Mar 25, 2024
The ONS podcast returns, this time looking at the importance of communicating uncertainty in statistics. Joining host Miles Fletcher to discuss is Sir Robert Chote, Chair of the UKSA; Dr Craig McLaren, of the ONS; and Professor Mairi Spowage, director of the Fraser of Allander Institute.
Transcript
MILES FLETCHER
Welcome back to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and to kick off this brand new season we're going to venture boldly into the world of uncertainty. Now, it is of course the case that nearly all important statistics are in fact estimates. They may be based on huge datasets calculated with the most robust methodologies, but at the end of the day they are statistical judgments subject to some degree of uncertainty. So, how should statisticians best communicate that uncertainty while still maintaining trust in the statistics themselves? It's a hot topic right now and to help us understand it, we have another cast of key players. I'm joined by the chair of the UK Statistics Authority Sir Robert Chote, Dr. Craig McLaren, head of national accounts and GDP here at the ONS, and from Scotland by Professor Mairi Spowage, director of the renowned Fraser of Allander Institute at the University of Strathclyde. Welcome to you all.
Well, Sir Robert, somebody once famously said that decimal points in GDP is an economist’s way of showing they've got a sense of humour. And well, that's quite amusing - particularly if you're not an economist - there's an important truth in there isn't there? When we say GDP has gone up by 0.6%. We really mean that's our best estimate.
SIR ROBERT
CHOTE
It is. I mean,
I've
come at this having
been a consumer of economic statistics for 30 years in
different
ways. I
started out as a journalist on the Independent and the Financial
Times writing about the new numbers as they were published each
day,
and then I had 10
years using them as an economic and fiscal
forecaster.
So I come at
this very much from the spirit of a consumer and
am
now obviously
delighted to be working with producers as well. And
you're
always I think,
conscious in those roles of the uncertainty that lies around
particular economic estimates. Now, there are some numbers that are
published, they are published once, and you are conscious that
that's
the number that stays
there. But there is uncertainty about how accurately that is
reflecting the
real world position and that's naturally the case. You then have
the world of
in
particular, the national accounts, which are
numbers, where you have initial estimates that the producer returns
to and updates as the information sets that you have available to
draw your conclusions develops over time. And it's very important
to remember on the
national accounts that that's not a bug, that's a feature of the system. And
what you're trying to do is to measure
a very
complicated set of transactions
you're
trying to do in three
ways, measuring what the economy produces, measuring incomes,
measuring expenditure. You do that in different ways
with information that
flows in at different
times.
So it's
a complex task and
necessarily the picture evolves. So I think from the
perspective of a user, it's important to be aware of the
uncertainty and it's important when you're presenting and
publishing statistics to help people engage with
that,
because if you are
making decisions based on statistics, if you're simply trying to
gain an understanding of what's going on in the economy or society,
generally speaking you shouldn't be betting the farm on the
assumption that any particular number is, as you say, going
to be right to
decimal places. And the more that producers can do to help people
engage with that in an informed and intelligent way, and therefore
mean that decisions that people take on
the basis of this more informed the
better.
MF
So it needs to be near enough to be
reliable, but at the same time we need to know about the
uncertainty. So how near is the system at
the moment as
far as these important indicators are concerned to getting that
right?
SRC
Well, I think there's an awful
lot of effort that goes into ensuring that you are presenting on
the basis of the information set that you have the best available
estimates that you can, and I think there's an awful lot
of effort that goes into thinking about quality, that thinks about quality
assurance when these are put together, that thinks about the
communication how they mesh in with the rest of the, for example, the economic
picture that you have, so you can reasonably assure yourself that
you're providing people with the best possible estimate that you
can at any given moment. But at the same time, you want to
try to guide people by saying, well, this is an
estimate, there's no guarantee that this is going
to exactly reflect the real world, the more that you can do to put
some sort of numerical context around that the more the reliable
basis you have for people who are using those numbers, and thinking
about as I say, particularly in the case of those statistics that
may be revised in future as you get more information.
You can learn things,
obviously from the direction, the size of revisions
to numbers that have happened in the past, in
order to give
people a sense of how much confidence they should place in any
given number produced at any given point in that cycle of evolution
as the numbers get firmer over time.
MF
If you're looking to use
the statistics to make some decision with your
business or personal life, where do you look for the small print?
Where do you look for the guidance on how reliable this number is
going to be?
