Capturing international influences in U.S. monetary
policy through a NLP approach
Laurent Ferrara

Nicolas de Roux *Skema Business School
laurent.ferrara@skema.edu

QuantCube Technology, EconomiX
n.deroux@quant-cube.com

February 23, 2023

Abstract
The U.S. Federal Reserve has a statutory dual domestic mandate of price stability and full employment. In
this paper, we question about the role of the international environment in shaping U.S. monetary policy
decisions. In this respect, we apply natural language processing (NLP) techniques to minutes of theFederal Open Market Committee (FOMC) and construct indexes of the attention paid by U.S. monetary
policymakers to the international economic and financial situation, as well as sentiment indexes. By
integrating those text-based indicators into a Taylor rule, we derive various quantitative measures of theexternal influences on Fed decisions. Our results show that when there is a focus on international topics
within the FOMC, the Fed’s monetary policy generally tends to be more accommodative than expected bya Taylor rule. This result is robust to various alternatives including a time-varying neutral interest rate
or a shadow central bank interest rate.

Keywords : Monetary policy, Federal Reserve, FOMC minutes, International environment, Natural Language Processing, Machine Learning* We would like to especially thank the organisers of the PhD students seminar at EconomiX - Université Paris Nanterre,
as well as seminar participants, especially discutants Thomas Chuffart and Stéphane Matton. We also thank Benoit Bellone,
Oussama Belmejdoub, Maxence David, Aspram Grigoryan, Min Li, Joao Pedro Scalco Macalos for useful discussions.

1February 2023 • Draft n°3 (2023-02-15)

1.

Introduction

To manage monetary policy in the United States, the Federal Reserve (the Fed hereafter), via it
Federal Market Open Committee (FOMC), has a dual statutory mandate to fulfill of promoting
maximum employment and stable prices, as well as moderate long-term interest rates. Clearly,
those objectives are domestic ones and do not account, at least directly, for the international
environment. If we add to this the fact that the U.S. economy is the archetype of a large, closed
economy, the common wisdom is that the Fed is considered to be largely indifferent to what’s
going on in the rest of the world. In an influential paper, Barry Eichengreen was asking in 2013:
Does the Federal Reserve care about the Rest of the World? (Eichengreen, 2013).
However, a first sight at the meeting minutes of the FOMC (two meetings per quarter) showsthat the international situation is often discussed among members during meetings, sometimes at
lengths. Indeed, international factors play a role in the stability of the U.S. economy and financial
system, through various mechanisms. The main factors are the global integration of financial
markets which influences domestic market conditions, the role of international prices on domestic
inflation (imported inflation) and the potential spillbacks on the U.S. economy of US monetary
policy’s influence on foreign economies (Obstfeld, 2020). Adverse economic conditions that could
affect the U.S. economy can therefore lead the FOMC to raise or to cut the Federal Funds Rates(FFR), the main conventional tool of monetary policy (Ihrig and Wolla, 2020).
The influence of the international environment on US monetary policy is sometimes explicitly
evoked by policymakers themselves. In the midst of the global financial turbulence in 1998,
marked by the Russian Federation’s default on its debt, Fed Chairman Alan Greenspan remarked
in a September 4th speech in Berkeley that “it is just not credible that the United States can remain an
oasis of prosperity unaffected by a world that is experiencing greatly increased stress” (Greenspan, 1998).
This take was widely seen by market participants as an early warning of the FFR cut decided laterthat month. Another example of this supposed attention to the international environment took
place in August 2015, at a time when the Fed was expected to start hiking rates from the zero
lower bound, where they had been stuck at for several years. At the same time, concerns over
the rebalancing of China’s growth model and the possible manipulation of the renminbi madeinternational headlines. Vice Chairman Stanley Fisher declared in a Jackson Hole speech that
the FOMC had to "consider [...] the influence of foreign economies on the U.S. economy as [they] reach[their] judgment on whether and how to change monetary policy", explicitly referring to the Chinese
slowdown in the same speech (Fischer, 2015). Some interpreted this concern as the driving force
behind the decision to shift the first rate hike towards December 2015.
Based on this anecdotal evidence, our research question is to quantitatively assess the possible
effects of the global economy on U.S. monetary policy decisions. Our empirical strategy is to use
the textual information contained in the minutes of the FOMC, published after each meeting of
the committee. In this respect, we construct various indicators of attention to the international
environment through Natural Language Processing methodologies. Then, we integrate thoseindicators into extended econometric equations building on the Taylor rule as initially put forward
by Taylor, 1993, and Taylor, 1999.
Our work contributes to two major strands of the economic literature (see next section for moredetails). The first one considers the role of the U.S. Fed within the global economy. Generally,
authors looks at the spillovers of Fed monetary policy decisions to the rest of the world (see2February 2023 • Draft n°3 (2023-02-15)

among others Dedola et al., 2017), and to emerging countries more specifically. This literature
has been highlighted by the famous words of Dilma Roussef in 2012, then-President of Brazil,referring to the ultra-accommodative U.S. monetary policy as a driver of the monetary tsunamithat was overwhelming emerging market with large capital flows. However, spillbacks from therest of the world to the U.S. monetary policy are less a concern in the literature (see however
Breitenlechner et al., 2022). The other strand of the literature is the quantitative analysis of central
bank speeches and especially Federal Reserve communication. This literature starts with the
influential work of Romer and Romer, 2004, who dig into Greenbook documents prepared by
Fed economists in order to estimate monetary policy shocks as the differences between FFR andforecasts of inflation, output and unemployment rate. With the development of Machine Learning
tools and Natural Language Processing (NLP) techniques, authors have developed approaches toextract more efficiently textual information. For example, Aruoba and Drechsel, 2022 have recently
proposed new NLP methods to estimate U.S. monetary policy shocks.
Our work fits into both fields by using NLP approaches to extract from official Fed documents anindex summarizing the attention of FOMC members to international economic conditions. In thisrespect, we extend and assess econometrically the idea put forward in the blog post by Ferrara
and Teuf, 2018, who are simply counting some international words contained in FOMC minutes.
The rest of this work is structured as follows. Section 2 briefly reviews the literature related to
our work. Section 3 presents the data and the methods we consider to create our international
environment indices and discusses our results in light of major international events. Section 4
presents the framework under which we will consider our index to assess its significance on the
conduct of US Monetary Policy. Section 5 concludes.

2.
2.1.

Literature review
US monetary policy and the rest of the world

The situation of the U.S. central bank as a pivot of the world economy has been well establishedin the literature. Relationships between the Fed and the rest of the world go in both ways: fromthe U.S. monetary policy to the rest of the world (spillovers) and from the rest of the world to the
U.S. monetary policy. However the literature appears somewhat asymmetric as the first direction
has been by far the most studied. Indeed, most of the time, authors looks at the spillovers of Fed
monetary policy decisions to the rest of the world, and to emerging countries more specifically.
This literature has been highlighted by the famous words of Dilma Roussef in 2012, then-Presidentof Brazil, referring to the very accommodative U.S. monetary policy as a driver of the monetary
tsunami that was overwhelming emerging market with large capital flows. Without being exhaustive, the recent works of Dedola et al., 2017), Miranda-Agrippino and Rey, 2021 and Bruno andShin, 2012 provide a good overview of the concept of global financial cycle and of the risk-taking
channel of monetary policy, while Degasperi et al., 2021 specifically study the effects of US
monetary policy tightening on advanced and emerging economies.
However, spillbacks from the rest of the world to the U.S. monetary policy are less a concern
in the literature. Eichengreen, 2013, offers a very thorough history of the attention paid by theFederal Reserve to international considerations in its century of existence. He widely describes
periods of lower attention and renewed interest in the international environment by U.S. monetary
policymakers. In the first half of the 1960s, as worries mounted over a possible devaluation3

