Luís Teles Morais

On tHe JoB mArKeT 25/26!

Apprentice economist @novasbe for a PhD by the sea. Previously playing the numbers @ecb and @ipp_jcs. Tweets EN and PT

[DG MEME 🇪🇺 @meme_ec]

+++ "His interviews bridged languages and cultures": Giovanni Trapattoni receives the FIFA Literature Prize.

[Luís Teles Morais @_luistm]

@JesusFerna7026 @Jongonzlz @BernardinoTiago Spoiler for the cross-country plots: they all look the same

[Luís Teles Morais @_luistm]

@JesusFerna7026 @Jongonzlz To your point, we actually have this for EU countries in our paper on fiscal effects of immigration in aging societies.

Here is the plot for the EU average, natives vs. immigrants (born outside the EU).

Country-by-country in p. 41 of the Appendix at https://t.co/1Z2I2sfh8K

[Luís Teles Morais @_luistm]

@mfariacastro @pedro_brinca Este então foi um parto bem difícil🧘Parabéns a todos!!

[Luís Teles Morais @_luistm]

Why Twitter’s still worth it

[Jesús Fernández-Villaverde @JesusFerna7026]

Let me explain why I believe modern economics is such a powerful tool for understanding the world. I’ll do this by discussing a great paper by Simone Cerreia-Vioglio, @UncertainLars, Fabio Maccheroni, and Massimo Marinacci, “Making Decisions Under Model Misspecification,” published in the Review of Economic Studies a few months ago.

Imagine I want to drive from UC San Diego to UCLA, but I’ve never driven that route before. I need to build a “model of the world” to guide me, which we usually call a map. Maps are simplified representations of reality. They can’t include every detail if they’re to be useful. Borges, in his short story On Exactitude in Science, makes this point beautifully. (In practice, I don’t draw the map myself—I use an app—but someone still had to make it.)

Because maps simplify, I can’t fully rely on them. Maybe last night’s storm knocked down a tree and closed a street, or there’s construction and the ramp off the highway in LA is shut down.

This uncertainty matters. Suppose I’m driving to UCLA for an important talk at 11 a.m. If the ramp is closed, I might need 15 extra minutes. When should I set my alarm to arrive on time, while still getting enough sleep to give a good talk?

The problem is that I can’t assign precise probabilities to all these contingencies. How likely is the fallen tree? Or new roadwork? Even the best traffic apps can’t capture every disruption, and some might happen after I’ve already left.

In economic terms, my “model of the world” (the map) is misspecified—and no matter how hard I try, I can’t fully fix that.

But sitting down and crying about misspecification doesn’t answer my basic question: when do I set the alarm? Too early, and I’m exhausted. Too late, and I’m late.

Simone and his co-authors offer a way to think about this. They start from the idea that we often hold several structured models of an economic phenomenon, grounded in theory. For example, a central bank might use a standard New Keynesian model and a search-and-matching model of money.

Yet, aware that each model is misspecified by design, the bank adds a protective belt of unstructured models—statistical constructs that help it gauge the consequences of misspecification.

The beauty of the paper is that it provides an axiomatic foundation for this protective belt (and even generalizes it to include a Bayesian approach). It shows that if a decision-maker’s preferences meet certain conditions —reflecting both rational and behavioral features— then those preferences can be represented by an augmented utility function that formally accounts for misspecification.

Crucially, we don’t assume that augmented utility function; we derive it. We start with general, plausible properties of preferences and prove that they imply such a representation.

That’s real progress. Instead of writing endless critiques of expected utility or rational expectations (as many have done for decades, with little to show), we now have a formal way to reason about misspecification—precise definitions, clear boundaries of validity, and awareness of what we still don’t know.

Take, for instance, a brilliant Penn graduate student on the market, Alfonso Maselli

https://t.co/rl2gu95V7t

His job-market paper pushes this frontier further. He studies cases where a decision-maker not only faces model misspecification but is also unsure which model best fits the data and can’t assign probabilities to them—what we call model ambiguity. In my example, the central bank is unsure whether the New Keynesian or the search-and-matching model fits better, and it worries that both might be incorrect.

If you read Simone et al. or Alfonso’s paper, you’ll see how misguided—and, frankly, cartoonish—many of the recent criticisms of economics on X have been.

First: the idea that economists don’t understand math or have “physics envy.” The math in these papers is subtle and advanced—utterly different from what physicists do (neither better nor worse, just distinct). An engineer transitioning into economics would find these tools unfamiliar.

Second: claims of ideological bias are unfounded. I have no idea about the political views of the authors, and I’d be surprised if anyone could infer them from the analysis—beyond vague guesses about typical academics.

Third: This has almost nothing to do with what one learns as an undergraduate, or even in first-year graduate school. If your knowledge of economics stops at an intro textbook, it’s best not to pontificate on the field’s frontiers.

Fourth: Is this science? Debating that word’s boundaries is pointless; every definition of “science” breaks down somewhere.