SRC
Well, there's plenty of guidance published
in different
ways. It
depends, obviously on the specific statistics in question, but I
think it's very important for producers to ensure that when people
come for example to websites or to releases that have the headline
numbers that are going to be reported, that it's reasonably
straightforward to get to a discussion of where do these numbers
come from? How are they calculated? What's the degree of uncertainty that
lies around that arising from these things? And so not everybody is
obviously going to have an appetite for the technical discussion
there. But providing that in a reasonably accessible,
reasonably findable way, is important and I think a key principle
is that if you're upfront about explaining how numbers are
generated, explaining about the uncertainty that lies around them
in as quantified way as you can, that actually increases and
enhances trust in the underlying production and communication
process and in the numbers rather than undermining it.
I think you
have
to give the
consumers of these numbers by and large the
credit for
understanding that these things are only estimates and that
if you're upfront about that, and you talk
as intelligently and clearly as you can about the
uncertainties
- potential for revision, for example
- then that enhances people's
confidence. It doesn't undermine
it.
MF
You mentioned there
about
enhancing
trust and that's the crux of all
this.
At a
time we're told of growing public mistrust
in national institutions and so forth, isn't there a risk that the
downside of talking more about uncertainty in statistics is the more aware
people will become of it and the less those statistics are
going to be trusted?
SRC
I think in general, if you are
clear with people about how a number is calculated, the uncertainty
that lies around it, the potential for revision, how things have evolved in the
past -
that’s
not for
everybody, but
for most people - is likely to enhance their trust
and crucially, their understanding of the numbers that you're
presenting and the context that you're putting around those. So
making that available - as I say, you have to recognise
that different people will have different appetites for the
technical detail around this - then there are different ways of
presenting the uncertainty not only about, you know, outturn
statistics, but in my old gig around forecasts of where things are
going in the future and doing that and testing it out with your
users as to what they find helpful and what they don't is a
valuable thing to be
doing.
MF
You've been the
stats regulator for a little while
now. Do you think policymakers, perhaps under pressure to achieve certain
outcomes, put too much reliance on
statistics when it suits them, in
order to show
progress against some policy objective? I mean, do the limitations of statistics
sometimes go out of the window when it's convenient. What's your view of how well certainty
is being treated by those in government and
elsewhere?
SRC
Well, I think certainly in my time as a
forecaster, you were constantly reminding users of forecasters and
consumers of that, that again, they're based on the best available
information set that you have at the time. You explain where the
judgements have come from
but in particular, if you're
trying to set policy in order to achieve a target for a particular
statistic at some point in the future, for example, a measure of
the budget deficit, then having an understanding of the
uncertainty, the nature of it, the potential
size of it in that context, helps you avoid making promises that
it's not really in your power to keep with the best will in the
world, given those uncertainties. And sometimes that message is
taken closer to heart than at other
times.
MF
Time I think to bring in Craig now at this
point, as head of national accounts and the team that produces GDP
at the ONS to talk about uncertainty in the real world of
statistical production. With this specific example, Craig,
you're
trying to produce a
single number, one single number that sums up
progress or lack of it in the economy
as a whole.
What do you do to make the users of the statistics and the wider
public aware of the fact that you're producing in GDP one
very broad
estimate with a lot
of uncertainty built in?
CRAIG
MCLAREN
Thanks, Miles. I mean, firstly, the UK
economy - incredibly
complex isn't it? The last set of numbers,
we've
got 2.7 trillion
pounds worth of value.
So if you
think about how we bring all
of those
numbers together, then absolutely what we're doing is providing the best
estimate at the time and then we start to think about this
trade off between timeliness and accuracy.
So even when we bring all
of those data
sources together, we often balance between what can we understand
at the point of time, and then equally as we get more information
from our businesses and our data suppliers, we evolve our estimates to
understand more about the complex nature of the UK economy.
So where we do
that and how we do that it's looking quite closely
at
our data
sources.
So for
example, we do a lot of surveys about businesses, and that uses
data provided by businesses and that can come with a little bit of
a what we call a time lag. So clearly when we run
our monthly business surveys that's quite timely. We get that information quite
quickly. But
actually when
we want to understand more detail about the UK economy, we have
what we call structural surveys, and they're like our annual surveys. So over
time, it can take us a couple of years actually
to get a more
complete picture of the UK economy.
So in that
time, absolutely. We may revise the estimate. Some businesses might
say, well, we forgot about this. We're going to send you a revised
number. We look at quite closely about the interplay between all
the dynamics of the different parts
of the economy, and
then we confront the data set.