February 2023 • Draft n°3 (2023-02-15)of the dollar and the amount of U.S. foreign liabilities, the Fed paid "considerable attention"
to the balance of payments, as argued by Bordo and Eichengreen, 2008, resulting in a tightermonetary policy than what would have been expected from inflation and output gap considerations, something Taylor, 1999 has called a policy mistake. Eichengreen, 2013 underlines that thisinfluence has drastically diminished after the late 1970s, when the conjunction of the collapseof the Bretton Woods system in 1973, the Federal Reserve Reform Act of 1977 (which carved
into stone the dual mandate) and the arrival of Paul Volcker (who made fighting inflation his
top priority), made international situation far less important for the Fed. The share of the US inworld GDP (33 percent in 1985, its peak), the moderate rise in the Trade/GDP ratio (comparedto the following decades) and weaker international finance links than in the subsequent years
meant that in this period and through the 1980s, the Fed was able to decide its policy under
the rough approximation that the U.S. could be considered as a closed economy. This situationof low attention to international environment (relative to what was seen before) has dominatedever since, even though the decades since the 1980s have seen an increasing coordination of theFederal Reserve and other central banks (for meetings, coordinated interventions like what wasseen in 2008, or through dollar swap lines opened during crises such as the subprime crisis). But
Eichengreen argues that these occurrences show that "what happens abroad doesn’t stay abroad"and in a prospective part, contends that international considerations should be more explicitly
taken into account by the FOMC in the future, for they will have more influence on the US economy.
In a more analytical work, Obstfeld, 2020, explores the way the global economic situation can
influence the U.S. economy, and in particular the economic variables monetary policymakers
are interested in, especially inflation, employment, output and financial conditions. He notesthat if global factors do not necessarily affect the ability of the central bank to exert a control on
long-term price changes, they can have a strong influence in the short run. The author identifies
the main channels of transmission through which these global factors can impact U.S. GDP. Firstof all, he highlights the role of international prices (such as commodities for example) but alsoof international competition in shaping U.S. inflation. The role of imported inflation is shown
to be more and more important in setting domestic prices. Another channel originates from theintegration of global financial markets, which affects the determination of asset returns (includingr ∗ , the natural real rate of interest) and domestic financial stability. Overall, the author concludes
that in a world made ever more complex by increasing cross-border interconnections, the U.S.
economy is likely to be increasingly affected by global savings and investment trends, therefore
requiring attention from U.S. monetary policy-makers. Those spillback effects have been highlighted in multiple studies, such as Sachs, 1985, who argues that higher interest rates mean astrong dollar which pushes import prices lower and favors disinflation, and extensively studied inBreitenlechner et al., 2022.
An overall result from empirical and theoretical works tends to acknowledge evidence of significant spillbacks, but of small magnitude. For example, Caldara et al., 2022, use the Sigma model inits dollar-dominance version, developed at the Fed Board, and show that a 100 basis point increasein the foreign economies results in a drop of U.S. GDP of 0.15 percent at the trough, that is about
half of the impact of U.S. monetary policy on foreign GDP.

4

February 2023 • Draft n°3 (2023-02-15)

2.2.

Textual analysis of monetary policy material

Our work also fits in the literature on textual analysis of documents produced by monetary
policymakers, and especially FOMC material. Within that common theme, researchers have useda large range of techniques to study widely different issues.
This literature starts with the influential work of Romer and Romer, 2004, who dig into Greenbook
documents prepared by Fed economists in order to estimate monetary policy shocks as the differences between FFR and forecasts of inflation, output and unemployment rate done by the Fed
staff. Those monetary policy shocks have been then extensively used in the macroeconometricfield to assess their effects on macroeconomic and financial variables (see for example Coibion
et al., 2017).
With the development of Machine Learning tools and Natural Language Processing (NLP) techniques, authors have developed approaches to extract more efficiently and more accurately textual
information from monetary policy documents. Perhaps the first example of computational textual
analysis of FOMC content is the paper by Boukus and Rosenberg, 2006 who are using Latent
Semantic Analysis (LSA), a form of principal component analysis applied to text. This approachallows recognition of textual patterns in documents to decompose minutes into a set of themes,
which are in turn linked they link to market moves. They conclude that market participants cangain "multifaceted signal" from the minutes, paving the way for more computational analysis ofminutes text data. The same LSA methodology has been applied more recently by Mazis andTsekrekos, 2016 who identify themes in FOMC statements, among them credit spread, short orlong end of the yield curve, with significant effects on the moves of the bond markets. Fligstein
et al., 2014 apply another topic modeling technique, Latent Dirichlet Allocation (LDA, see Blei,
2003), to analyze how FOMC members recognize financial crises. Hansen and McMahon, 2016
also use a LDA to carry out an investigation of the effects of Fed communication on macro
variables. Both LSA and LDA approaches are unsupervised topic modeling techniques whichsets them apart from the methods we use in section 3.2.2. These topic modeling techniques
have in recent years become a de facto default for textual data analysis in this field, but several
others have been used in the related literature or in other economics-related natural language
processing papers. Aruoba and Drechsel, 2022 use Natural Language Processing techniques
to build a novel method to identify monetary policy shocks by analyzing Fed Greenbook documents. In this respect, they construct multiple time series of the sentiment surrounding various
economic concepts they identify in the documents (‘aspect-based’ sentiments) using a dictionary first put forward by Loughran and Mcdonald, 2011. This approach through dictionaries
is close to what we study in section 3.2.1 and in line with the initial work by Ferrara and Teuf, 2018.
Some research works have also been conducted in a supervised learning framework. For example,
Handlan, 2020 applies a Neural Network that predicts change in Fed funds futures using FOMC
statements to create a series of monetary policy shock. Rohlfs et al., 2016 use a classification
method (Max-Entropy Discrimination LDA or MedLDA) to classify whether a given topic leads tochanges in interest rates. Then, they infer from this classification whether interest rates are likely
to move in a given direction after the publication of the minutes of a meeting.
Many other studies focus on the interactions between Fed decisions and financial markets. Cieslak
and Vissing-Jorgensen, 2021, count market-related words in Fed minutes to show that Fed monetary policy pays attention to the stock market. Sharpe et al., 2022, use Greenbook documents toestimate a sentiment index which is in turn put into quantile regressions to show that sentiment5

February 2023 • Draft n°3 (2023-02-15)

can predict monetary policy surprises and stock returns. Shapiro and Wilson, 2019, estimate
FOMC’s preferences, including the implicit inflation target, from the tone of the language used intranscripts, minutes, and members’ speeches using predefined dictionaries of words associatedwith sentiments (“Bag of Words” or “lexical” approach). Ochs, 2021, uses a measure of sentiment
in FOMC documents to disentangle effects of a monetary policy shock between the actual effects ofmonetary policy and the reaction of private agents to the newly acquired information (measured
by a text-extracted variable). Arseneau et al., 2022 analyze central banks speeches with NLP
approaches to understand how central banks communicate about climate change.
Note that some other works have been conducted on other countries and central banks : Shibamoto,2016 uses a text-mining approach to show that asset prices react to Bank of Japan communication,
while Hubert and Labondance, 2021 finds that the ECB statements’ tone explains monetary surprises.

3.

Computing International Attention Indexes

In this section, we describe data and methods used to compute the International Attention Indexes(IAI).

3.1.

Data

Published three weeks after each FOMC meeting, FOMC minutes are a more complete but
less timely overview of the mechanisms behind monetary policy decisions than the statement,
published immediately after the meeting. As such, they are a major tool of transparency andaccountability policy of the Federal Reserve. Despite this delayed publication, they can have a
sizeable impact on financial markets. Nechio and Wilson, 2016, show that information contained in
FOMC minutes can impact Treasury bond yields at the time of their release, and that these impacts
are unsurprisingly larger when the tone of the minutes differs from the tone of the statement.
We choose to work on FOMC minutes (and not on speeches made by its members or press
conferences by its chair) for three main reasons. First, our aim is to study whether discussionsof international matters within the FOMC are likely to influence monetary policy decisions, andwe therefore limit ourselves to materials reflecting communication within the Federal Reserve,
not from the FOMC towards outsiders1 . Second, among internal materials, minutes offer thebest compromise between completeness and delay, since the more complete Meeting Transcripts
are published with a longer lag2 therefore preventing us from studying recent periods. Finally,minutes feature a general structure which is stable throughout the period under study (1993-2022),
making comparisons between values at different times both easier and more relevant.
We retrieve text files of the minutes of FOMC meetings from the dedicated section on the Federal
Reserve Board of Governors website. We include the minutes of conference calls held by the FOMC
1 Additionally, while we recognize that all central bank communication is political communication, we assume thatsince these documents, including the minutes, reflect internal discussion and are released with a delay, they are less prone
to suffer from the biases usually highlighted in central bank communication analysis, such as carefully chosen words.
2 As of January 2023, the latest transcripts published date back to mid-2016.

