# load packages, paths, and the macro plateform data
library(tidyverse)
library(data.table)
library(here)
library(kableExtra)
library(tidytext)
library(DT)
data_path <- "C:/Users/goutsmedt/Documents/MEGAsync/Research/R/projets/data/green_ecb_responsiveness"
source(here(path.expand("~"), "green_ecb", "function", "functions_for_topic_modelling.R"))
K = 120
# load the topics stats and gamma attributes
lda <- readRDS(here(data_path, "topic_modelling", paste0("LDA_", K, ".rds")))
lda_data <- readRDS(here(data_path,
"topic_modelling",
paste0("LDA_", K, "_data.rds"))) %>%
as.data.table() %>%
.[, period := case_when(between(date, "1998-11-20", "2011-11-08") ~ "Period_1",
between(date, "2011-11-08", "2021-09-01") ~ "Period_2",
between(date, "2021-09-01", "2023-02-01") ~ "Period_3")]
lda_proximity <- readRDS(here(data_path,
"topic_modelling",
"similarities_LDA.rds"))
data_year_subset <- lda_data %>%
filter(! is.na(period)) %>%
.[,`:=` (mean = mean(gamma),
st_err = sd(gamma)/sqrt(length(gamma))), by = .(topic, period)] %>%
.[order(period, desc(mean)),] %>%
distinct(topic, topic_name, inflation_topic, period, mean, st_err) %>%
.[, rank := 1:.N, by = period] %>%
pivot_wider(names_from = "period", values_from = c("mean", "st_err", "rank"))
topics_per_speech <- lda_data %>%
.[, gamma_speech := mean(gamma), by = .(topic, file)] %>%
select(topic, file, title, year, date, speaker_cleaned, gamma_speech, pdf_link, period) %>%
unique()
# Calculate top frex and lift value for the topic
beta_lda <- tidy(lda, matrix = "beta") %>%
group_by(topic) %>%
slice_max(order_by = beta, n = 15, with_ties = FALSE) %>%
mutate(rank_beta = 1:n()) %>%
select(topic, term_beta = term, rank_beta, beta)
frex_lda <- calculate_frex(lda, 15, 0.5, topic_method = "LDA") %>%
group_by(topic) %>%
slice_max(order_by = frex, n = 15, with_ties = FALSE) %>%
ungroup() %>%
select(term_frex = term, rank_frex = rank, frex)
lda_words <- beta_lda %>%
bind_cols(frex_lda)
# Most representative speech per period
top_speech_paragraphs_period <- lda_data %>%
select(topic, document_id, title, date, speaker_cleaned, period, pdf_link, paragraphs, period, gamma) %>%
filter(! is.na(period)) %>%
group_by(period, topic) %>%
slice_max(gamma, n = 5, with_ties = FALSE) %>%
mutate(title_link = paste0("[", title, "](", pdf_link, ")"),
paragraphs = str_trunc(paragraphs, 1500, "right") %>% str_squish(),
gamma = round(gamma, 3)) %>%
ungroup()
top_speech_period <- topics_per_speech %>%
select(topic, file, title, date, speaker_cleaned, period, pdf_link, gamma_speech, period) %>%
filter(! is.na(period)) %>%
group_by(period, topic) %>%
slice_max(gamma_speech, n = 5, with_ties = FALSE) %>%
mutate(title_link = paste0("[", title, "](", pdf_link, ")"),
gamma_speech = round(gamma_speech, 3)) %>%
ungroup()Topics that deal with inflation are displayed first. Within topics dealing with inflation and topics that deal with other subjects, topics are ranked from the most prevalent topic to the less prevalent one.
