<p>It is common to say that the behavior of a national economy is complicated because it involves millions of individuals (<span class="math-container">$10^8$</span>) each of whom makes thousands of relevant economic decisions a year (<span class="math-container">$10^4$</span>) and that their mutual interaction is too complex. However, it is forgotten that the aggregate behavior makes the actions of some agents partly compensate with those of others so we do not need to know exactly what each individual does at each moment but the average effect. This should make it easier to predict what will happen in a national economy in 10 years than to know what an individual will do in 10 years with his life (degrees of freedom: <span class="math-container">$&lt; 10^8\cdot 10^4 \cdot 10^2 = 10^{14}$</span>).</p>
<p>In fact, that situation occurs in meteorology, the atmosphere has <span class="math-container">$1.09\cdot 10^{44}$</span> molecules and its physical state requires 6 variables, so in reality the number of degrees of freedom of the atmosphere (<span class="math-container">$10^{45}$</span>) is actually much larger than that of a national economy, and yet while meteorological prediction is complex it is far more accurate than macroeconomic prediction (see Nate Silver <a href="https://en.wikipedia.org/wiki/The_Signal_and_the_Noise" rel="nofollow noreferrer">The Signal and the Noise</a>).</p>
<p>So the question is: let's accept that it is not the number of variables that makes macroeconomic forecasting difficult, what are the four or five factors that make economic forecasts even less accurate than weather forecasts?</p>

CC BY-SA 4.0
2022-07-08T00:24:24.600Z
Controlled experiments in the underlying science are very limited (cannot thrust random people into squalor for a year to see how their behavior changes).
Giskard
CC BY-SA 4.0
2022-07-08T00:30:14.053Z
Beliefs about the future (e.g., future prices) play a large role in decisions, but these are hard to measure and are not independent for different people - there are relatively few news sources. Due to lack of independence large numbers do not yield a nearly constant average.
Giskard
CC BY-SA 4.0
2022-07-10T10:01:24.417Z
Useful predictabity does not depend on the number of parameters.  A double pendulum has four, and its behaviour is generally not predictable.  Weather is *also* not predictable far in advance as there is SDIC and chaos, and even very simple weather models share this characteristic (Lorenz's model had 12 I think).  (I am unclear on the state of chaos in climate modelling: I think there *are* chaotic regions in the space, but the chaos is longer-term.)   Equating the behaviour of humans, who follow who-knows-what rules with particles which obey well-understood laws of physics is absurd.
ignis volens
CC BY-SA 4.0
2022-07-10T17:28:35.513Z
You have two different fields predicting different phenomena. What does it mean that one field predicts the things it predicts better than another field predicts the things it predicts better?
Michael Greinecker
CC BY-SA 4.0
2022-07-10T18:15:19.800Z
@MichaelGreinecker you are right, I used "better" to mean more accurate (considering the relative errors in the predictions), but these disciplines in fact are trying to predict different things, and accuracy is only a limited measure of "goodness".
Davius
CC BY-SA 4.0
2022-07-10T18:27:23.387Z
@Davius I think the problem is bigger. How do you measure accuracy? Your measure is probably going to depend on the units of measurement. Do you measure GDP forecasts in pennies or billion dollars?
Michael Greinecker
CC BY-SA 4.0
2022-07-10T18:32:19.470Z
@MichaelGreinecker The units are no problem because $|M_a - M_e|/|M-a|$, being $M_a$ actual value, and $M_e$ expected value, all the dimensions are the same, and, thus, relative error is dimensionless.
Davius
CC BY-SA 4.0
2022-07-10T18:49:53.783Z
@Davius What is $M$ and $a$ here? You only explained your numerator here.
Michael Greinecker
CC BY-SA 4.0
2022-07-10T19:55:54.310Z
$M$ is any magnitude (no restrictions), $M_a$ the observed/actual value, and $M_e$ the predicted/expected value.
Davius
<macroeconomics><philosophy-of-economics><forecasting>
Weather forecasting vs. macroeconomic forecasting
Davius
# Weather forecasting vs. macroeconomic forecasting

(Asked by: username_0 on 2022-07-07)

It is common to say that the behavior of a national economy is complicated because it involves millions of individuals ($10^8$) each of whom makes thousands of relevant economic decisions a year ($10^4$) and that their mutual interaction is too complex. However, it is forgotten that the aggregate behavior makes the actions of some agents partly compensate with those of others so we do not need to know exactly what each individual does at each moment but the average effect. This should make it easier to predict what will happen in a national economy in 10 years than to know what an individual will do in 10 years with his life (degrees of freedom: $< 10^8\cdot 10^4 \cdot 10^2 = 10^{14}$).

In fact, that situation occurs in meteorology, the atmosphere has $1.09\cdot 10^{44}$ molecules and its physical state requires 6 variables, so in reality the number of degrees of freedom of the atmosphere ($10^{45}$) is actually much larger than that of a national economy, and yet while meteorological prediction is complex it is far more accurate than macroeconomic prediction (see Nate Silver [The Signal and the Noise](https://en.wikipedia.org/wiki/The_Signal_and_the_Noise)).

So the question is: let's accept that it is not the number of variables that makes macroeconomic forecasting difficult, what are the four or five factors that make economic forecasts even less accurate than weather forecasts?

**Tags:** macroeconomics, philosophy-of-economics, forecasting
> **Comments:**
> **username_1**: Controlled experiments in the underlying science are very limited (cannot thrust random people into squalor for a year to see how their behavior changes).  
> **username_1**: Beliefs about the future (e.g., future prices) play a large role in decisions, but these are hard to measure and are not independent for different people - there are relatively few news sources. Due to lack of independence large numbers do not yield a nearly constant average.  
> **username_2**: Useful predictabity does not depend on the number of parameters.  A double pendulum has four, and its behaviour is generally not predictable.  Weather is *also* not predictable far in advance as there is SDIC and chaos, and even very simple weather models share this characteristic (Lorenz's model had 12 I think).  (I am unclear on the state of chaos in climate modelling: I think there *are* chaotic regions in the space, but the chaos is longer-term.)   Equating the behaviour of humans, who follow who-knows-what rules with particles which obey well-understood laws of physics is absurd.  
> **username_3**: You have two different fields predicting different phenomena. What does it mean that one field predicts the things it predicts better than another field predicts the things it predicts better?  
> **username_0**: @MichaelGreinecker you are right, I used "better" to mean more accurate (considering the relative errors in the predictions), but these disciplines in fact are trying to predict different things, and accuracy is only a limited measure of "goodness".  
> **username_3**: @Davius I think the problem is bigger. How do you measure accuracy? Your measure is probably going to depend on the units of measurement. Do you measure GDP forecasts in pennies or billion dollars?  
> **username_0**: @MichaelGreinecker The units are no problem because $|M_a - M_e|/|M-a|$, being $M_a$ actual value, and $M_e$ expected value, all the dimensions are the same, and, thus, relative error is dimensionless.  
> **username_3**: @Davius What is $M$ and $a$ here? You only explained your numerator here.  
> **username_0**: $M$ is any magnitude (no restrictions), $M_a$ the observed/actual value, and $M_e$ the predicted/expected value.  

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## 0 answers