6February 2023 • Draft n°3 (2023-02-15)

in case of emergency meetings (mainly during economic or financial crises) that are included at
the end of the minutes of the following meetings. We also remove the list of all attendants of themeeting (included at the start of every document). Our dataset comprises 238 meetings, around
30,000 paragraphs and 60,000 sentences. We transform each html document into a text file, whichenables us to read the raw textual content and process it with Python.
All economic data time series were retrieved from the Atlanta Fed’s Taylor Rule Utility website(Higgins, 2016), which includes data from the Federal Reserve, Bureau of Economic Analysis andCongressional Budget Office, among others.
3.1.1

Data Preprocessing

To efficiently use text data in computational textual analysis models, it is necessary to pre-process
our raw text to "normalize" it. Our preprocessing 3 follows standard procedures for Natural
Language Process projects, applied in much of the literature (see Thorsrud, 2020, or Handlan,
2020, for example).
Cleaning and stemmingFirst, stopwords (such as the, a, ...) words which do not bring any meaning to the sentence, areremoved. We also remove numbers from the text. Words are then lowercased and tokenized.
Finally, we stem all words, to reduce inflected words to their root form. For example, stemming
transforms dollars into dollar or fairly into fair. To stem our text, we use the Snowball Stemmer(Porter, 2001), a stemmer which builds on the Porter stemmer and is widely accepted to be more
efficient, for example at stemming adverbs. To clarify how it works, we provide examples of this2-step process in Table 1 below.
A meeting of the Federal Open
Market Committee was held.

meeting Federal Open
Market Committee held.

meet feder open market
committe held

Table 1: Examples of the 2-step preprocessing of a sentence

Vectorization
Since machine learning models are not capable of processing raw text but only numbers, we haveto transform our sentences into vectors. For that, we select two methods whose results will becompared in our empirical analysis: Term Frequency - Inverse Document Frequency (TF-IDF) andWord2Vec.
TF-IDF vectorization, based on a given corpus D, transforms a given sentence d into a vector ofsize n (the number of different words in the corpus) by including for each word t in the corpus ameasure of its importance in the sentence, denoted tfidf(t, d, D). TF-IDF is the combination of two
measures: Term Frequency, put forward by Luhn, 1958, and Inverse Document Frequency, firstdevised by Sparck Jones, 1972. For a term t in a document d in a corpus D, we define:t f id f (t, d, D ) = t f (t, d) × id f (t, D )(1)where:
3 A diagram of our preprocessing is featured in Figure 6, Appendix A.

7

February 2023 • Draft n°3 (2023-02-15)f• t f (t, d) = ∑ ′ t,df ′ is the relative frequency of term t within sentence d, where f t,d is the raw
t ∈d t ,dcount of term t in sentence d.
|D|• id f (t, D ) = log |{d∈ D:t∈d}| is the inverse document frequency of term t in corpus D, where| D | is the total number of documents in the corpus4 .
Word2Vec, published in Mikolov et al., 2013, is a word embedding method: it transforms words
into vectors by using a neural networks to infer word meanings from a corpus of text. The method
generates a vector space, where, ideally, words are positioned to be close in the space (in terms ofcosine similarity) if they are close in meaning. The vector space means that the representation cansubtract or add vectors to each other. For example, we have: king - man + woman = queen.
Both vectorization methods need to be trained on a given corpus of textual data: TF-IDF derives
frequency from a corpus and Word2Vec needs to learn word association. In our case, both trainings
are performed on the corpus of cleaned, stemmed minutes from 1993 to 2018. Details on thosevectorization methods are featured in Appendix C.

3.2.

NLP Methods

We describe here the two NLP methods that we carry out after the vectorization step of the FOMC
minutes, namely the dictionary approach and the classification with supervised models.
3.2.1

Dictionary countIn this first method, we track simple counts of international-related words contained in a given
dictionary5 . For each FOMC document, we count the number of occurrences of the dictionary’s
words contained in the document and divide it by the total number of words. This ratio is our
first indicator of attention to foreign matters by the FOMC, shown in Figure 3 and defined by:∑ 1w ∈ D

IAI M =

w∈ M

| M|

(2)

where M is the set of all words in stemmed minutes and |.| denotes the cardinality of a set. To
obtain a quarterly time series, we perform this count on MQ , the set of all words in stemmed
minutes of a given quarter Q that includes two FOMC meetings.
Table 2 lists the words and groups of words we use in this dictionary count, organized in threedifferent categories: foreign-exchange related, trade-related, others. Appendix E reports the numberof times each search phrase appears in the corpus. Obviously, the choice of the dictionary is
crucial in this exercise. In most of the literature on dictionary count, words are chosen according
to economic intuition. To justify our dictionary, we follow an algorithm inspired by Aruoba andDrechsel, 2022, detailed in Appendix B.
4 The denominator (number of documents where the term t appears) is commonly adjusted to 1 + |{ d ∈ D : t ∈ d }| toavoid division by 0 if the word is not in the original corpus.
5 While it is true that the index depends of the phrases selected, our methodology is robust to small changes in the
dictionary. The effects of such changes are discussed in Appendix F.

8February 2023 • Draft n°3 (2023-02-15)

Foreign Exchange

Trade

Othersdepreciation
currency
foreign currencies
foreign exchange
dollareuro
poundsterling
renminbi
rubleinternational trade
imports
exportsexternal sector

international
globalforeign economies
emerging market
EME
advanced economies
chinarussiaeurope
india
japan
asia
world
abroadTable 2: Dictionary used in the study.

3.2.2

Classification with supervised models

In this second NLP method to construct international attention indicators from our textual data,
we divide the text into sentences 6 , which we then classify using Machine Learning models as
either relating to international matters or not7 . Once sentences are classified, we compute theratio of the number of words in a text belonging to international-classified sentences on the total
number of words in the text. This ratio is defined as our international attention indicator for theminutes considered.
∑ 1cl (s)=1 |s|

IAI M =

s∈ M

∑ |s|

,

(3)

s∈ M

where M is the set of all sentences in stemmed minutes, s is a sentence (set of words) and Cl is thesentence classifier that returns 1 if the sentence is classified as international-related.To obtain aquarterly series, we perform this count on MQ , the set of all sentences in stemmed minutes of agiven quarter Q.
Building classification-based indexes is essentially a four-steps process : (i) labeling data to train
the algorithms, (ii) training algorithms on a training set, (iii) determining the most relevant ones
using a test set and (iv) finally computing the classification of all sentences and the international
environment index.
Data Labelling
In a first step, we manually label a total of 4,300 sentences to classify them between those which
relate to the international situation (class 1, or "Positive") and those which do not (class 2, or"Negative"). To avoid labeling only data from a specific time period or specific part of the
6 Extracted from the text using the nltk library in Python (Bird et al., 2009)
7 We performed tests before the manual labeling to decide whether to use sentences or paragraphs as our reference
atom for the study, and decided for sentences. Tests for paragraphs are featured in Appendix H.

9February 2023 • Draft n°3 (2023-02-15)

minutes structure, we shuffle the dataset and label sentences that have been randomly chosen.
We also avoid inserting a forward-looking bias on recent events by only labeling data until 2018.
Descriptive statistics of labeled data and total data are displayed in Table 3 below:

Total Number
Average Length (in words)
Number of positives

Labeled data

Total data

4314
11.55
401 (9.3%)

66557
12.66

Table 3: Descriptive statistics of sentences and labeled sentences. Positive means relating to international situation.

Since the dataset of labeled data is biased (there are 90.7% of non-international-related sentences
and 9.3% of international-related sentences), we rebalance it by keeping only 1/9th of the negative
observations. Indeed, we find that even though the imbalance is not extreme8 , the models trainedon a dataset on which we do not apply this bias correction tend to exhibit a very low recall(percentage of positives well predicted by our model), i.e. many positive observations are likely to
be classified as negative9 .
Classification algorithms training
We first divide the dataset of minute sentences between a training dataset (two thirds of thedataset) and a testing dataset (the remaining third of the sentences). The attribution of a sentence
to one set or the other is done randomly. The training dataset is the one the algorithms will haveaccess to estimate parameters, while the testing dataset will be used to asses their performance.
For every algorithm, the parameters are determined via a k-fold Cross Validation approach, as it iscommon in Machine Learning. The training dataset is divided into five subsets, and the algorithm
is trained on four of them. It is then tested on the subset that was left out, and its performance on
this subset is assessed. We retain the model version that performs better on its testing subset. We
also perform a grid search of hyperparameters, where every combination of hyperparameters is
tested by cross validation.
Here we test two vectorization methods, TF-IDF and Word2Vec, and three machine learning classifiers : Naive Bayes, Logistic Regression, Random Forest10 . That makes six pipelines (combinationof a preprocessing and an algorithm).
We add a seventh pipeline: Dictionary-based naive classification. In this pipeline, we classify
sentences as positive or negative based on whether or not they contain one of the phrases in theinternational dictionary described in Table 2. Each sentence that contains at least one word orgroup of word belonging to the dictionary is classified as international-related.
8 Extreme imbalance would be defined in Machine Learning literature as 1, 2 or 5% of class 1 observations.
9 We could have used a resampling technique to augment the number of positive observations in our unbalanced labeleddataset. Since the models we use do not require huge volumes of labeled data and more manual labeling is possible for our
minutes sentences dataset, we decided against this option, which never reaches the results obtained with a non-augmented
unbiased dataset, as shown by Baesens et al., 2022
10 More information on these algorithms can be found in Appendix D

10February 2023 • Draft n°3 (2023-02-15)

Results of classification pipelines on the testing set
We therefore compare the results of seven pipelines, using standard classification metrics: Precision, Recall, F1 Score. For every class, Precision is the proportion of true positives (i.e. rightly
classified in this class) on all predicted positives, Recall is the proportion of actual positives thathave been predicted as such, and F1 Score is the harmonic mean of precision and recall. All metrics
are reported in Table 4 and have to be maximized. We can also compute a macro average of every
metric for the whole dataset (not just one class), detailed results of the seven pipelines on thetesting dataset are featured in Appendix G.

