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… is set out over the fold. I’m confident readers who take a little time to think about it will realise it’s far superior to existing policy, and to any alternative proposed so far. (Previously posted at Crooked Timber in 2011) Share this:Like this:Like Loading...

 by New Deal democrat Weekly Indicators for May 3 – 7 at Seeking Alpha My Weekly Indicators post is up at Seeking Alpha. YoY comparisons remain ridiculous, so seasonally adjusted data, plus comparisons with 2019 where available are more informative. That being said, all of the coincident and short leading indicators point towards continued strong expansion. The longer leading forecast is much murkier, partly because of some sharp...

 by New Deal democrat The long leading indicator of credit conditions has just been updated by the Fed The easing or tightening of credit conditions has a good track record as a long leading indicator, giving us information about whether the economy will be expanding or contracting in 12+ months. The Fed just updated its Senior Loan Officer Survey for Q1, which covers both the easing or tightening of credit supply on the one hand, and...

Tags: open thread

from Asad Zaman Brief History of Econometrics: Launched in early 20th Century by Ragnar Frisch, econometric methodology was strongly shaped by the Cowles Commission (CC) in the 1960’s. The CC approach relied on structural equations, which embodied causal information known in advanced to the researcher. The goal was estimation of causal effects, and not discovery or assessment of the hypothesized causal structures. The oil shock of the 1970’s led to dramatic failures of macroeconomic...

Source: International Monetary Fund. “As the chart shows, China’s economy first passed the U.S. in 2017. It is projected to be more than 16 percent larger this year, and by 2025 is projected to be almost 40 percent larger by 2025.” Dean Baker

from Lars Syll There are three fundamental differences between statistical and causal assumptions. First, statistical assumptions, even untested, are testable in principle, given sufficiently large sample and sufficiently fine measurements. Causal assumptions, in contrast, cannot be verified even in principle, unless one resorts to experimental control. This difference is especially accentuated in Bayesian analysis. Though the priors that Bayesians commonly assign to statistical...