So I think by
bringing all this information together, both on the timeliness but
also as we get a more complete picture, we start to refine our
estimates.
So in
practice, what we do find is as we evolve our estimates, we
can monitor that. We do look quite closely at the
revisions of GDP, then we can produce analysis that helps our users
understand those revisions and then we quite heavily focus on the
need for rapid information that helps policymakers. So how can
policymakers take this in a short period
of
time,
but then we provide
this information to understand the revision properties of what we
would call that about how our estimates can change and evolve over
time as we get additional information going
forwards.
MF
So let's just look at the
specifics, just to help people understand
the process and how you put what you've just explained so well into
action. Craig, the last quarterly estimate of GDP showed the economy contracted
slightly.
CM
That's exactly right
Miles and I think where we do
produce our estimates in a timely basis, absolutely
they will be subject to revision
or more information as we get them.
So this is
why it's important, perhaps not to just focus on a single
estimate. And I know in our most recent year in the economy,
when that's all pretty
flat,
for example, or there's sort of a small fall, we do have a
challenge in our communication. And that becomes a little bit back
to the user understanding about how these numbers are compiled.
And
also perhaps
how can you
use additional information as part of
that?
So as I
mentioned the UK economy is very complex, GDP is a part of that, but we also
have other broader indicators as well.
So when we do
talk about small movements in the economy, we do need to think
about the wider picture alongside
that.
MF
Okay, so the last quarterly
estimate, what was the potential for
revision there? Just how big could that have
been?
CM
We don't formally produce what we call
range estimates at
the moment. We are working quite closely with colleagues
about how we might do that.
So if you
think about all the information that comes together to produce GDP,
some of that
is survey base
which will have a degree of perhaps error around it, but we also use
administrative data sources as well.
So we
have access to VAT
records anonymized of course, which we bring
in to our
estimates.
So the complex
nature around the 300 different data sources that we bring in to
make GDP means that having a range can be quite a statistical
challenge.
So what we do
is we can actually
look at our
historical record of GDP revisions, and by doing that, in
perhaps normal
times, are quite
unbiased. And by that, I mean we don't expect to see that to be
significant either way.
So we may
revise up by perhaps 0.1 or down by
0.1,
but overall, it's quite a sort of considered
picture and we don't see radical revisions to our
first estimates over time.
MF
You're saying that when revisions
happen they are as likely to be up as they are to be
down and there's no historical bias in there
either way, because presumably, if
there was that bias
detectable, you would have acted some time ago, to make sure it was
removed from the methodology.
CM
Exactly. Exactly.
MF
Just staying with this whole business of trying to make a very fast estimate because it is by international standards, a fast estimate of a very, very big subject. How much data in percentage terms would you say you’ve got at the point of that first estimate as a proportion of all the data you're eventually going to get when you produce your final number?
CM
It does depend on the
indicator Miles.
So
the UK is one of the
few countries in the world that produces monthly GDP.
So we are
quite rapid in producing monthly GDP. Robert did mention
in
the introduction of
this session that with monthly GDP we do an output
measure.
So this is
information we have quite quickly from businesses.
So our monthly
GDP estimate is based on one of the measures of the economy. So
that uses the output measure. We get that from very rapid surveys,
and that has quite a good coverage around 60 or 70% that we can get
quite quickly. But then as we confront with our different measures
of GDP, that's when the other sources come
in.
So we have our
expenditure measure which takes a bit longer and then we have our
income measure as well.
So we have
this process in the UK working for a monthly GDP which is quite
rapid. We then bring in additional data sources and each of these
measures have their own strengths and weaknesses until we can
finally confront them fully in what we call an annual framework.
And then often that takes us a couple of years to fully bring
together all those different data sources so we can see the
evolution of our GDP estimates as additional data comes in.
MF
Now looking back to what happened
during the pandemic, of course, we saw this incredible downturn in
the economy as the effects of lockdown took effect on international
travel that shuddered to a halt for a
while and
everyone was staying at home for long periods. The ONS said at that point, it was the
most significant downturn it had ever recorded. But then that was
closely followed of course when those restrictions were eased by
the most dramatic recovery ever recorded. Just how difficult was it
to precisely manage the sheer scale of that change, delivered over
quite a short
period,
relatively
speaking, just
how good a job did the system do under those very testing
circumstances?
CM
It was incredibly
challenging and I think not just for official
statistics of course but for a range of outputs as well.