Vectorization
Word2Vec

TF-IDF

Precision

Recall

F1-Score

Logistic Regression
Naive Bayes
Random Forest
Logistic Regression
Naive Bayes
Random Forest

0.57700.28410.78520.89870.84270.90770.51540.50000.50430.88430.83780.8971

0.36170.36240.37170.88920.81910.9011

Dictionary Classification

0.86920.85320.8582NLP Algorithm

Table 4: Results of the seven pipelines

Results show that the models using Word2Vec for vectorization perform poorly on our test dataset.
One explanation could be that the training dataset of the Word2Vec algorithm, that is our corpus,
is not large enough for the neural network to learn words association. This is why we will only
focus on the TF-IDF vectorization approach in our results. As regards the TF-IDF-based pipelines,
performance for Naive Bayes seem to be lower than other algorithms, as it performs worse in
Precision and Recall criteria, and Random Forest seems to perform best, boasting the best resultsin terms of both Precision and Recall criteria.
Our Naive Dictionary classifier has performance on par with the best models in terms of precision,
which validates this dictionary-based approach: the phrases selected in our dictionary are relevant
to detect the occurrences of attention to the international situation in the FOMC minutes.
We therefore choose to built indexes with four pipelines: the Naive Dictionary classifier and allclassifiers based on TF-IDF.

3.3.

International Attention Indexes

In this subsection, we present and discuss the various versions of our International Attention
Index (IAI). Indexes stemming from our dictionary count approach described in section 3.2.1 andthe four classification methods described in section 3.2.2 are shown in Figures 1 and 2.
By eye-balling thos graphs, we note that spikes in the indexes, that is periods of accrued international attention in FOMC minutes, match the major international events of the past three decades.
In particular, there is a good fit with crises of any type outside the U.S.: we point out spikes at thetime of the Latin-American crisis of the mid 1990s, the Asian crisis, the 2008-09 Great Recession,11

February 2023 • Draft n°3 (2023-02-15)

Figure 1: International Attention Indexes obtained with dictionary count and dictionary classifier.

Figure 2: International Attention Indexes obtained with sentences classification through Machine Learning methods.

the European debt crisis in the 2011-13 period, the concerns about the Chinese economy in 20152016, as well as both recent crises, namely the Covid pandemic and the Russian invasion of Ukraine.
The size of these spikes differs depending on the version of the index. The relatively small rise
seen during the Great Recession in dictionary-based models (relative to the Machine Learning
based ones) might come from a different type of attention in these years (through swap lines
with foreign central banks for example) that is not as well captured in our dictionary. Similarly,
Machine Learning based models are able to catch a significant spike during the Covid crisis (aperiod for which they have seen no training data, since we only labeled sentences from 1993 to2018) while dictionary count does not move.
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All indexes tend to show an asymmetric behaviour in the sense that discussions about the international environment seem to take place mainly when there are negative news on foreign economic
activity or markets11 .

Figure 3: Value of the International Attention Index for dictionary count method and sub-dictionaries counts.

One of the advantages of the dictionary approach is that we can split international words into
categories that sum up to the global index. Thus we decompose the dictionary-based IAI into
three main categories, namely trade, currency and others (see Table 2). Figure 3 shows thisdecomposition of the IAI into specific channels through which foreign events might affect the
Fed monetary policy discussions. We first note a long-run decline in the component related tocurrencies. This was the topic of discussions before the 2000s, maybe related to the introduction of
the euro as competitor for the dollar, but since then it does seem to be an issue among FOMC
members. Let’s just note the spike in 2013q1 at the end of the European debt crisis, a long periodof dollar over-evaluation with respect to the euro. In comparison, the trade component tends toshow more spikes, the last one being between mid-2015 and beginning 2016 amidst concerns on
the Chinese economic growth and the renminbi. Interestingly, the trade war between the U.S.
and China/Europe started by the Trump’s administration in 2018 didn’t affect discussions among
FOMC members. Increase in tariffs from imports of those countries was not perceived as a concern
for monetary policy by Fed’s officials.
The last component, named others is driving the headline value of the international attention index
since the mid 2000s. This is likely due to the fact that most of the mentions of foreign crises link
the name of the involved countries with broad economic concepts (growth, inflation, etc.), which
we could not include in our dictionary because these concepts are also associated with separate
domestic news in other parts of the documents that are of not of direct interest for our researchwork.
11 Section 3.4 investigates this point by studying the positive or negative sentiments of international-related words, but
we do not conclude on the systematic negativity of attention to international matters.

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February 2023 • Draft n°3 (2023-02-15)

3.4.

Sentiment around international-related words

Against our background, it also tempting to extract a sentiment around some international topics
evoked during FOMC meetings. Sentiment indexes have proved extremely useful for economists
to assess the tone of large textual databases (see Algaba et al., 2020), including for economic
forecasting (Barbaglia et al., 2022). Here, we investigate evidence of a negative bias of Fed attention
to the international economic situation, in the sense that FOMC members are likely to discuss
more about international situation during crises. In this respect, we follow a methodology used
by Aruoba and Drechsel, 2022: for a given set of phrases (our international dictionary in Table2), we isolate the 10 words 12 surrounding each occurrence of any of the phrases and computethe "sentiment" (positivity or negativity) of these words using the Loughran and Mcdonald, 2011
dictionary. The sentiment around a phrase is estimated as the number of positively connotedwords around its vicinity minus the number of negatively connoted ones. In fine, for a given
FOMC meeting, a final international sentiment score is obtained by summing up the sentimentscores of all phrases contained in the related document. Given the structure of our dictionary inTable 2, it is also possible to compute a sentiment index for the three categories, namely Currency,
Trade and Others. All those sentiment indexes are presented in Figure 4.
By looking at Figure 4, we note that the global international sentiment estimated from FOMC
minutes seems to be relatively well balanced until the Great Recession. However, starting from2009, the sentiment index is clearly tilted to the downside. We observe large negative spikes
during the European sovereign crisis, the period of rising concerns about the Chine economy
or during the Covid pandemic. The sequence of international crises seems to have introduce anegative bias in the attention of FOMC members.
12 As a robustness check, we test the size of the "halo", i.e. the number of surrounding words taken into account. Results
remain extremely similar.

Figure 4: Sentiment around international-related words, for the global dictionary and each of our 3 channels (10 words
surrounding each international-related word).

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February 2023 • Draft n°3 (2023-02-15)

Breaking down the global sentiment into the three categorical sentiments, also featured on Figure 4,shows that the sentiment related to category Others is driving the global dynamics. The currencyrelated international sentiment exhibits much less volatility and seems overall balanced. However,
we note negative values during the period of concern about China economic growth in line withrising uncertainties at that time on the composition of the panel of currencies against which therenminbi is managed by Chinese authorities. The trade-related international sentiment does show
a clear pattern and appears well balanced overtime. Interestingly, the trade war launched in 2018
by the Trump’s administration seems to positively affect the sentiment index.

4.

Effects of International Attention Indexes on monetary policy

This section empirically investigates if the attention to international environment by the FOMC
(as measured by the different indexes detailed in section 3.3) can have an influence on monetary
policy decisions. In this respect, we follow a framework in the spirit of Cecchetti et al., 2000 by
estimating a Taylor rule augmented with our International Attention Indexes (IAI). We assess
whether our indexes provide significant information on Fed decisions. We assume in this sectionthat despite the wider range of tools policymakers have now at their disposal (forward guidance,
asset purchases ...), the Fed Funds Rate remains the main monetary policy tool during the period
under study. The estimation sample goes from 1994q1 to 2022q3.

4.1.

Framework

The so-called Taylor rule, initially put forward by Taylor, 1993, estimates the response of Fed
interest rate to macroeconomic conditions. In a non-augmented form, it is the endogenous
response of monetary policy to domestic macroeconomic conditions. The basic version of the
Taylor rule (Equation (4)) linearly relates the nominal Fed Funds Rate rt , to the (core PCE) inflation
rate πt , the inflation target πt∗ , the output gap gt and a to Gaussian white noise ε t in this way:
rt = α + γ(πt − πt∗ ) + βgt + ε t(4)where α can be interpreted as the nominal neutral interest rate that prevails when inflation is at
the target and output gap is closed (i.e. α = πt + rt∗ , where rt∗ is the real neutral interest rate). In
this work, we use an extended version of the Taylor Rule given by Equation (5) , which includes ρ,a smoothing term for the FFR and it , our International Attention Index (IAI)13 .
rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + δit ) + ε t(5)We chose to work on data provided by the Atlanta Fed on its Taylor Rule Utility website andnot on Greenbook forecast like Istrefi et al., 2021, since these forecasts are no longer available
on the Fed website from 2010 onward, and we would also like to study the influence of ourindex in the 2010s and early 2020s. We choose to use as πt∗ the four-quarter core PCE inflation,
constructed by the US Bureau of Economic Analysis (BEA),and as gt the output gap derived
from the Congressional Budget Office’s (CBO) estimate of potential real gross domestic product.
These choices are consistent with the default options provided by of Atlanta, n.d. All variables are
expressed in percent, and our index it is standardized before entering the equation. We estimate
13 Another version of the Taylor Rule, with a time-varying r ∗ , is discussed in Appendix J.

