<p>Looking at the US Economy,  the <a href="http://www.bea.gov/" rel="nofollow">US Bureau of Economic Analysis</a> (<strong>BEA</strong>) and specifically <a href="http://www.bea.gov/methodologies/index.htm#national_meth" rel="nofollow">ch. 3 p. 49 of the Handbok of NIPA (National Income and Product Accounts),</a> we read</p>

<blockquote>
  <p>"<em>The data that BEA uses are collected from a variety of sources and <strong>are
  usually collected for purposes other than for incorporation into BEA's
  estimates</strong>. <strong>Data collected by federal government agencies</strong> provide the
  backbone of the estimates; these data <strong>are supplemented by data from
  trade associations, businesses, international organizations, and other
  sources</strong>. The Government data are from a number of agencies, including
  the Commerce Department's Census Bureau, the Labor Department's Bureau
  of Labor Statistics (BLS), the Treasury Department, the Office of
  Management and Budget, and the Agriculture Department.
  <strong>“Administrative” data are data that are tabulated by federal
  government and by state and local government agencies as a byproduct
  of administering their programs—such as processing corporate tax
  returns, regulating public utilities, and issuing building permits.</strong>
  <strong>“Statistical”</strong> data are data collected by the federal statistical
  agencies, such as the Census Bureau and BLS. These data consist of
  <strong>periodic economic and population censuses and a wide range of sample
  surveys,</strong> such as those that collect data on manufacturing and trade,
  employment, and prices. The relatively few surveys that BEA conducts
  cover international trade in services and international direct
  investment, both by foreign companies in the United States and by U.S.
  companies in foreign countries. <strong>The source data available to BEA are not always ideal for the preparation of the
  NIPAs. BEA must develop methods that transform the best available data into estimates that are consistent with the NIPA concepts and framework and that fill gaps in the
  coverage of the source data."</strong></em>  </p>
</blockquote>

<p><strong><em>Macro-data are not just micro-data simply sitting out there and only needing adding up</em></strong>. So "collecting macroeconomic data" is a misleading term : "<strong>laboriously <em>producing</em> macroeconomic data</strong>", where collection is just a first step, moving on to selection, clean-up, adjustment, homogenization, <em>and</em> estimation, is more like it. This is a vast enterprise, even with today's Information Technology capabilities, even for smaller economies than US.  </p>

<p>Increasing the frequency of macro-data amounts to increasing the total output from this data-production function, which looks convincingly like a huge increase in input factors absorption. <strong>So to do it, it would increase costs substantially.</strong>  </p>

<p>Moreover, the "collected for reasons other than..." part, indicates that a lot of <em>procedures</em> should be disrupted and changed in order to achieve that. Also, the number of different primary sources implies too many people and institutions should change their way of doing things. <strong>These are significant barriers on their own</strong>, irrespective of cost-issues.</p>

<p><strong>What could be the benefits?</strong>
One could think that it would permit a "better" fine-tuning of the economy? -to the degree that one believes that an economy can really be fine-tuned by its Government (it can certainly be <em>affected</em>, but fine-tuning is a whole different game).</p>

<p>What about the benefits to the private sector? It is indeed the case that, say, Gross Domestic Output is produced <em>every day</em>. Assume that in some magical way, GDP was recorded and published <em>every day</em>. <em>So we could follow GDP as we follow the Stock Market</em>. We will be able to observe the <em>everyday variability</em> of output. Would that be beneficial? No, on the contrary, it would be misleading. Why? Because, although GDP is produced everyday, it mostly is produced based on structural relations (contracts, technologies, customs, knowledge etc) that have a lower frequency of change -and for reasons other than lack of data. So its everyday variability would be mostly due to pure randomness or exogenous factors like the weather -and observing it would only torment us towards trying futilely to do something about it (at our own micro-level).  </p>

<p>What about the benefits to the discipline of Economics? Presumably, more data is always better. Is it? By increasing the number of instances of "asking for" and recording data about an economic variable, we increase the instances for which we may have <em>measurement error</em> - and this may very well degrade the overall quality of our data. It is a standard wisdom departed in undergraduate statistics classes, that even if we could conceivably run a census than just collecting a sample from the population under study, it would be better to have just the sample, because the error of representability would most likely be smaller than the error of measurement we would have if we attempted to record the whole population.</p>