for(i in (1:K)[-99]){
topic_stats <- data_year_subset %>%
filter(topic == i)
################ Beginning of the template ######################
cat("## ", paste0("**Topic ", topic_stats$topic, "**: ", topic_stats$topic_name), "\n")
cat("###", "Describing Topics in general \n\n")
cat("The most common terms according to different indicators:\n\n")
print(kable(filter(lda_words, topic == i)) %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F)))
cat("\n\n")
cat("###", "Describing Topics for the 3 periods \n\n")
cat("We list the 5 most representative paragraphs for each period:\n\n")
print(kable(filter(top_speech_paragraphs_period, topic == i) %>% select(title_link, period, date, gamma, paragraphs)) %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 12))
cat("\n\n")
cat("The most representative speeches:\n\n")
print(kable(filter(top_speech_period, topic == topic_stats$topic) %>% select(title_link, period, date, gamma_speech)) %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 12))
cat("\n\n")
}The most common terms according to different indicators:
| topic | term_beta | rank_beta | beta | term_frex | rank_frex | frex |
|---|---|---|---|---|---|---|
| 1 | model | 1 | 0.1004713 | phillips | 1 | 0.9998247 |
| 1 | curve | 2 | 0.0616441 | phillips curve | 2 | 0.9996495 |
| 1 | phillips | 3 | 0.0485457 | model | 3 | 0.9994741 |
| 1 | phillips curve | 4 | 0.0474932 | slope | 4 | 0.9989484 |
| 1 | estimate | 5 | 0.0245711 | dsge | 5 | 0.9985093 |
| 1 | slack | 6 | 0.0226999 | curve | 6 | 0.9984210 |
| 1 | dynamic | 7 | 0.0162677 | keynesian | 7 | 0.9983352 |
| 1 | relationship | 8 | 0.0149812 | economic slack | 8 | 0.9978968 |
| 1 | economic slack | 9 | 0.0128761 | dsge model | 9 | 0.9977196 |
| 1 | slope | 10 | 0.0120575 | specification | 10 | 0.9974565 |
| 1 | base | 11 | 0.0111219 | slack | 11 | 0.9962243 |
| 1 | variable | 12 | 0.0107710 | nairu | 12 | 0.9960541 |
| 1 | keynesian | 13 | 0.0091338 | flat | 13 | 0.9951813 |
| 1 | unemployment | 14 | 0.0088999 | relationship | 14 | 0.9946386 |
| 1 | structural | 15 | 0.0078473 | policy analysis | 15 | 0.9945592 |
We list the 5 most representative paragraphs for each period:
| title_link | period | date | gamma | paragraphs |
|---|---|---|---|---|
| Jürgen Stark: Lessons for central bankers from the history of the Phillips Curve | Period_1 | 2008-06-16 | 0.375 | indeed, the great inflation episode was associated with the breakdown of empirical phillips curves, as equations estimated over the previous, more stable period lost predictive power and had to be adjusted judgementally by forecasters in policy institutions. the shifting nature of the unemployment-inflation relationship around the time of the great inflation is clearly apparent even to the naked eye from the bottom-right panel of figure 1. whereas from the second half of the 19th century up until mid-1960s inflation and unemployment had systematically negatively co-moved in both the united kingdom and the united states, around the time of the great inflation they started to move upward in lockstep. as pointed out by lucas and sargent in a paper presented in 1978 at a conference organised by the boston fed, 8 the inflation of the 1970s represented, under this respect, a sort of large-scale experiment for the old consensus. for lucas and sargent the verdict of this experiment was clear: the “halcyon days” 9 of post-keynesian macroeconomics, with its belief in the exploitability of the phillips trade-off, were gone for ever. over subsequent years, and until mid-1990s, the phillips curve essentially disappeared from frontier research. alienated by the disappointing performance of the phillips curve, and following the lead of kydland and prescott, 10 frontier researchers were preoccupied with building flexible-price general equilibrium models driven by technological impulses. w… |
| Jürgen Stark: Delivering price stability - benefits and challenges | Period_1 | 2007-12-04 | 0.215 | long term inflation and economic performance empirically, the relationship between inflation and economic performance in the long run has been extensively studied. simplifying, one could argue that the famous phillips curve has been rotating in the minds (and charts) of macroeconomists in the last 35 years or so. it was positively sloped when i studied macroeconomics: a little bit more of inflation was supposed to bring a little bit more of real income as a permanent effect. then, it became vertical, as rational expectations advocates belied the samuelson-solow consensus model and drew attention onto a principle that had long been discovered and forgotten: monetary neutrality. it is futile for a central bank to manoeuvre inflation in the hope of systematically stimulating growth, as this policy is doomed to failure in the end, and produces inflation as the only certain outcome. in more recent years the measured slope has bent to negative. many of the papers presented at this conference give indications that a negative phillips curve in the long run not only conforms to evidence, but is born out of general equilibrium micro-foundations. to be sure, at lower rates of inflation, the empirical long-term link between growth and inflation seems difficult to ascertain, at least on the basis of reduced-form, non-structural analysis. this might motivate the fact that for years – in the absence of data-congruent structural models – the slope issue was considered unsettled. i should … |
