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Tag Archives: Statistics & Econometrics

20 Best Econometrics Blogs and Websites in 2024

20 Best Econometrics Blogs and Websites in 2024 Yours truly, of course, feels truly honoured to find himself on the list of the world’s 20 Best Econometrics Blogs and Websites. 1. The Stata Blog 3. Cambridge Econometrics Blog 9. How the (Econometric) Sausage is Made 14. Lars P Syll Pålsson Syll received a PhD in economic history in 1991 and a PhD in economics in 1997, both at Lund University. He became an associate professor in economic history in 1995 and...

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Econometrics — a second-best explanatory practice

Econometrics — a second-best explanatory practice While appeal to R squared is a common rhetorical device, it is a very tenuous connection to any plausible explanatory virtues for many reasons. Either it is meant to be merely a measure of predictability in a given data set or it is a measure of causal influence. In either case it does not tell us much about explanatory power. Taken as a measure of predictive power, it is limited in that it predicts...

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The 20 Best Econometrics Blogs and Websites

The 20 Best Econometrics Blogs and Websites Yours truly, of course, feels truly honoured to find himself on the list of the world’s 20 Best Econometrics Blogs and Websites. 1. The Stata Blog 2. Bruno Rodrigues 7. Eran Raviv Blog Statistics and Econometrics 9. How the (Econometric) Sausage is Made 14. Lars P Syll Pålsson Syll received a PhD in economic history in 1991 and a PhD in economics in 1997, both at Lund University. He became an associate professor...

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Econometric modeling and inference

Econometric modeling and inference The impossibility of proper specification is true generally in regression analyses across the social sciences, whether we are looking at the factors affecting occupational status, voting behavior, etc. The problem is that as implied by the three conditions for regression analyses to yield accurate, unbiased estimates, you need to investigate a phenomenon that has underlying mathematical regularities – and, moreover,...

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Why quasi-experimental evaluations fail

Why quasi-experimental evaluations fail Evaluation research tends to be method-driven. Everything needs to be apportioned as an ‘input’ or ‘output’, so that the programme itself becomes a ‘variable’, and the chief research interest in it is to inspect the dosage in order to see that a good proper spoonful has been applied … The quasi-exprimental conception is again deficient. Communities clearly differ. They also have attributes that are not reducible to...

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Design-based vs model-based inferences

Design-based vs model-based inferences Following the introduction of the model-based inferential framework by Fisher and the introduction of the design-based inferential framework by Neyman [and Pearson], survey sampling statisticians began to identify their respective weaknesses. With regard to the model-based framework, sampling statisticians found that conditioning on all stratification and selection/recruitment variables, and allowing for their...

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Are RCTs — really — the best way to establish causality?

Are RCTs — really — the best way to establish causality? The best method is always the one that yields the most convincing and relevant answers in the context at hand. We all have our preferred methods that we think are underused. My own personal favorites are cross-tabulations and graphs that stay close to the data; the hard work lies in deciding what to put into them and how to process the data to learn something that we did not know before, or that...

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Statistical assumptions and racial bias

Statistical assumptions and racial bias Our analysis indicates that existing empirical work in this area is producing a misleading portrait of evidence as to the severity of racial bias in police behavior. Replicating and extending the study of police behavior in New York in Fryer (2019), we show that the consequences of ignoring the selective process that generates police data are severe, leading analysts to dramatically underestimate or conceal entirely...

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