Viewing it in context
now, I think when the economy is
going relatively stable, perhaps a 0.1 or 0.2 change, we might start to
be a bit nervous if we saw some revisions to that but
if you think about I believe at the time was around 20%
drop in activity and actually the
challenge of ensuring that our surveys were capturing what was
happening in the economy in the UK, and in the ONS we stood up some additional
surveys to provide us with additional information so we could
understand what was happening. We still have that survey
that's
a fortnightly
survey.
So the
challenge that we had was to try and get the information in near
real time to provide us with the confidence and
also obtaining
information from businesses that are not at their place of
work,
so they
weren't
responding to our
surveys.
So we
had to
pivot
to using perhaps
telephone, collecting information in
a different
way really to
understand the impact the economy.
So when we
look back now, in retrospect, perhaps a 20% drop should that have been 21
or 22%. It's all relative to the size of the drop is my
main point I would make.
So in the
context of providing the information at the time, we were quite
fortunate in the survey on the data collection front to really have
a world leading survey for businesses that provided
that information in near real time,
which we could then use to understand the impacts on
different
parts of the
UK economy. And I think now when we get new information
in an annual basis,
we can go back and just confront that data set and understand how
reliable those estimates were, of course.
MF
Of course the UK was not alone in
making some quite significant revisions subsequently to its initial
estimates, what was done, though, at the time to let the users of
the statistics know that because of those circumstances, which
were
so unusual, because
the pace of change you were seeing was so dramatic, that perhaps
there was a need for special caution around what the data was
seeming to say about the state of the economy?
CM
Exactly, and it was unprecedented
of course as well.
So in our
communication and coming back to how we communicate
statistics, and
also the
understanding as well. We added some additional phrasing, if you like
Miles, to ensure people did
sort of
understand and perhaps acknowledge
the fact that in
times like this, there is an additional degree of uncertainty.
So the
phrasing becomes very important, of course to reflect that these
are estimates they're our first estimate at the time,
they perhaps
will be
maybe more
revised than
perhaps
typically we
would expect to happen.
So the
narrative and
communication and phrasing, and the use of the term
‘estimate’, for example, became incredibly important
in the
time of the pandemic.
And it's also incredibly important in the
context of smaller movements as well.
So while we
had this large
impact on
COVID, it was our best estimate at the time, and I think it's important to reflect
that,
and as we get
more and more
understanding of our
data sources, then those numbers will be revised.
So what we did
do was really make sure that was front and centre to our
communications just to reflect the fact that there can be
additional
information after
the
fact but this
is the best estimate at the time and there's a degree of uncertainty.
And we've continued that work working
closely with colleagues in the regulator to understand about how
best we can continue to improve the way that we communicate around
uncertainty in what is a complex compilation process as
well.
MF
Professor Mairi Spowage. You've heard Sir Robert talking earlier about the importance of understanding uncertainty in statistics and the need to make sure our statistical system can deal with that, and explain it to people properly. You've heard Craig also there explain from a production point of view the length to which the ONS goes to deal with the uncertainty in its initial estimates of GDP and the experience of dealing with those dramatic swings around the pandemic. What is your personal take on this from your understanding of what the wider public and the users of economic statistics have a right to expect? What do you make of all that?
MAIRI SPOWAGE
So I think I’d just like to start by agreeing with Robert, that explaining uncertainty to users is really important. And in my view, and certainly some research that some of my colleagues at the Economic Statistics Centre of Excellence have done, which show that actually it increases confidence in statistics, because we all know that GDP statistics will be updated as more information comes in when these are presented as revisions to the initial estimates. And I think the more you can do to set expectations of users that this is normal, and sort of core part of estimation of what's going on in the economy, the better when these revisions inevitably happen. We very much see ourselves as not just a user of statistics, but also I guess a filter through which others consume them. We discuss the statistics that ONS produce a lot, and I think we like to highlight for example, if it's the first estimate that more information will be coming in where revisions have happened. And particularly when you're quite close to zero, as we've been over the last year or so, you know, folks can get quite excited about it being slightly above or below zero, but generally the statistics are in the same area even though they may be slightly negative or slightly positive.
MF
Yes, and I'd urge people to have a listen to our other podcast on the whole subject of ‘what is a recession’ to perhaps get some more understanding of just how easily these so called technical recessions can in fact be revised away. So overall then Mairi, do you think the system is doing enough that people do appreciate, particularly on the subject of GDP, of course, because we've had this really powerful example recently, is doing enough to communicate the inherent uncertainty among those early estimates, or perhaps we couldn't be doing more?