<p>That would offset the perceived benefits of having pushed the magnifying glass closer to the object of study. And sometimes, it is better to look from a certain distance, anyway.</p>

<p>Closing, by a "Laffer-curve" style of argument, one could argue that there must exist some <em>optimal</em> frequency to have available macro-data. I am not saying that what we do have now is this <em>optimal</em> frequency, but I doubt making the frequency higher is the way to go (my opinion is that modern economies of the western world at least would be tracked better under a three four-month cycle - Jan-Apr, May-Aug, Sep-Dec -it seems to me that this scheme combines better the seasons with the social aspects of life and economic activity). </p>

CC BY-SA 3.0
2014-12-25T00:03:15.443Z
2014-12-25T00:17:22.063Z
2014-12-25T00:17:22.063Z
Alecos Papadopoulos
<p>Deciding on whether to use monthly,quarterly or yearly data depends on over what period you wish to investigate for potential policy intervention.</p>

<p>The term costs and benefits solely depends on what type of data you are looking at. If you want to figure out if the crime rate that is currently being experienced by a given city is normal currently, yearly data  is probably not the best for investigating such phenomenon.</p>

<p>Quarterly or monthly data is probably better because controls for seasonality can be applied, however for yearly data you cant implement seasonal dummies because there is no rationale for doing so.</p>

<p>On the other hand,recall the more often you sample macroeconomic data the "noisier" it gets, so sampling more often than a monthly basis can add unnecessary noise which can make your data more difficult to interpret.</p>

CC BY-SA 3.0
2017-11-11T23:23:41.473Z
2017-11-11T23:23:41.473Z
EconJohn
<p>What would be the costs and benefits of collecting and releasing macroeconomic data, say, monthly instead of quarterly, or weekly instead of monthly? And what are the barriers to doing so?</p>

CC BY-SA 3.0
2014-12-30T22:08:27.183Z
Of interest may be the case in Brazil where the Central Bank of Brazil produces a monthly GDP figure while the National Statistics Office produce a quarterly GDP figure. I'm not an expert on the Brazilian economy, but some details can be found in this Federal Reserve Board Discussion Series [paper](http://www.federalreserve.gov/econresdata/feds/2014/files/201494pap.pdf). The title of paper is *The Importance of Updating: Evidence from a Brazilian Nowcasting Model*. An important point is whether or not the same body or same methodology would be used to produce the higher frequency estimate.
Graeme Walsh
<macroeconomics>
Measuring macroeconomic variables more frequently
sirallen
# Measuring macroeconomic variables more frequently

(Asked by: username_0 on 2014-12-24)

What would be the costs and benefits of collecting and releasing macroeconomic data, say, monthly instead of quarterly, or weekly instead of monthly? And what are the barriers to doing so?

**Tags:** macroeconomics
> **Comments:**
> **username_1**: Of interest may be the case in Brazil where the Central Bank of Brazil produces a monthly GDP figure while the National Statistics Office produce a quarterly GDP figure. I'm not an expert on the Brazilian economy, but some details can be found in this Federal Reserve Board Discussion Series [paper](http://www.federalreserve.gov/econresdata/feds/2014/files/201494pap.pdf). The title of paper is *The Importance of Updating: Evidence from a Brazilian Nowcasting Model*. An important point is whether or not the same body or same methodology would be used to produce the higher frequency estimate.  

---

## 2 answers

### Answer by username_2 on 2014-12-25. Score: 3

Looking at the US Economy, the [US Bureau of Economic Analysis](http://www.bea.gov/) (**BEA**) and specifically [ch. 3 p. 49 of the Handbok of NIPA (National Income and Product Accounts),](http://www.bea.gov/methodologies/index.htm#national_meth) we read

