1em}Time series: harder to master, but necessary}{37}}
\@writefile{toc}{\contentsline {chapter}{\numberline {11}Why and how do we need to think about time series differently to cross sectional?}{39}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {12}The basic building blocks of time series models: autoregressive and moving averages}{41}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {13}Testing for stationarity and what to do with non-stationary data}{43}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {15}An introduction to models for real processes: partial adjustment and error-correction models}{47}}
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\@writefile{toc}{\contentsline {part}{IV\hspace {1em}Panel data: the best of both worlds}{49}}
\@writefile{toc}{\contentsline {chapter}{\numberline {16}The benefits of panel data}{51}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {17}Why do we need more estimators? An introduction to First Differences and Fixed Effects}{53}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {18}The poor relation: Random Effects}{55}}
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\@writefile{toc}{\contentsline {part}{V\hspace {1em}A simple new paradigm in estimation: Maximum Likelihood}{57}}
\@writefile{toc}{\contentsline {chapter}{\numberline {19}The flaws in the Linear Probability Model}{59}}
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\@writefile{toc}{\contentsline {chapter}{\numberline {20}Beautifully simple: An introduction to Maximum Likelihood}{61}}
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