MS
Yeah, absolutely. Obviously, there's different types of uncertainty and the way that you can communicate and talk about uncertainty when you're producing GDP statistics is slightly different to that, that you might talk about things like labour market statistics, you know. I know there are a lot of issues with labour market statistics at the moment, but obviously, the issues with labour market statistics in normal times is really about the fact it's based on a survey and that therefore has an inherent uncertainty due to the sampling that has to be done. And it might mean that a seemingly you know, an increase in say unemployment from one quarter to the next isn't actually a significant difference. Whereas with GDP, it's much more about the fact that this is only a small proportion of the data that will eventually be used to estimate what's happened in this period in the economy. And over time we’ll sort of be building it up. I think the ONS are doing a good job in trying to communicate uncertainty in statistics but I think we could always do more. I think having you know, statisticians come on and talk about the statistics and pointing these things out proactively is a good idea. So much more media engagement is definitely a good idea. As I said, we try and through you know informal means like blogs and podcasts like this, to talk about the data that have been produced. And you know, when there are interesting features of it, which are driving some of the changes and to what extent those might change. So, one of the features over the last year for 2023 has been the influence of, you know, things like public sector strikes on the data, because when there's less activity in the public sector that also changes the profile of growth over the year quite a lot. And that's been very influential over 2023. So I think it's important that there's more discussion about this and, to be honest, more knowledge in economic circles about how these statistics are put together. Or you know, I'm an economic statistician rather than an economist per se, and I think the more knowledge and awareness that can be amongst economic commentators on these issues, I think the better because if we’re upfront about the uncertainty, I think it increases the confidence when these revisions inevitably happen.
MF
Perhaps then it is the way the statistics are told in the media and elsewhere? Of course, they're invested by those observers with more authority perhaps than they deserve. Particularly, of course, it must be very tempting if you're a politician and the numbers are going your way, then obviously you want people to believe they are absolutely 100% accurate.
MS
Absolutely. We're in a funny situation at the moment. I mean, you know, our research institute focuses a lot on the Scottish economy. And the data for Scotland for 2023 shows... Yes, it shows two quarters of contraction and two quarters of growth, but they're not joined together. So there wasn't a technical recession in Scotland. But you know, over the year, basically, the Scottish and UK economies have had a really poor year with hardly any growth. But you know, I haven't seen it yet, but I'm expecting that there will be some people, you know, sort of crowing about that, like it's really showing that the Scottish economy is doing better or something when it's not really. So there will always be politicians who try to you know, over interpret changes in the data. Another example would be the first estimates of quarterly growth in the first part of 2023 showed 0.4 growth in Scotland compared to 0.1 in the UK, and there were politicians saying that Scotland was growing four times as fast as the UK. These things will happen, but you know, one of our roles to be honest is in our regular blogs and communications with the policy community, particularly in Scotland, but also beyond, is to point these things out and say that they're a bit silly. That no doubt these things will be revised and come closer together and nobody should get too excited about them.
MF
Thinking particularly about when you're looking at levels of geography different from the UK for yourselves in Scotland and from where I'm sitting here in Wales as well, for that matter. Do the data tend to become more or less accurate, should we have more or less confidence in the sort of datasets we're seeing for those different levels of geography?
MS
Well, generally it becomes more unreliable, and it's subject to more uncertainty. A lot of the data that's used is based on business surveys for estimating what's going on in the economy. And there are two areas of uncertainty there. The samples at smaller geographies are smaller so it's greater uncertainty because of sampling variability. But there's also a key problem on the data infrastructure in the UK that business data - this is across GB because Northern Ireland's is collected slightly differently - is collected on units which are GB wide. So it does make estimating what's going on in the parts of GB quite challenging. And there are some additional estimation procedures that need to be done to actually say what's going on in Scotland or in Wales. So it does add an additional layer of uncertainty to any sort of economic estimation at sub-UK geographies.
MF
I should add at that point that improving the quality of regional sub-national data has always been an important part of the ONS’s work and continues to be part of its strategic drive. But Sir Robert, from what you've seen recently, particularly over the last year, the way that GDP estimates have been used in the media and in politics, and particularly the whole business of comparing quite small differences in GDP change internationally and the significance that's invested in that, the relative growth rates between one country or another. Has there been too much discussion around that; has too much weight being put on that recently from where you've been sitting?