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February 2023 • Draft n°3 (2023-02-15)

this Taylor Rule by Non-Linear Least Squares14 and obtain the results featured in the following
section.

4.2.

Empirical results

4.2.1

Main results

Table 5 presents the results of our various regressions. Estimated parameters of the standard
Taylor rule (column 0) are quite close for the usual calibration based on initial results of Taylor. The
smoothing parameter is highly significant underlying the strong persistence of FFR. The intercept
is close to 4%, meaning that the real neutral interest rate r ∗ can be proxied by a constant around
2% over the 1994-2022 period. We will later relax this assumption in line with recent research(see Laubach and Williams, 2003). The estimated weight of the inflation gap is above 1 (1.3) but
doesn’t appear significantly different from zero. In opposition the estimated coefficient in frontof the output gap is clearly significant. We now integrate our IAIs into the Taylor equation (5).
First, we note that the integration of the IAIs doesn’t change the other coefficients, highlighting
the stability of the equation. Then, we get that the estimated IAI coefficients are always negative,
and generally significant, except for models (1) and (5) (dictionary and Naive Bayes, respectively.
So we can interpret this result as The version of the IAI that yields the best results in terms ofsignificativity, AIC and MSE is model (4), that is the one based on Random Forest classification ofsentences. This is also consistent with the fact that it is the method that performs best in termsof out-of-sample classification (Table 4). In this version, the (standardized) IAI has a coefficientof -8.1, which means that when the index is equal to 0.1, the marginal effect of the international
environment on FFR is about 81 basis points on average. So for example, at the heart of the Global
Financial Crisis, the IAI went to 0.3, meaning that the contribution of the international outlook to
the zero lower bound was about 240 basis point at that time.

4.2.2

Robustness checks

Let’s focus on Model (4), which is the best model according to all criteria and assess some robustness checks.
Let’s first introduce some additional control variables in equation (5) to ensure that our IAI
provides information beyond that of a simple measure of international financial instability. In thisrespect, we estimate the following augmented equation (6)):
rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + δit + ϕst ) + ε t .

(6)where st is an index of global conditions. We consider two indexes: the MSCI World (equity)
index and the Geopolitical Risk (GPR) index of Caldara and Iacoviello, 2022 (both differentiated).
Results are reported in Table 6. While the coefficient for the global equity index is not statisticallysignificant, as for the GPR index, its coefficient is significantly negative meaning that, as expected,
higher geopolitical risk translates into lower rates. But importantly, the coefficient of our IAI isstable across regressions and remains significant when those two control variables are included.
This suggests that our IAI goes beyond the information contained in global equity or geopoliticaltensions.
14 Non-Linear Least Squares, implemented in R with the nls function is based on Bates and Watts, 1988. It estimatesparameters using an iterative procedure, with linearization approximation and a least-squares problem at each step.

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February 2023 • Draft n°3 (2023-02-15)

Dependent
variable
Model

FFR
(Cal)

(0)

(1)

(2)

(3)

(4)

(5)

r t −1

0.85

α

4.0

PCEGapt

1.5

gt

1.0

0.915***(0.024)4.17***(0.557)1.285(0.794)
1.209***(0.244)0.913***(0.024)4.191***(0.548)0.889(0.777)1.251***(0.246)-3.06(2.506)

0.894***(0.026)3.853***(0.44)
0.858(0.598)1.005***(0.195)-4.084**(1.824)0.91***(0.023)3.801***(0.508)1.236*(0.725)0.96***(0.225)-5.185**(2.322)0.903***(0.023)3.529***(0.459)0.922(0.615)0.79***(0.206)-8.149***(2.639)0.92***(0.024)4.121***(0.59)
1.364(0.863)
1.178***(0.257)-3.32(2.693)115
19942022
115.637
16.872911519942022
115.9516.62612

11519942022
113.197
16.23312

115
19942022
110.812
15.9
12

115
19942022
102.444
14.78412

11519942022
115.546
16.56812

itObservations
PeriodAIC
MSE
Taylor Version

Table 5: Taylor rule regression results — Equation 5 specification
Note: The table shows Taylor Rule (Equation 5) results for Non-Linear Least Squares Regression, withdifferent inputs as the international attention index it : (0) is no international environment index, (1) theindex derived from the dictionary count, (2) the index from dictionary classification of sentences, (3) theindex from logistic regression classification of sentences, (4) the index from random forest classificationof sentences, (5) the index from Naive Bayes classification of sentences. The (Cal) column features thecalibrated parameters (from the Atlanta Fed)* p<0.1; ** p<0.05; *** p<0.01.

Then, we study the variation over time of δ, the estimated coefficient of our IAI, in search forpossible shifts during some specific periods. We carry out a recursive analysis starting from theperiod 1994-2005, then we progressively extend the window by adding a new data point, and we
re-estimate the augmented Taylor rule. For each point in time, we report the estimated value of δ,
the coefficient of it , and its 68% confidence interval. Figure 5 shows the evolution overtime of δ̂.
We observe that the estimate is always largely significantly different from zero. The international
background was playing negatively on central bank interest rate before the Global Financial Crisis,
but there is a clear downward shift in 2008 due to the cross-border propagation of the crisis and its
spill-back effect on U.S. monetary policy. In the wake of the GFC, the estimated coefficient stayed
at a higher level in absolute terms, reflecting a stronger sensitivity of policy-makers to external
developments.
Additional results involving Model (4) are reported in the Appendices.
First, it is well known that monetary policy tools have changed since the GFC. Now, unconventional
tools, such as quantitative easing or forward guidance, have joined the toolbox of policy-makers.
To account for this, some researchers have proposed a modified version of central bank interest
rates that reflects additional accommodation in the monetary policy stance. In this respect, Wu and17

February 2023 • Draft n°3 (2023-02-15)

Dependent variable
Model

(Cal)

(4)

(4.4.1)r t −1

0.85

α

4.0

PCEGapt

1.5

gt

1.0

0.903***(0.023)3.529***(0.459)0.922(0.615)0.79***(0.206)-8.149***(2.639)0.912***(0.023)3.397***(0.507)1.154(0.722)0.769***(0.224)-8.111***(2.862)7.538(5.689)

it
MSCIt

FFR
(4.4.2)0.926***(0.024)
3.927***(0.618)1.698*(1.004)
1.167***(0.27)

115
1994-2022
102.444
14.78412

115
1994-2022
101.967
14.46917(4.5.2)0.909***(0.022)3.657***(0.476)1.161*(0.672)0.774***(0.211)-8.115***(2.707)0.92***(0.023)4.329***(0.594)
1.586*(0.882)
1.186***(0.248)-1.619**(0.733)-2.062**(0.959)115
1994-2022
96.66313.81619

115
1994-2022
109.004
15.65220

13.566*(8.068)

Riskt
Observations
PeriodAIC
MSE
Taylor Version(4.5.1)

115
1994-2022
112.3216.10918

Table 6: Taylor rule regression results - with financial situation control variables
Note: The table shows Taylor Rule regression with additional variables to control for the global financial
environment All models use Non-Linear Least Squares Regression, with the same international attention
index. Model (4) features no control, (4.4.1) and (4.4.2) the Quarter over Quarter evolution of the MSCIWorld Equity index, respectively with and without the IAI index included (Equations 17 and 18), (4.5.1)
and (4.5.2) the Quarter over Quarter evolution of the GPR index, respectively with and without the IAI
index included (Equations 19 and 20). The (Cal) column features the calibrated parameters (from theAtlanta Fed).
* p<0.1; ** p<0.05; *** p<0.01.

Xia, 2016, provide a shadow interest rate for the US economy that we integrate into equation (5).
Appendix I present the results obtained when the Fed Funds Rates is replaced as the dependent
variable by the Wu-Xia Shadow Rates. Results are stronger in the sense that the estimated coefficientof the IAI increase to -10.5 and stays highly significant.
Second, the literature on real natural interest rates shows clear evidence of long-run decline inits measure rt∗ , mainly due to structural issues related to population ageing or secular stagnation
hypothesis (see for example Laubach and Williams, 2003). Appendix J explores another versionof the Taylor Rule which incorporates a time-varying rt∗ estimated by Laubach and Williams,
2003 and updated by of New-York, n.d. When rt∗ is integrated in a standard Taylor rule, bothcoefficients of inflation gap and output gap sharply decline, although the one for the output gap
stays significant. There is no change as regards the IAI estimated coefficient that stays at -8.03 (vs-8.15 in the standard version). So results appear extremely robust to the inclusion of time-varying
neutral interest rate.

