Tag Archives: Statistics & Econometrics

Don’t trust summary statistics alone When teaching statistics and econometrics yours truly always stress the importance of data visualization. The thirteen datasets shown above is a great illustration of why one should always plot data. They all have the same summary statistics — mean, standard deviation, correlation coefficient — but some are dinosaurs and others are stars … Conclusion: never trust summary statistics alone! Advertisements...

The spectacular failure of DSGE models In most aspects of their lives humans must plan forwards. They take decisions today that affect their future in complex interactions with the decisions of others. When taking such decisions, the available information is only ever a subset of the universe of past and present information, as no individual or group of individuals can be aware of all the relevant information. Hence, views or expectations about the future,...

Cholesky decomposition (student stuff)  [embedded content] Advertisements

The problem with unjustified assumptions An ongoing concern is that excessive focus on formal modeling and statistics can lead to neglect of practical issues and to overconfidence in formal results … Analysis interpretation depends on contextual judgments about how reality is to be mapped onto the model, and how the formal analysis results are to be mapped back into reality. But overconfidence in formal outputs is only to be expected when much labor has...

Textbooks problem — teaching the wrong things all too well It is well known that even experienced scientists routinely misinterpret p-values in all sorts of ways, including confusion of statistical and practical significance, treating non-rejection as acceptance of the null hypothesis, and interpreting the p-value as some sort of replication probability or as the posterior probability that the null hypothesis is true … It is shocking that these errors seem...

Monte Carlo simulation on p-values (wonkish) In many social sciences p values and null hypothesis significance testing (NHST) are often used to draw far-reaching scientific conclusions – despite the fact that they are as a rule poorly understood and that there exist altenatives that are easier to understand and more informative. Not the least using confidence intervals (CIs) and effect sizes are to be preferred to the Neyman-Pearson-Fisher mishmash approach...

Factor analysis — like telling time with a stopped clock Exploratory factor analysis exploits correlations to summarize data, and confirmatory factor analysis — stuff like testing that the right partial correlations vanish — is a prudent way of checking whether a model with latent variables could possibly be right. What the modern g-mongers do, however, is try to use exploratory factor analysis to uncover hidden causal structures. I am very, very interested...

The one place that preregistration is really needed … is if you want clean p-values. A p-value is very explicitly a statement about how you would’ve analyzed the data, had they come out differently. Sometimes when I’ve criticized published p-values on the grounds of forking paths, the original authors have fought back angrily, saying how unfair it is for me to first make an assumption about what they would’ve done under different conditions, and then make conclusions based on...

Some methodological perspectives on statistical inference in economics Causal modeling attempts to maintain this deductive focus within imperfect research by deriving models for observed associations from more elaborate causal (‘structural’) models with randomized inputs … But in the world of risk assessment … the causal-inference process cannot rely solely on deductions from models or other purely algorithmic approaches. Instead, when randomization is...

Simpson’s paradox, Trump voters and the limits of econometrics [embedded content] From a more theoretical perspective, Simpson’s paradox importantly shows that causality can never be reduced to a question of statistics or probabilities, unless you are — miraculously — able to keep constant all other factors that influence the probability of the outcome studied. To understand causality we always have to relate it to a specific causal structure. Statistical...