The Germans solved this long ago with the idea of Wissenschaft—the systematic pursuit of knowledge, whether of nature, society, or the humanities. By that measure, modern mainstream economics is clearly a Wissenschaft: a disciplined, cumulative, and highly useful effort to understand how the world works. Simone and his co-authors have demonstrated that beyond any reasonable doubt.

[Luís Teles Morais @_luistm]

@al_antdp https://t.co/1cV8UCN2yd se tiver comentários serão muito bem-vindos!

[João Pereira dos Santos @JPSantos_econ]

And that's a wrap on the 2025 @iseglisbon Summer School! We will certainly not forget what we learned in Pedro Bordalo' s course on "Attention, Memory, and Expectations." Thank you all for coming to Lisbon!

[Jesús Fernández-Villaverde @JesusFerna7026]

Every time I read an economist or journalist write something along the lines of:

“public consumption” (or investment, or exports, or any other component of national accounts) “explains 39% of GDP growth in 2023”,

I despair.

No, no, and a thousand times no!

The basic accounting identity:

Y = C + I + G + X - M

always holds (it’s an identity!), and therefore, changes on the left-hand side must equal changes on the right-hand side. But that does not mean those changes “explain” anything, at least not in the usual causal sense of the word “explain.”

Imagine that, for whatever reason, the government increases public consumption (G) by 2% of GDP. In response to this expansion, economic agents increase their saving by 1% of GDP, anticipating higher future taxes. Investment (I) and net exports (X − M) remain unchanged. In that case, GDP grows by 1%.

But does that mean the 2% increase in G “explains” 200% of GDP growth (1%)? Obviously not.

To answer that kind of question, we would need to estimate what would have happened without the increase in G. Perhaps GDP would have grown more. Perhaps it would have grown less. To know that, we need an economic model.

Staring at a mere accounting identity tells you nothing about the counterfactual. At best, it might give you a rough hint. But not much more than that.

@briancalbrecht explains it very well here:

https://t.co/OlnFIvCU8J

So, the next time you see someone claiming that a component of national accounts “explains” something, go ahead and add them to your list of sources to ignore.

[Jesper-Bojeryd @BojerydJesper]

Together with @RoineVestman and others, we're seeking an RA to work with household data and answer questions in macro and finance. The position is at Stockholm University and will last for 12 months, starting at the latest in September 2025. Please share link in comment below.

[Ricardo Reis @R2Rsquared]

Why didn’t electrical engineers forecast the Iberian electricity crisis?

[Luís Teles Morais @_luistm]

Excited to present an update of our paper on the fiscal effects of immigration (with @BernardinoTiago and F. Franco) - Friday at the @NovaSBE PhD Research Group. It's hybrid so HMU if interested. And check out our brilliant lineup for the coming weeks!

https://t.co/d9tYfBufJx

[Luís Teles Morais @_luistm]

A imagem original https://t.co/EjDJhN8IcJ

[Luís Teles Morais @_luistm]

Ainda sobre o índice da OCDE! Andava por aí a circular uma infografia a dizer o mesmo da capa do Expresso. O site responsável, o "Visual Capitalist" já a corrigiu para falar de "Home Price to Income Ratio Change" https://t.co/TFOgMxqbaj

[Luís Teles Morais @_luistm]

@v_ulgaris @expresso @OECD Curiosamente a resposta para isso não é fácil de encontrar publicamente (e também daí a confusão)

[Luís Teles Morais @_luistm]

Este título do @expresso hoje assenta num erro. Sermos 1.º neste índice da @OECD significa que a relação preços das casas/rendimentos cresceu, desde 2015, mais do que noutros países. Não significa que tenhamos os preços mais altos, mesmo em relação ao rendimento (não temos)

[Luís Teles Morais @_luistm]

@v_ulgaris @expresso @OECD Está escrito ali mesmo: "Rácio entre preço médio da habitação e os rendimentos é o mais alto". E acabei de ouvir outra vez na SIC

[Luís Teles Morais @_luistm]

@expresso @OECD Ninguém duvida da gravidade da crise da habitação. Mas títulos como este demonstram a necessidade de definir e medir melhor o problema.

Para podermos pensar em políticas para o resolver, e não para criar outros novos

cc @JoaoBDuart3

[Luís Teles Morais @_luistm]

@expresso @OECD Em Portugal, a subida foi grande, mas também porque partimos de preços baixos em 2014-15.

O que era natural após a forte recessão, mais a desalavancagem da banca, com uma forte contração da concessão de crédito à habitação.

[Luís Teles Morais @_luistm]

@expresso Não, não é! O índice em causa mede a evolução (2015=100) dos preços das casas face aos rendimentos. Sermos “primeiros” neste ranking significa que tivemos a maior subida desde 2015. Não quer dizer que tenhamos os preços mais altos, mesmo em relação ao rendimento.

[Luís Teles Morais @_luistm]

if interested, this guide by @aaronsojourner is very helpful https://t.co/2jpKkhZFCH

[Luís Teles Morais @_luistm]

2025 goal: spend less time on social media
2025 day 2: ** new social media account **

join me at #EconSky

https://t.co/paOlaQMtxi