| Jürgen Stark: Lessons for central bankers from the history of the Phillips Curve | Period_1 | 2008-06-16 | 0.188 |
|
| Lucas Papademos: The science of monetary policy ¿ past advances and future challenges | Period_1 | 2007-09-24 | 0.187 | such model and data uncertainty is clearly warranted. model uncertainty suggests that further analysis of the robustness of the effects of monetary policy actions across a variety of models would be valuable. recent research has shown that central banks should moderate the responsiveness of their policy decisions to real activity when underlying data are known to be subject to measurement error. after all, a strong policy response to mismeasured data will induce unnecessary fluctuations in the economy. in view of this, the weight given to the individual information variables should depend on how precisely those variables are measured. this is especially applicable to variables that are not directly observable, but which are relevant for policy formulation, such as the potential output growth and the equilibrium values of the real unemployment rate and the real interest rate. it would therefore be not advisable for central banks to heavily rely in their policy decisions – and in the respective communication – on models or policy rules that place an inordinate weight on such unobservable parameters, which are difficult to measure in real time, and subject to considerable uncertainty of the underlying data. my final remark on model and methodological challenges concerns the fact that most macroeconomic models today are solved and estimated in linear form, essentially relying on the linear stochastic difference approach used in macroeconomics since the 1950s. again, this featu… |
| Jürgen Stark: Main challenges for monetary policy in a globalised world | Period_1 | 2008-03-31 | 0.170 | 5 galí and monacelli (2005) model a small open economy trading with an infinite number of other foreign economies. in the model’s phillips curve, they show that the more open the economy is and the more substitutable domestic goods are with regard to foreign goods, the lower the coefficient for the domestic output gap becomes. research conducted at the imf and the bis (imf (2006) and borio and filardo (2007)) finds supporting evidence for the hypothesis that the increase in international trade and production specialisation has increased the importance of global indicators of economic slack and inflation, relative to domestic indicators, in explaining domestic inflation in developed countries. ciccareli and mojon (2005) also argue that in a sample of oecd economies domestic inflation is driven by a global inflation process, which at short horizons is a function of global real developments. the robustness of such evidence remains controversial, however, as empirical estimates depend on the specification of the estimated phillips curve (ihrig et al. (2007)). 6 batini et al. (2005). 7 rogoff (2003) contends that globalisation may, in fact, have had the opposite effect on firms’ pricing behaviour, arguing that the increase in competitive pressures arising from globalisation means that the cost for firms of setting the price at the wrong level is much higher than it would be with less competition. hence, globalisation would lead to firms revising their prices more frequently and… |
| Vítor Constâncio: Understanding inflation dynamics and monetary policy in a low inflation environment | Period_2 | 2015-11-10 | 0.410 | over-predicting inflation. for the euro area, the evidence from several new papers points to a relative steepening recently, following the previous flattening, (see e.g. oinonen and paloviita (2014), riggi and venditti (2015) and foroni and porqueddu (2015)). this development is especially marked in those countries which experienced deeper and longer recessions and made greater efforts to reform their product and labour markets. estimating the same specifications of phillips curves over two samples, one stopping at 2012 q1 (when we started systematically over-predicting inflation) and the other covering the full sample ending in 