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	<title>BookPasta.net &#187; Econometrics</title>
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	<link>http://bookpasta.net</link>
	<description>and eBookz for all</description>
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		<title>Design of Observational Studies</title>
		<link>http://bookpasta.net/blog/2010/01/18/design-of-observational-studies/</link>
		<comments>http://bookpasta.net/blog/2010/01/18/design-of-observational-studies/#comments</comments>
		<pubDate>Mon, 18 Jan 2010 21:32:36 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Econometrics]]></category>
		<category><![CDATA[Statistics]]></category>
		<category><![CDATA[observational study]]></category>
		<category><![CDATA[sensitivity analysis]]></category>

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		<description><![CDATA[The concepts of causal inference in experiments and observational studies are introduced using the elementary mathematics of independent coin flips to determine treatment assignment The basic tools of multivariate matching – such as propensity scores, optimal matching, full matching, fine balance, risk set matching – are introduced with many examples and with reference to implementation in R The key source of uncertainty in an observational study is possible bias from covariates that were not measured. The ability of competing designs to separate treatment effects from unmeasured biases – that is, the design sensitivity – is discussed in detail for the first time in book form An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided [...]]]></description>
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		<title>Introduction to the Mathematical and Statistical Foundations of Econometrics</title>
		<link>http://bookpasta.net/blog/2009/11/17/introduction-to-the-mathematical-and-statistical-foundations-of-econometrics/</link>
		<comments>http://bookpasta.net/blog/2009/11/17/introduction-to-the-mathematical-and-statistical-foundations-of-econometrics/#comments</comments>
		<pubDate>Wed, 18 Nov 2009 01:02:11 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Econometrics]]></category>
		<category><![CDATA[measurement]]></category>
		<category><![CDATA[statistic]]></category>
		<category><![CDATA[stochastic]]></category>

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		<description><![CDATA[This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory. Some chapters have their own appendices containing the more advanced topics and/or difficult proofs. Moreover, there are three appendices with material that is supposed to be known. Appendix I contains a comprehensive review of linear algebra, including all the proofs. Appendix II reviews a variety of mathematical topics and concepts that are used throughout the main text, and Appendix III reviews complex analysis. Therefore, this book is uniquely self-contained. • Rigorous and comprehensive overview of the mathematical and statistical foundations of econometrics • The focus is on understanding ‘why’ rather than ‘how’, therefore all the proofs are provided • Appendices contain enough advanced material to make the book suitable for a specialty course in econometric theory]]></description>
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