SRC
Well, I think just to pick up on the point that Mairi was making, you can end up investing, you know, much too much significance in comparisons of what's going on in one place and in another place over a relatively short period of time in which there's likely to be noise in those numbers. So, as she said, the idea that you know, taking one area where the growth is 0.4 in a quarter and another where it’s 0.1 and saying that one economy is growing four times more quickly, while strictly true on the basis of those that is really not an informative comparison, you have to look over a longer period for both. When you get to international comparisons, there's the additional issue of the extent to which although there are international standards and best practices as to how, for example, national accounts are put together. The way in which this is actually carried out from place to place can be done in different ways that make those sorts of comparisons again, particularly over short periods, but also when the economy is doing strange things as it was during the course of the pandemic, particularly tricky. So in the GDP context, obviously, there was the question mark about having big changes in the composition of what the education and health sectors were doing as we went into the period of lockdown and therefore judging how the output of those sectors had changed was a really very tricky conceptual judgement to make. And one of the issues that arose about trying to make international comparisons is that different people will be doing that in different ways, depending in part on how they measure the outputs of health and education under normal circumstances. So if you are going to do international comparisons, it’s certainly better to look over a longer period. So you're avoiding being misled by short term noise but also having a care to the way in which methodologies may differ, and that that may matter that sometimes more than it does allow others to see if this is actually a meaningful comparison of like for like.
MF
It's also worth pointing out, as I think we have in previous podcasts, that the UK is one of the few economies that does actually seek to measure the actual output of public services, whereas some countries just make broad assumptions about what those sectors have been doing. But it's also worth mentioning, I think that some countries simply don't revise as much as we do because their system makes an initial estimate, and then they don't return to it for some years in the case of a number of countries.
SRC
Yes, that's true. And so then the question is sometimes - and I think this arose relatively recently in the UK context - of the set of revisions that you look at and change the international comparison, but you know that some countries have not yet essentially done the same set of revisions. For example, the way in which you try to pull together the estimates of output income and expenditure at times afterwards as you have more information from annual surveys you have more information on incomes for example, from the tax system. So, again, at any given moment, even though you know, you're in both cases, trying to say well, what's our best sense of what was going on a year ago, different countries will be at different stages of the statistical production process and the proportion of the eventual total information set on which you base your estimates, you know, some countries will have incorporated more of that than less, and so a revision that you're doing this month, somebody else may not do for six months, and that again, complicates the picture, and really again, suggests that looking at international comparisons at too high frequency or too much in the recent past, there are bigger uncertainties and caveats that you ought to be placing around big calls and big interpretations based on that.
MF
Yes, and while it's hard enough to know where you are at any given point in the economy, it's even harder of course - infinitely harder you might say - to work out where on earth you're going to go next. You've spent a lot of time in the forecasting business, how are forecasters, and I know the Bank of England in particular is taking a good look at this at the moment - the data it relies upon in order to make its forecast - what can the statistical system be doing to support organisations with that unenviable task of having to look into the future and guide us on what is going to happen next?
SRC
Well, I think from the perspective of the forecasters themselves, many of the same principles that we've been talking to in terms of how the statistical system should communicate uncertainty applies in spades, in the case of forecasts where explaining how you've reached the judgements that you have, the uncertainty that you know, past forecast errors, that particular sensitivity of a forecast, a judgement that you'd be maybe making in some part of it, the more you can do to explain that increases people's trust rather than reduces it. From the perspective of the statistical producer helping the forecaster, I think, again, explanation, if you have got particular difficulties, particular reasons why you think there might be greater uncertainty than in the past around particular numbers, it's very important. The current evolution of the labour market statistics is a good example of that - you need to be talking to the big users and the big forecasters about the particular uncertainties, there may be at a given time so they can take account of that as best they can. On the other hand, having been a forecaster for 10 years, I certainly took the view that for forecasters to complain about revisions in economic data is like sailors complaining about waves in the sea. I'm afraid that is what you're dealing with. That's what you have to sail on and everybody makes their best effort to come up with the best possible numbers, but it's a fact of life. And your knowledge and understanding of what's going on in the past now, and how that informs your judgements in the future evolves over the time. It doesn't remain static and you're gazing through a murky cloud at some time, but that doesn't reduce the importance of doing the best job you can.
MF
Final word then for the forecasters and for everybody else. The statistics are reliable but understand their limitations.
SRC
Yeah.
MF
Well, that's it for another episode of
Statistically Speaking. Thanks to Sir Robert Chote, Professor Mairi Spowage, and Dr.
Craig McLaren, and of
course, thanks to you, as always for
listening.
You can subscribe to future
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I'm Miles Fletcher and from
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Goodbye
ENDS.