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Figure 5: Time-varying estimated value δ̂, the IAI coefficient, and its 68% confidence interval in an expanding window.

5.

Conclusions

The U.S. Federal Reserve has purely domestic objectives of price stability and full employment
and is widely considered to pay little attention to the rest of the world. Yet economic analysis andanecdotal evidence suggest that the international situation is likely to play a role in U.S. monetary
policy, or at least is considered to some extent by policy-makers. Therefore, in this paper, wedevelop a quantitative approach to assess the attention paid by members of the Federal Open
Market Committee (FOMC) to foreign economic conditions and its potential effects on monetary
policy decisions from 1993 to 2022.
In this respect, we deploy various Natural Language Processing (NLP) methods of computer-based
textual analysis to construct International Attention Indexes (IAI) from the minutes of FOMC
meetings. Those indexes are proxies of the attention paid by FOMC members to economic situation
abroad. We focus on the FOMC minutes as they reflect internal discussions within the committee
and offer of good trade-off between publication delays on the one hand and completeness interms of policy discussions, on the other hand. After pre-processing this large textual dataset, we
include it into both dictionary-based models and Machine Learning classification models. As an
output, we get various IAIs reflecting the main economic events of the last decades.
In a second step, we use these IAIs to augment standard Taylor rules of monetary policy reaction
to U.S. domestic conditions. Overall, we get that those indexes have a significantly negative effecton the level of Fed Funds Rates. In other words, those indexes reinforce a dovish pattern withrespect to a standard Taylor rule. We interpret those results as evidence that FOMC members
tend to take global conditions into account when reaching a decision. Those results turn out to berobust to various changes, such as the integration of shadow central bank interest rate or the useof time-varying real neutral interest rates.
One possible continuation of this work could be to conduct the same type of study on a longer
time frame to try to identify the longer-term shifts put forward by Eichengreen, 2013. While very
appealing, this potential continuation runs into data-linked obstacles, namely the availability of19

February 2023 • Draft n°3 (2023-02-15)

comparable documents. Minutes of FOMC meetings are available from 1993 onward, but only
Minutes of Actions exist from 1967 to 1992, and Historical Minutes from 1936 to 1967. If historicalminutes are pretty comparable to current-format minutes, the Minutes of Actions are much shorterin length and feature significantly less discussion.

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communications on US treasury rates. Proceedings of the 2016 Conference on Empirical
Methods in Natural Language Processing, 2096–2102. https://doi.org/10.18653/v1/D16-1226
Romer, C., & Romer, D. (2004). A new measure of monetary shocks: Derivation and implications.
American Economic Review, 94(4), 1055–1084. https://doi.org/10.1257/0002828042002651
Sachs, J. D. (1985, January 1). The dollar and the policy mix: 1985 [Brookings]. Retrieved November 8,2022, from https://www.brookings.edu/bpea-articles/the-dollar-and-the-policy-mix1985/
Shapiro, A., & Wilson, D. (2019). Taking the fed at its word: Direct estimation of central bankobjectives using text analytics. Federal Reserve Bank of San Francisco, Working Paper Series,
01–67. https://doi.org/10.24148/wp2019-02

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Sharpe, S. A., Sinha, N. R., & Hollrah, C. A. (2022). The power of narrative sentiment in economic
forecasts. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2022.04.
008
Shibamoto, M. (2016). Empirical assessment of the impact of monetary policy communication on
the financial market, 44.
Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval[Publisher: MCB UP Ltd]. Journal of Documentation, 28(1), 11–21. https://doi.org/10.1108/
eb026526
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on
Public Policy, 39, 195–214. https://doi.org/10.1016/0167-2231(93)90009-L
Taylor, J. B. (1999, January). A historical analysis of monetary policy rules. In Monetary policy
rules (pp. 319–348). University of Chicago Press. Retrieved November 8, 2022, fromhttps://www.nber.org/books-and-chapters/monetary-policy-rules/historical-analysismonetary-policy-rules
Thorsrud, L. A. (2020). Words are the new numbers: A newsy coincident index of the businesscycle [Publisher: Taylor & Francis Journals]. Journal of Business & Economic Statistics, 38(2),393–409. Retrieved November 9, 2022, from https : / / ideas . repec . org / a / taf / jnlbes /v38y2020i2p393-409.html
Wu, J. C., & Xia, F. D. (2016). Measuring the macroeconomic impact of monetary policy at thezero lower bound [Publisher: Blackwell Publishing]. Journal of Money, Credit and Banking,48(2), 253–291. Retrieved January 5, 2023, from https://econpapers.repec.org/article/
wlyjmoncb/v_3a48_3ay_3a2016_3ai_3a2-3_3ap_3a253-291.htm

24February 2023 • Draft n°3 (2023-02-15)

Appendices
A.

Preprocessing

Figure 6: Preprocessing of our text data
(1) Cleaning. Stopwords and numbers removal(2) Stemming. The text is ready to be used as input for Word Counting models.
(3) Vectorization. The text is ready to be used as input for Machine Learning models.

B.

Selecting international-related phrases
1. We sort all words, doubles (sets of two words) and triples (three words) in the documents by
their number of occurrences.
2. We only keep those related to international economic matters.
3. We limit ourselves to phrases above a frequency threshold (above 460 occurrences for words,
230 for doubles, 115 for triples).
4. For phrases contained in or containing other phrases, we select either the containing phraseor the contained: foreign economies, for example, is contained in advanced foreign economies
and contains foreign, and we settle on including foreign economies.

25

February 2023 • Draft n°3 (2023-02-15)

C.

Vectorization Methods

In this section, we give more information on the workings of the two vectorization methods weuse, TF-IDF and Word2Vec. Both methods are implemented in standard python libraries 15 .

C.1.

TF-IDF

TF-IDF vectorization based on corpus D transforms a sentence d into a vector of size n (the numberof different words in the corpus) by including for each word t in the corpus a measure of its
importance in the sentence: its tfidf(t, d, D). An example of this process can be found in Table ??.
tfidf(t, d, D) is the combination of two measures: Term Frequency, put forward by Luhn, 1958 andInverse Document Frequency, first devised by Sparck Jones, 1972. For a term t in a document d, ina corpus D, t f id f (t, d, D ) = t f (t, d) × id f (t, D ).
f

• t f (t, d) = ∑ ′ t,df ′t ∈d t ,dis the relative frequency of term t within document d, where f t,d is theraw count of term t in document d.
|D|• id f (t, D ) = log |{d∈ D:t∈d}| is the inverse document frequency of term t in corpus D, where| D | is the total number of documents in the corpus16 .
As a consequence, for a given word, high weight in TF-IDF is reached by a high term frequency(in the given document) and a low document frequency of the term in the whole collection of
documents; TF-IDF vectorization tends to filter out common terms.
Sentence 1: Inflation today is very high and lasting.
Sentence 2: Inflation today is not high and is temporary.
Sentence 3: Inflation today is entrenched and dangerous.
Term
Inflation
today
is
very
high
and
lasting
nottemporary
entrencheddangerous

1
1
1
1
1
1
1
1
0
0
0
0

Count
2 3
1 1
1 1
2 1
0 0
1 0
1 1
0 0
1 0
1 0
0 1
0 1

1
1/7
1/7
1/7
1/7
1/7
1/7
1/7
0
0
0
0

TF
2
1/8
1/8
1/4
0
1/8
1/8
0
1/8
1/8
0
0

3
1/6
1/6
1/6
0
0
1/6
0
0
0
1/6
1/6

1
0
0
0
0.477
0.176
0
0.477
0.477
0.477
0.477
0.477

IDF
2
0
0
0
0.477
0.176
0
0.477
0.477
0.477
0.477
0.477

3
0
0
0
0.477
0.176
0
0.477
0.477
0.477
0.477
0.477

1
0
0
0
0.068
0.025
0
0.068
0
0
0
0

TF-IDF
2
3
0
0
0
0
0
0
0
0
0.022
0
0
0
0
0
0.060
0
0.060
0
0
0.080
0
0.080

Table 7: TF-IDF Vectorization example
15 We use the sklearn package of Python, developed by Pedregosa et al., 2011 for TF-IDF and the gensim package of
Python, developed by Rehurek and Sojka, 2011 for Word2Vec.
16 The denominator (number of documents where the term t appears) is commonly adjusted to 1 + |{ d ∈ D : t ∈ d }| toavoid division by 0 if the word is not in the original corpus.

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February 2023 • Draft n°3 (2023-02-15)

C.2.

