tag:blogger.com,1999:blog-2198942534740642384.comments2017-08-22T06:16:51.666-07:00Econometrics Beat: Dave Giles' BlogDave Gileshttp://www.blogger.com/profile/05389606956062019445noreply@blogger.comBlogger3779125tag:blogger.com,1999:blog-2198942534740642384.post-64941798524727542652017-08-21T23:37:24.636-07:002017-08-21T23:37:24.636-07:00Ah right, thank you!Ah right, thank you!Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-9140236046005379402017-08-19T03:37:49.270-07:002017-08-19T03:37:49.270-07:00Yes, see step 11.Yes, see step 11.Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-6491439581177692622017-08-19T03:03:04.926-07:002017-08-19T03:03:04.926-07:00Dear Prof. Giles
By critical values, do you mean t...Dear Prof. Giles<br />By critical values, do you mean the ones from the chi square table?<br />Thanks in advance.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-83127293374598341872017-08-18T13:08:47.590-07:002017-08-18T13:08:47.590-07:00Musefiu - I'll see what I can do.
DGMusefiu - I'll see what I can do.<br /><br />DGDave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-69503169019034340772017-08-18T10:52:24.799-07:002017-08-18T10:52:24.799-07:00Dear Prof. Gile,
I have been following your blog f...Dear Prof. Gile,<br />I have been following your blog for quite sometimes now, though i don't usually ask questions and it has really been helpful to me as a postgraduate student in a developing country.<br />Prof. can you help me provide a blog on the new approach to non-linear Unit root tests (such as that of Kruse, 2011 and Kilic, 2011). These two approaches are what am working on in my Ph.D. thesis. Kindly assist sir. <br /><br />Kind Regards <br />Adeleke M.AMusefiu Adelekehttps://www.blogger.com/profile/03296811247164268639noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-27999002788782140482017-08-15T14:49:05.287-07:002017-08-15T14:49:05.287-07:00Thanks you sir. Very good explanation.
Thanks you sir. Very good explanation.<br />SALEShttp://www.uol.com.brnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-41743060664519727512017-08-04T04:51:59.500-07:002017-08-04T04:51:59.500-07:00Thanks Mark!Thanks Mark!Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-51001525291139300232017-08-03T20:50:57.450-07:002017-08-03T20:50:57.450-07:00The best discussion on Andrew Gelman's blog is...The best discussion on Andrew Gelman's blog is in connection with this entry:<br /><br />http://andrewgelman.com/2017/03/04/interpret-confidence-intervals/<br /><br />Some good contributions there, esp. by Carlos Ungil and Daniel Lakeland.Mark Schafferhttp://ideas.repec.org/e/psc51.htmnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-15394184929803638722017-08-03T08:04:00.171-07:002017-08-03T08:04:00.171-07:00«if you were a Bayesian, then the whole idea of a ...«if you were a Bayesian, then the whole idea of a confidence interval will be meanngless, regardless of the sample size - because you'd have no interest in "repeated sampling", or the associated idea of the "sampling distribution".»<br /><br />I think that is an bizarre misdescription of bayesian approaches, as if they were "well one sample is plenty and then we bet the farm, because priors!".Blissexnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-90397949726258628452017-08-03T07:58:55.564-07:002017-08-03T07:58:55.564-07:00«I like to get students doing MC simulations nice ...«I like to get students doing MC simulations nice and early.»<br /><br />That is a really really good point. For example I found that myself and others only understand ("somewhat") the dreaded p-value if it is computed from a MC simulation, because the classic definition is in effect a double negative, not a constructive one.Blissexnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-23932731852124072422017-08-03T07:42:53.771-07:002017-08-03T07:42:53.771-07:00«The true value of the coefficient in this regress...«The true value of the coefficient in this regression model is a parameter. It's a constant, whose value we just don't happen to know. On the other hand, the (point) estimate of 1.1 is just one particular "realized" value of a random variable - generated using this one particular sample of data. An estimator is a formula - like the OLS formula in our example. Except in rather silly cases, this formula involves using the (random) sample data. So, an estimator is a function of the sample data - in othere words, what we call a statistic. When we apply this formula using a particular sample of data, we generate a number - a point estimate. Because an estimator is a function of the random data, it's random itself. Being a random variable, an estimator has a distribution function.»