2014 q4, one finds evidence of an increase in the slope estimate. indeed, regime-switching estimates, accounting for parameter change due to state-dependency on various measures of the business cycle can help to explain the “excessive” disinflation since 2012. how much time variation is there in the slope of the phillips curve, and in which direction? let us take an agnostic view on the origins of the time-variation and estimate a hybrid nkpc for the euro area with time-varying parameters, similar to that in blanchard et al. (2015). over the sample period running from 1999 q1 to 2015 q2, the slope of headline inflation had a general tendency to decrease until 2011, after which it rebounded. for core inflation the upward shift started earlier. similar results have been obtained also using the structural new area wide model (nawm). in the nawm framew… |
| Vítor Constâncio: Past and future of the European Central Bank monetary policy | Period_2 | 2018-05-11 | 0.371 | right fork in the road of the post-1975 evolution, features an approach developed by kydland, prescott and sargent, and more recently by galì and gertler and others. inflation depends on forward-looking expectations, and expectations respond rationally to actual and expected changes in monetary and fiscal policy. this two-way game has no room for supply shocks or inertia”. this second approach produces what is called the new keynesian phillips curve (nkpc) that basically focuses on the output gap and expectations and is a crucial part of the dsge models, where the interest rate is all-powerful to control demand and therefore inflation. gordon and others forged ahead with the first approach, adding variables representing supply shocks (as prices depend on demand and supply factors), inertia represented by long lags of inflation and a time-varying nairu determined by a simple stochastic process. to this day, using this so-called “triangle approach”, gordon continues to obtain very good results in predicting inflation out-of-sample[30]. in 2013, gordon shows that his model “can estimate coefficients up to 1996 and then in a 16-year-long dynamic simulation, with no information on the actual values of lagged inflation, predict the 2013 value of inflation to within 0.5 percentage point. the slope of the pc relationship between inflation and the unemployment gap does not decline by half or more as in the recent literature, but instead is stable.” this last aspect is particularly … |
| Vítor Constâncio: Understanding inflation dynamics and monetary policy in a low inflation environment | Period_2 | 2015-11-10 | 0.369 | the phillips curve as a vehicle to discuss inflation dynamics the current attention to the relationship between inflation and economic slack has led to an intense debate on the stability of the phillips curve and its power to explain the twin puzzle. an important consideration is that economic slack is a multidimensional concept that is not directly observable and choices must be made on how to estimate or measure it. the outcome is highly sensitive to the assumptions used for the decomposition of economic activity into trend and cycle. usual measures of slack can vary substantially across methods and variables included, although they tend to agree on the timing of peaks and troughs. the fact that economic activity is multidimensional suggests that there might be advantages in using multivariate dynamic models to estimate it. for instance, ecb staff uses a dynamic factor model that performs a trend/cycle decomposition of real activity and core inflation. 3 the model uses a single factor to capture common cyclical fluctuations and estimates the output gap as the deviation of output from its trend. different modelling assumptions, such as different sets of real activity indicators and different specifications of the trend components of the variables, lead to different estimates of the output gap. these differences are economically very relevant, with some models estimating an output gap that was close to zero in 2014 on average, and others estimating remaining slack of as mu… |