Word2Vec

Word2Vec, published in Mikolov et al., 2013 is a word embedding method: it transforms words
into vectors by using a neural network to infer word meanings from a corpus of text. The method
generates a vector space (generally of several hundreds dimensions), where, ideally, words representations (embeddings) are positioned to be close in the space (in terms of cosine similarity) if they
are close in meaning.
In the vector space, it is possible to infer the semantic proximity of two words from the distance
between their representations in the vector space, usually determined with cosine similarity, i.e. thecosine of the angle between the two vectors. The vector space also means that the representation
can substract or add vectors to each other: in that representation, king - man + woman = queen.

Figure 7: Distance between two vectors in Word2Vec
representation: an example

Figure 8: Vector composition in Word2Vec representation: an example

Here, cos(θ) is the distance between A and B.

The Word2Vec technique can use one either continuous bag-or-words (CBOW) or continuous
skip-gram to produce the representation of a word in the vector space. Both methods use a 3-layer
neural network, and consider both individual words and the context words surrounding them.
They differ in that in the CBOW method the model predicts the current word from its surroundingwords (without taking into account the order of the context words, hence bag-of-words) while
the continuous skip-gram uses the current word to predict its surrounding context words (andweights context words differently based on their place in the text).
Words which are semantically similar (and should be used in similar contexts), will usually sharea large part of their context words and have similar influences on the relative word probabilities in
the model, meaning that they will therefore be positioned in similar embeddings. That explainsthat semantically similar words are represented by similar vectors.

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February 2023 • Draft n°3 (2023-02-15)

D.

Supervised Machine Learning algorithms

In this section, we give an overview 17 of the theory behind the machine learning models we usedin Section 3.2.2, namely Naive Bayes Classifier and Random Forest Classifier.
Both models are supervised learning, which means that the target Y is given. If we represent
the model by h(·), and assume that the objective function to minimize is the Mean Squared Error(MSE), training the model consists in determining the parameters β minimizing the error.

ERR( X, Y, β)

min
β

=

(MSE)

1 m
( h ( x i , β ) − y i )2
m i∑
=1

In the case of a classification algorithm, Y is a vector of 0s and 1s, and the function h(·) returns 0
or 1.

D.1.

Naive Bayes

The Naive Bayes Classifier, often taken as a baseline to evaluate more complex methods (Chakrabortyand Joseph, 2017), is based on applying the Bayes’ rule of conditional probability to determine
the probability that observation ci is a part of class ci . In computing the probability, the assumption that all features are independent from each other is made, hence the Naive denomination.
In a situation with C classes (in our problem, C = 2), c1 , ..., cC , the classifier is based on the
maximising-a-posteriori (MAP) decision rule :h( xi ) = argmax P(c| xi ) = argmax(cc

P ( xi | c ) P ( c )))P ( xin(Independence) = argmax P(c) ∏ P( xi,j |c)c

j =1

P(ci ) is the probability of being assigned to a class (and therefore is constant).
The P( xi,j |c) are the class-conditional likelihood factors for xi being labeled c according to feature j 18 .

D.2.

Random Forest

The random forest classifier operates by constructing multiple decision trees and aggregating their
output.
The idea behind the working of a decision tree is to divide the dataset X based on the xi , j featuresof the xi to minimize the entropy H (Y | X ) within areas of Y defined by the tree, which acts as
the objective function in classification problems. This is done through successive identifications
17 Among multiple others, a more complete explanation of the working of these models can be found in Chakrabortyand Joseph, 2017, Section 3.

28

February 2023 • Draft n°3 (2023-02-15)of the feature x of X which, when used as the split criterion, leads to the highest information
gain I (Y | x ). The decision tree model consists of the sequence of splitting rules and the final value
assignment defined.

I (Y | x ) = H (Y | X ) − ∑ ∈ xv| Xv |H (Y | X v )|m|C

H (Y | X ) = − ∑ p(Y = c| X )log( p(Y = c| X ))c =1

p(Y = c| X ) is the relative frequency of class c observations in X.

While they can be very powerful and can handle complex relations within data, on top of having
the advantage of being relatively interpretable, decision trees can be prone to severe over-fitting.
Indeed, the learning method can lead the model to learn very specific patterns of the training
dataset, which leads to high variance in out-of-sample testing, and therefore large errors.
Random forest models, developed to alleviate this issue, are based on separately training a numberof roughly independent tree models from randomly selected subsamples of X, and make theclassification decision based on majority voting of all different trees. Figure 9 illustrates thisprocess.

Figure 9: Random Forest

Even though trees in the forest are not independent from each other (as they as constructed from
subsets of the same data), the variance of the final prediction is expected to decline compared to asingle decision tree, therefore making random forest a reliable choice for classification.
While they are less interpretable than decision trees19 , as a consequence of the randomness
inherent to the construction of the model, random forests can be seen as a manifestation of thewisdom of the crowd phenomenon, putting crowd intelligence into artificial intelligence.

19 It can be possible, by studying which branch splittings are performed by the trees, to extract information about themost important features, and even economic rationale (Chakraborty and Joseph, 2017).

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February 2023 • Draft n°3 (2023-02-15)

E.

Counts by phrase in the international dictionary

Number of Occurences
dollarcurrency
foreign exchange
foreign currencies
euro
depreciation
pound
renminbi
sterling
ruble

1415
932
488
355
304
206
36
28
17
3

imports
exports
international trade
external sector

1782
1207
179
43

globalinternationalabroadjapan
foreign economies
china
emerging market
europe
EME
asia
world
russiaadvanced economies
india

693
459
377
363
314
278
262
201
188
147
111
54
43
17

Table 8: Counts by phrase.

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February 2023 • Draft n°3 (2023-02-15)

F.

Dictionary count index with variations in the dictionary

To ensure that our results are robust to small changes in the dictionary, we study whether the
random exclusion of 3 of the 28 phrases in the dictionary yields different indexes.
Missing Phrases
Alternative 1
Alternative 2
Alternative 3
Alternative 4
Alternative 5

dollar, asia, EME
abroad, foreign currencies, currency
sterling, emerging market, EME
sterling, foreign exchange, ruble
yuan, india, advanced economies

Table 9: Missing phrases for each of our alternative dictionaries

Table 10 shows the Pearson correlation between indexes obtained with the reference dictionary andthe 5 alternatives. What is shown is that all correlations are high (in the 90s), and even extremely
high (above 98 %) for alternatives 3 to 5. Alternative 1 has the lowest correlation to other indexes,
but this stems from the fact that dollar, the phrase with the second-highest count (Table 8), hasbeen excluded, as well as Asia, which explains why this index is not able to catch the start of thelate 90s Asian crisis while the other alternatives manage to do it, as shown in Figure 10.

Basis
1
2
3
4
5

Basis

1

2

3

4

5

1.000000
0.922090
0.934825
0.982972
0.986841
0.999289

0.922090
1.000000
0.860623
0.911276
0.926918
0.919711

0.934825
0.860623
1.000000
0.904087
0.938426
0.931466

0.982972
0.911276
0.904087
1.000000
0.952997
0.986321

0.986841
0.926918
0.938426
0.952997
1.000000
0.983821

0.999289
0.919711
0.931466
0.986321
0.983821
1.000000

Table 10: Pearson correlation of headline dictionary count (Basis) index and its 5 alternatives

Figure 10: Headline dictionary count index and its alternatives

We conclude from Table 10 and Figure 10 that our dictionary count index is robust to smallchanges in the dictionary, even though more significant changes (exclusion of words with higher
counts) can lead to slightly more different results.
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February 2023 • Draft n°3 (2023-02-15)

G.

Detailed results of classification models

Vectorization

Algorithm
Dictionary

Logistic Regression

TF-IDF

Naive Bayes

Random Forest

Logistic Regression

Word2Vec

Naive Bayes

Random Forest

Precision

Recall

F1-Score

0.821622
0.976744
0.899183
0.874251
0.923077
0.898664
0.972973
0.712500
0.842736
0.886905
0.951456
0.919181
0.736842
0.594203
0.665523
0.764706
0.477273
0.620989
0.661654
0.521739
0.591697

0.987013
0.717949
0.852481
0.948052
0.820513
0.884282
0.701299
0.974359
0.837829
0.967532
0.837607
0.902570
0.636364
0.700855
0.668609
0.253247
0.897436
0.575341
0.571429
0.615385
0.593407

0.896755
0.827586
0.8621710.909657
0.868778
0.889218
0.815094
0.823105
0.8191000.925466
0.890909
0.908187
0.682927
0.643137
0.663032
0.380488
0.623145
0.501817
0.613240
0.564706
0.588973

Class
0.0
1.0
Macro Average
0.0
1.0
Macro Average
0.0
1.0
Macro Average
0.0
1.0
Macro Average
0.0
1.0
Macro Average
0.0
1.0
Macro Average
0.0
1.0
Macro Average

Table 11: Classification results: detailed results

32February 2023 • Draft n°3 (2023-02-15)

H.