<br /><br />To me this looks like extremely loose and obfuscating terminology that gets so many people in trouble, for example can a "formula" be a "random variable" and have a "distribution function"? That's simply ridiculous. The way I learned it from some very clear thinking definettian subjectivists (but it is not a subjectivist point of view) is:<br /><br />* There is an algebra of arithmetic number and an algebra of stochastic numbers, and they are fundamentally different.<br /><br />* A "statistic" is a measure over a set of numbers, whether they be arithmetic or stochastic. The same formula for a measure can portend two different functions, one over arithmetic numbers, one over stochastic numbers.<br /><br />* Arithmetic numbers arise from populations, stochastic numbers from samples (under the hypothesis that the sampling process is ergodic, but I am not sure that is what a definettian subjectivist would say).<br /><br />* A measure on a sample is at the same time an arithmetic number with respect to the sample, and a stochastic number if *interpreted* as an estimate of the same measure on the population, while a measure on a population is always and only an arithmetic number.<br /><br />* Bonus point: it fantastically important (especially in studies of the political economy) to always ask what is the population from which a sample has been drawn, and whether the sampling process was indeed ergodic. And if you consider those two questions deeply enough, you end up a definettian subjectivist I guess :-).<br /><br />I do hope that I was not that loose conceptually or in terminology in the above, and that it reflects the insights I got from those clear thinking people.Blissexnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-57622927931892628002017-08-03T06:47:27.450-07:002017-08-03T06:47:27.450-07:00I will definitely be looking into this - thanks ag...I will definitely be looking into this - thanks again for alerting me (and other readers).Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-84356096868555581222017-08-03T06:45:02.702-07:002017-08-03T06:45:02.702-07:00Ah... hadn't noticed that! In 2011 I wasn'...Ah... hadn't noticed that! In 2011 I wasn't aware of "bet-proofness" either - I only learned about it from the M-N 2016 paper. But the concept has been around for decades, apparently. It's curious that it isn't more widely known.Mark Schafferhttp://ideas.repec.org/e/psc51.htmnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-74668767068748671652017-08-03T06:21:33.091-07:002017-08-03T06:21:33.091-07:00Mark - thanks for pointing this out! I'll chec...Mark - thanks for pointing this out! I'll check it out.(Note that my blog post was from 2011 - I promoted it recently because it was the 100'th anniversary of Friedman's birth.)Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-77331569994474404032017-08-03T06:13:23.347-07:002017-08-03T06:13:23.347-07:00Actually, there IS a way to interpret realized CIs...Actually, there IS a way to interpret realized CIs. The concept is “bet-proofness”. We had quite a good discussion about it over at Andrew Gelman's blog several months ago. I learned about the concept from a recent paper by Mueller-Norets (Econometrica 2016).<br /><br />Mueller-Norets (2016, published version, p. 2185):<br /><br />“Following Buehler (1959) and Robinson (1977), we consider a formalization of “reasonableness” of a confidence set by a betting scheme: Suppose an inspector does not know the true value of θ either, but sees the data and the confidence set of level 1−α. For any realization, the inspector can choose to object to the confidence set by claiming that she does not believe that the true value of θ is contained in the set. Suppose a correct objection yields her a payoff of unity, while she loses α/(1−α) for a mistaken objection, so that the odds correspond to the level of the confidence interval. Is it possible for the inspector to be right on average with her objections no matter what the true parameter is, that is, can she generate positive expected payoffs uniformly over the parameter space? … The possibility of uniformly positive expected winnings may thus usefully serve as a formal indicator for the “reasonableness” of confidence sets.”<br /><br />“The analysis of set estimators via betting schemes, and the closely related notion of a relevant or recognizable subset, goes back to Fisher (1956), Buehler (1959), Wallace (1959), Cornfield (1969), Pierce (1973), and Robinson (1977). The main result of this literature is that a set is “reasonable” or bet-proof (uniformly positive expected winnings are impossible) if and only if it is a superset of a Bayesian credible set with respect to some prior. In the standard problem of inference about an unrestricted mean of a normal variate with known variance, which arises as the limiting problem in well behaved parametric models, the usual [realized confidence] interval can hence be shown to be bet-proof."