| Vítor Constâncio: Understanding inflation dynamics and monetary policy | Period_2 | 2015-09-02 | 0.332 | further, the standard hybrid new keynesian phillips curve includes agents’ inflation expectations, which are also difficult to measure. recent work by coibion and gorodnichenko (2015) uses expectations from surveys, with some practical success. it highlights that the choice of the measure of inflation expectations is crucial in understanding inflation dynamics in the united states, advocating the use of surveys of household inflation expectations rather than those of professional forecasters. naturally, expectations from surveys or professional forecasters are not microfounded. more generally, nkpc has had many problems to predict inflation even when embedded in a dsge model. as king and watson (2012) highlight when using the labour income share or unit labour costs the models do not capture that the last 15 years do not show a co-movement of inflation with the significant decline of those ulcs. gürkaynak, kisacikoglu and rossi (2013) also illustrate the subpar performance of dsge models to forecast inflation. in their encompassing survey mavroeidis, plagborg-møller, and j. stock (2014), also conclude that without rejecting the nkpc, “we are unable to pin down the role of expectations in the inflation process sufficiently accurately for the results to be useful for policy analysis”. finally there is the question of stability of phillips curve parameters, in the form of non-linearities or structural changes. as sudden decreases in forecasting ability are frequently associat… |
| Vítor Constâncio: Understanding inflation dynamics and monetary policy | Period_2 | 2015-09-02 | 0.316 | however, as policy makers we need more than just good inflation forecasts: we also need to understand the inflation process in order to better assess the role of monetary policy. we also need to be able to explain our reasoning to the public, as the management of expectations has become such an important monetary policy instrument. this is one more reason for continuing to use the phillips curve as a tool to discuss inflation dynamics. the current attention to the relationship between inflation and economic slack has led to an intense debate on the stability of the phillips curve and its power to explain the twin puzzle. empirical research, especially in the united states, shows that the slope of the phillips curve has varied over time, with a clear tendency to flatten over the years. for the euro area, the evidence from several recent papers points to a steepening in recent years (see e.g. oinonen and paloviita (2014), riggi and venditti (2015) and foroni and porqueddu (2015)). this development is especially marked in those countries which experienced deeper and longer recessions and made greater efforts to reform their product and labour markets, with an impact on nominal rigidities (see, for italy, riggi and santoro (2015) and, to a lesser extent, for spain, banco de españa (2013 and 2015). when analysing the excessive disinflation in the euro area since 2012, natural questions arise as to whether we are facing a new regime of low inflation (e.g. due to demographics, in… |
| Isabel Schnabel: Prospects for inflation - sneezes and breezes | Period_3 | 2021-11-14 | 0.299 | flattened considerably over recent decades, a finding that continues to generate substantial debate in academia and policy circles (chart 7). chart 7 change in the slope of the phillips curve (x-axis: log percentage points, y-axis: percentage points) us data: 1980-2000 us data: 2000-2020 6 6 4 oo 4 - * we qo =< : of ao 0 ° so rs pene) dus s 0 ae so er is log = c a. o is “0 5 g ke ee soak) fe) o s b {op o 4 = 6 -4 -2 0 2 4 6 6 4 -2 0 2 4 6 wo 4 model: 1980-2000 model: 2000-2020 = 6 6 o 4 8 4 -2 c-88 “ohs9, gp o fso ; o -2 = bsi — = 4 se) i 4 -6 -6 -6 4 “2 0 2 4 6 -6 -4 -2 0 2 4 6 output gap source: costain, j., nakov, a. and petit, b. (2021), “flattening of the phillips curve with state-dependent prices and wages”, the economic journal, july. note: the top panels show scatterplots of the change in inflation and the output gap in us data over the periods 1980-2000 and 2000-2020, respectively. the bottom panels illustrate simulated data generated by a model of state-dependent price and wage setting (costain et al., 2021) for the same time periods. the analysis indicates that the model explains roughly half of the observed flattening of the phillips curve. according to the model, lower inflation in the second period (2000-2020) has reduced the frequency of price changes, implying a reduced transmission of demand shocks to the aggregate price level. such a change would have significant implications for the conduct of monetary policy. when inflation is below target, a flatter st… |
| Isabel Schnabel: Reconciling the macro and micro evidence on the effects of monetary policy | Period_3 | 2022-09-13 | 0.235 | macro evidence on the effects of policy rates one stream of the literature investigates the effects of policy rates using macro data, time-series econometric models and dynamic stochastic general equilibrium (dsge) models. a broad consensus has emerged about the aggregate effects of interest rate policy. the econometric models, mainly structural vector autoregressions, show that policy rate changes have significant effects on both inflation and economic activity. maximum effects occur with a lag, with the impact on inflation taking somewhat longer to materialise than the impact on the real economy.[1] furthermore, at least at times when inflation is not too far from the 2% target, the models indicate that the effect on inflation tends to be modest relative to the effect on the real economy. in other words, the phillips curve is rather flat.[2] dsge models can replicate the effects of interest rate changes found in the macro data, shedding some light on the transmission mechanism of monetary policy.[3] |