> "*The data that BEA uses are collected from a variety of sources and **are
> usually collected for purposes other than for incorporation into BEA's
> estimates**. **Data collected by federal government agencies** provide the
> backbone of the estimates; these data **are supplemented by data from
> trade associations, businesses, international organizations, and other
> sources**. The Government data are from a number of agencies, including
> the Commerce Department's Census Bureau, the Labor Department's Bureau
> of Labor Statistics (BLS), the Treasury Department, the Office of
> Management and Budget, and the Agriculture Department.
> **“Administrative” data are data that are tabulated by federal
> government and by state and local government agencies as a byproduct
> of administering their programs—such as processing corporate tax
> returns, regulating public utilities, and issuing building permits.**
> **“Statistical”** data are data collected by the federal statistical
> agencies, such as the Census Bureau and BLS. These data consist of
> **periodic economic and population censuses and a wide range of sample
> surveys,** such as those that collect data on manufacturing and trade,
> employment, and prices. The relatively few surveys that BEA conducts
> cover international trade in services and international direct
> investment, both by foreign companies in the United States and by U.S.
> companies in foreign countries. **The source data available to BEA are not always ideal for the preparation of the
> NIPAs. BEA must develop methods that transform the best available data into estimates that are consistent with the NIPA concepts and framework and that fill gaps in the
> coverage of the source data."***

***Macro-data are not just micro-data simply sitting out there and only needing adding up***. So "collecting macroeconomic data" is a misleading term : "**laboriously *producing* macroeconomic data**", where collection is just a first step, moving on to selection, clean-up, adjustment, homogenization, *and* estimation, is more like it. This is a vast enterprise, even with today's Information Technology capabilities, even for smaller economies than US.

Increasing the frequency of macro-data amounts to increasing the total output from this data-production function, which looks convincingly like a huge increase in input factors absorption. **So to do it, it would increase costs substantially.**

Moreover, the "collected for reasons other than..." part, indicates that a lot of *procedures* should be disrupted and changed in order to achieve that. Also, the number of different primary sources implies too many people and institutions should change their way of doing things. **These are significant barriers on their own**, irrespective of cost-issues.

**What could be the benefits?**
One could think that it would permit a "better" fine-tuning of the economy? -to the degree that one believes that an economy can really be fine-tuned by its Government (it can certainly be *affected*, but fine-tuning is a whole different game).

What about the benefits to the private sector? It is indeed the case that, say, Gross Domestic Output is produced *every day*. Assume that in some magical way, GDP was recorded and published *every day*. *So we could follow GDP as we follow the Stock Market*. We will be able to observe the *everyday variability* of output. Would that be beneficial? No, on the contrary, it would be misleading. Why? Because, although GDP is produced everyday, it mostly is produced based on structural relations (contracts, technologies, customs, knowledge etc) that have a lower frequency of change -and for reasons other than lack of data. So its everyday variability would be mostly due to pure randomness or exogenous factors like the weather -and observing it would only torment us towards trying futilely to do something about it (at our own micro-level).

What about the benefits to the discipline of Economics? Presumably, more data is always better. Is it? By increasing the number of instances of "asking for" and recording data about an economic variable, we increase the instances for which we may have *measurement error* - and this may very well degrade the overall quality of our data. It is a standard wisdom departed in undergraduate statistics classes, that even if we could conceivably run a census than just collecting a sample from the population under study, it would be better to have just the sample, because the error of representability would most likely be smaller than the error of measurement we would have if we attempted to record the whole population.

That would offset the perceived benefits of having pushed the magnifying glass closer to the object of study. And sometimes, it is better to look from a certain distance, anyway.

Closing, by a "Laffer-curve" style of argument, one could argue that there must exist some *optimal* frequency to have available macro-data. I am not saying that what we do have now is this *optimal* frequency, but I doubt making the frequency higher is the way to go (my opinion is that modern economies of the western world at least would be tracked better under a three four-month cycle - Jan-Apr, May-Aug, Sep-Dec -it seems to me that this scheme combines better the seasons with the social aspects of life and economic activity).


---

### Answer by username_3 on 2017-11-11. Score: 0

Deciding on whether to use monthly,quarterly or yearly data depends on over what period you wish to investigate for potential policy intervention.

The term costs and benefits solely depends on what type of data you are looking at. If you want to figure out if the crime rate that is currently being experienced by a given city is normal currently, yearly data is probably not the best for investigating such phenomenon.

Quarterly or monthly data is probably better because controls for seasonality can be applied, however for yearly data you cant implement seasonal dummies because there is no rationale for doing so.

On the other hand,recall the more often you sample macroeconomic data the "noisier" it gets, so sampling more often than a monthly basis can add unnecessary noise which can make your data more difficult to interpret.


---