Classification of paragraphs

We test the naive dictionary classification method on paragraphs, i.e. we classify paragraphs as
positive or negative based on whether or not they contain one of the phrases in the international
dictionary described in Table 2. We then compute the ratio of words in paragraphs classified as
positive on the total number of words, and obtain the index featured in Figure 11.

Figure 11: Index of Fed attention to international, computed via paragraphs classification.

A quick comparison with the naive dictionary classification on sentences shows that this classification option is less promising. Indeed, the highs of the index are close to 60%, with a meanvalue around 30%, which would mean that about half of the minutes conversation is directly
related to international matters (something that contradicts basic assumptions about the role of
the Fed). This might be due to the size of minutes paragraphs, which can start with discussionof domestic matters and end with an international situation briefing. Therefore, classifying thewhole paragraph as related or not to international matters does not make economic sense. We
also observe shocks in the value of the index, for example at the start of 1999. These jumps do notseem to be related to international events, but rather to structural breaks in minutes structure andparagraph size (since we use the paragraphs delimitations provided on the Fed website).

33

February 2023 • Draft n°3 (2023-02-15)

I.

Results of Taylor Rule Regression with Shadow Rates

Since the monetary policy of the late 2000s and early 2010s has been marked by rates that stayed
for a long time at the Zero Lower Bound, we want to investigate in this section whether it affected
the potential significativity of the Taylor rule. To offset the potential hurdles posed by the monetary
policy of this period and account for the Zero Lower Bound, we test another dependent variablein the Taylor rule by replacing the Fed Funds Rates by the Shadow Rates put forward by Wu andXia, 2016.
They compute an alternative to the Fed Funds Rates to reflect the stimulus provided by the Fed
through quantitative easing in the period when additional stimulus through the conventional
channel of monetary policy (lowering interest rates) was not available because of the Zero Lower
Bound. The interest rate derived from Wu and Xia, 2016 summarizes monetary policy in the Zero
Lower Bound years and enables us to link monetary policy (with all its tools) to the economy.
Dependent variable
Model

(Cal)

r t −1

0.85

α

4.0

PCEGapt

1.5

gt

1.0

FFR
(0)

Shadow Rates(0)(4)

0.915***(0.024)4.17***(0.557)1.285(0.794)
1.209***(0.244)0.903***(0.023)3.529***(0.459)0.922(0.615)0.79***(0.206)-8.149***(2.639)0.921***(0.026)4.204***(0.856)1.979(1.286)1.459***(0.374)0.916***(0.025)3.421***(0.76)
1.615(1.1)
0.945***(0.34)-10.483**(4.449)115
1994-2022
115.637
16.8729

115
1994-2022
102.444
14.78412

115
1994-2022
193.755
33.279115
1994-2022
186.188
30.622itObservations
PeriodAIC
MSE
Taylor Version(4)

Table 12: Results of Taylor Rule Regression with Shadow Rates as the dependent variable
Note: The table shows Taylor Rule (Equations 4 & 5) results for Non-Linear Least Squares Regression,
with two possible dependent variables (Fed Funds Rates, Shadow Rates) and different inputs as theinternational attention index it : (0) is no international environment index, (4) the index from random
forest classification of sentences. The (Cal) column features the calibrated parameters (from the AtlantaFed).
* p<0.1; ** p<0.05; *** p<0.01.

We conclude from Table 12 that when used to explain variations in the Shadow Rates of Wu andXia, 2016, our Fed IAI retains significativity and the conclusions of 4.2 still hold : attention to
the international situation has a negative effect on Shadow Rates. A rise in the Fed’s attention to
the global economy translates into a more accommodating monetary policy (conventional andunconventional).

34

February 2023 • Draft n°3 (2023-02-15)

J.

Results of Taylor Rule Regression with time-varying r ∗In this section, we estimate a different version of the Taylor Rule which differs from Equation 5 inthat α is replaced by a time varying neutral interest rate.
rt = ρrt−1 + (1 − ρ)((rt∗ + πt ) + γ(πt − πt∗ ) + βgt + δit )(7)

We take as rt∗ the Laubach-Williams model 1-sided estimate, which is based on Laubach andWilliams, 2003 and updated by the Federal Reserve Bank of New York (of New-York, n.d.).
Using the same non-linear least square method as in Section 4, we obtain the following results.

Dependent variable
Model

FFR
(Cal)

r t −1

0.85

α

4.0

PCEGapt

1.5

gt

1.0

FFR (TVR)(0)(4)
0.913***(0.027)0.919***(0.025)0.567(0.808)0.828***(0.191)0.537(0.82)
0.604***(0.197)-8.034**(3.569)115
1994-2022
117.02917.377
10

115
1994-2022
107.8
15.76
13

it
Observations
PeriodAIC
MSE
Taylor Version

Table 13: Results of Taylor Rule Regression with time-varying R∗
Note: The table shows time-varying R∗ Taylor Rule (Equation 7) results for Non-Linear Least Squares
Regression, with different inputs as the international attention index it : (0) is no international environment
index, (4) the index from random forest classification of sentences. (.1) and (.2) in the model name denoteattention to a specific period : (.1) is the 1994-2008 period, (.2) the 2009-2022 period. The (Cal) column
features the calibrated parameters (from the Atlanta Fed).
* p<0.1; ** p<0.05; *** p<0.01.

The results are globally on par with those in Table 5: the coefficient our our IAI is negative andsignificant (although less than in the standard version of the Taylor rule) and the conclusions ofSection 4 hold in terms of the effect of international attention. Yet, Table 13 shows that the PCE
gap is never statistically significant.
Overall, our choice to center our study on the version of the Taylor rule presented in Equation
12 is vindicated: even though it might be seen as a model improvement, this version does notyield more satisfying results in the (0) model (without IAI) and offers similar interpretations of
the effect of our IAI.

35

February 2023 • Draft n°3 (2023-02-15)

K.

Results of Taylor Rule Regression with unemployment gap
Dependent variable
Model

(Cal)

r t −1

0.85

α

4.0

PCEGapt

1.5

gt

1.0

it

FFR
(4)
0.899***(0.026)3.592***(0.478)1.142(1.05)
0.75***(0.231)-8.067***(2.771)ut
Observations
PeriodAIC
MSE
Taylor Version

104
1994-201986.07212.41312(4.3)
0.935***(0.024)
2.94***(0.715)2.244(1.724)-13.955**(5.656)0.092(0.209)104
1994-201991.33413.05715

Table 14: Results of Taylor Rule Regression with unemployment gap
Note: The table shows Taylor Rule regression (Equations 12 and 15) results for Non-Linear Least Squares
Regression, with the same international attention index and two options for the economic slack variable :(4) uses the output gap, (4.3) the unemployment gap (from the Congressional Budget Office). The (Cal)column features the calibrated parameters (from the Atlanta Fed).
* p<0.1; ** p<0.05; *** p<0.01.

Results in Table 14 show that including the unemployment gap as the slack measure in an augmented Taylor Rule does not lead to improve performances in terms of AIC and MSE. In such amodel, the unemployment gap does not seem to be statistically significant.

36February 2023 • Draft n°3 (2023-02-15)

L.

Different versions of Taylor Rule Regression Used

In the equations below:• rt is the Fed Funds Rate (or the Shadow Rate if relevant)• πt is the (core PCE) inflation rate
• πt∗ is the inflation target• gt is the output gap• ρ is a smoothing term for the FFR• α is a constant playing the role of the neutral interest rate
• R∗t is the time-varying neutral interest rate
• it is our International Attention Index (IAI)• ut is is the unemployment gap• st is a variable derived from an equity index• vt is a variable derived from a risk index

L.1.

L.2.

L.3.

Non-augmented versions
rt = πt + R∗t + γ(πt − πt∗ ) + βgt(8)rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt )(9)rt = ρrt−1 + (1 − ρ)(( R∗t + πt ) + γ(πt − πt∗ ) + βgt )(10)Augmented versions
rt = πt + R∗t + γ(πt − πt∗ ) + βgt + δit(11)

rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + δit )(12)

rt = ρrt−1 + (1 − ρ)(( R∗t + πt ) + γ(πt − πt∗ ) + βgt + δit )(13)

With Unemployment Gap
rt = πt + R∗t + γ(πt − πt∗ ) + βut + δit(14)

rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βut + δit )(15)

rt = ρrt−1 + (1 − ρ)(( R∗t + πt ) + γ(πt − πt∗ ) + βut + δit )

(16)

37

February 2023 • Draft n°3 (2023-02-15)

L.4.

With additional control variables
rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + δit + σst )(17)

rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + σst )(18)

rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + δit + µvt )(19)

rt = ρrt−1 + (1 − ρ)(α + γ(πt − πt∗ ) + βgt + µvt )(20)38