<br /><br />Full reference:<br /><br />Credibility of Confidence Sets in Nonstandard Econometric Problems<br />Ulrich K. Mueller and Andriy Norets (2016)<br />https://www.princeton.edu/~umueller/cred.pdf<br />http://onlinelibrary.wiley.com/doi/10.3982/ECTA14023/abstract<br /><br />Interesting stuff!<br /><br />--Mark<br />Mark Schafferhttp://ideas.repec.org/e/psc51.htmnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-21813538768676256242017-08-02T08:59:57.770-07:002017-08-02T08:59:57.770-07:00Excellent explanation... but sorry, but you're...Excellent explanation... but sorry, but you're really just parsing words here. If 95% of the intervals would cover the true value, then IMO it's not illogical at all to say that there's a 95% chance that any particular interval selected contains the true value. Yes, I get that the specific one we estimated either does or does not, but on average 95% is the best estimate we have of whether it does or does not.Billhttps://www.blogger.com/profile/15982661420006351208noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-77882416903742455772017-08-02T00:06:58.016-07:002017-08-02T00:06:58.016-07:00Dear Prof. Giles,
In a closed economy, saving = in...Dear Prof. Giles,<br />In a closed economy, saving = investment; S=I<br />So, if I regress I = a+b*S+u, and find b=0.8<br />This implies that if I rises by 0.8% when S rises by 1%. Alternatively, it implies that when I rises by 1%, S should rise by 1/b=1/0.8=1.25. If this interpretation is correct, then when I regress S=c+d*I+v, <br />Should not d be 1/b=1/0.8=1.25? <br /><br />Thank you<br /><br />SKAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-24521652093782250742017-08-01T21:45:00.668-07:002017-08-01T21:45:00.668-07:00I am grateful to all of you (Dave Giles and Richar...I am grateful to all of you (Dave Giles and Richard Morey et al) for explaining this so clearly. Thank you!Alan T.noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-73607341387674560302017-07-17T04:03:45.811-07:002017-07-17T04:03:45.811-07:00Yes, that's what's assumed in this example...Yes, that's what's assumed in this example.Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-62025977638528766892017-07-17T04:02:26.343-07:002017-07-17T04:02:26.343-07:00Dear Prof. Giles,
In the example, the gasoline pr...Dear Prof. Giles,<br /><br />In the example, the gasoline price is estimated on crude oil price. Does it mean that there is no opposite direction (from gasoline price to crude oil) because ARDL of the first equation assume exogenous independent? <br /><br />Many thanks for your detailed post about ARDL here.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-52811239475650615742017-07-13T06:23:53.498-07:002017-07-13T06:23:53.498-07:00I don't use Stata (unless I absolutely have to...I don't use Stata (unless I absolutely have to!) There are lots of Stata user groups out there where you can go for help. Good luck!Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-53548825841179528192017-07-13T01:39:27.555-07:002017-07-13T01:39:27.555-07:00Hi Prof. Giles,
I am performing Johansen Cointegr...Hi Prof. Giles, <br />I am performing Johansen Cointegration test on my time series data set. The problem is I am getting an error prompt on Stata ''the sample has gaps r(498)'' . What does this mean and what can be done about it ? Also note that my data set does not contain any missing data however on taking the first difference of the variables,some data seem to be missing. <br />Thanking you in anticipation . <br /><br />Shraddhahttps://www.blogger.com/profile/18387225857473960605noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-64240252759288044462017-07-03T19:34:26.160-07:002017-07-03T19:34:26.160-07:00Hamilton's paper fantastic. The alternative ap...Hamilton's paper fantastic. The alternative approach he proposes is very neat too! Adam Elderfieldhttps://www.blogger.com/profile/14269270642659046827noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-46577294709176721082017-07-03T16:41:49.855-07:002017-07-03T16:41:49.855-07:00Biased in finite samples, but still consistent.Biased in finite samples, but still consistent.Dave Gileshttps://www.blogger.com/profile/05389606956062019445noreply@blogger.comtag:blogger.com,1999:blog-2198942534740642384.post-82033552782211960332017-07-03T15:59:34.124-07:002017-07-03T15:59:34.124-07:00Professor Giles, in your earlier post, you mention...Professor Giles, in your earlier post, you mentioned how including lagged dependent variables as in ARDL models yields biased coefficient estimates. Does the Pesaran et al. (2001) approach not suffer from the same problem? And if so, does "Step 4" ensure coefficient estimates are not inconsistent as well?Davidhttps://www.blogger.com/profile/15569977052029142069noreply@blogger.com