| Isabel Schnabel: Reconciling the macro and micro evidence on the effects of monetary policy | Period_3 | 2022-09-13 | 0.199 | digging deeper into the micro evidence since the golosov-lucas paper was published, economists have been studying numerous micro price datasets from different countries. researchers have been busy constructing models that can match various features of the micro data, including the average frequency and size of price changes. in 2018 the european system of central banks established prisma – the price-setting microdata analysis network – to collect and study various kinds of micro data, aiming to deepen our understanding of price-setting behaviour and inflation dynamics. today, peter karadi will present the findings from a research project undertaken as part of the prisma network. in the paper, peter and his co-authors raphael schoenle and jesse wursten set out to measure the selection effect in micro data. to give you a preview of their findings in a nutshell, the selection effect is absent. the probability that a given price will change increases, to a certain extent, when that price is further from the optimum. however, the probability of price adjustment conditional on an aggregate shock does not seem to depend on the distance from the optimum.[6] the authors also discuss which models can match such price-setting behaviour. the promising candidates are state-dependent models with random menu costs and models of information- constrained price-setting.[7] in both classes of models, the selection effect can be weak: some prices fail to adjust even though the distance from t… |
| Isabel Schnabel: Reconciling the macro and micro evidence on the effects of monetary policy | Period_3 | 2022-09-13 | 0.187 | early micro evidence on the effects of policy rates with the advent of more and more granular data, research shifted its focus to micro data on prices. researchers documented how individual firms set prices, aiming at building models that would be consistent with both the price-setting behaviour at the micro level and the effects of monetary policy in the macro data. meeting these two objectives simultaneously proved challenging. when economists studied the micro data underlying the consumer price index in the united states and other countries, they found that individual price changes are infrequent but typically large in absolute terms, in the order of 10%.[4] in their famous paper “menu costs and phillips curves”, mikhail golosov and robert lucas investigated the implications of this evidence for the effects of monetary policy in a dynamic equilibrium model with idiosyncratic shocks and a fixed cost of nominal price changes. when calibrated to match the average frequency and size of price changes in the micro data in a low- inflation environment, the model predicted monetary policy having a strong effect on inflation and a weak effect on output – that is a steep phillips curve, in contrast to the relationship found in the macro data.[5] the key reason behind this result was the “selection effect”. in a menu cost model, after a change in the policy rate, prices further away from their optimum adjust sooner than others. in the golosov-lucas model, this selection effect is … |
| Isabel Schnabel: Monetary policy and the Great Volatility | Period_3 | 2022-08-30 | 0.175 |
|
The most representative speeches:
The most common terms according to different indicators:
| topic | term_beta | rank_beta | beta | term_frex | rank_frex | frex |
|---|---|---|---|---|---|---|
| 2 | global | 1 | 0.1291946 | globalisation | 1 | 0.9999124 |
| 2 | globalisation | 2 | 0.0906577 | international | 2 | 0.9987727 |
| 2 | international | 3 | 0.0697889 | globalise | 3 | 0.9987723 |
| 2 | domestic | 4 | 0.0422625 | global | 4 | 0.9985084 |
| 2 | economy | 5 | 0.0276672 | imf | 5 | 0.9983348 |
| 2 | imf | 6 | 0.0176809 | domestic inflation | 6 | 0.9982902 |
| 2 | factor | 7 | 0.0156324 | global economic | 7 | 0.9978973 |
| 2 | trade | 8 | 0.0146082 | international monetary | 8 | 0.9977196 |
| 2 | affect | 9 | 0.0126877 | financial globalisation | 9 | 0.9975424 |
| 2 | change | 10 | 0.0110233 | global inflation | 10 | 0.9971054 |
| 2 | influence | 11 | 0.0106393 | international financial | 11 | 0.9969288 |
| 2 | global economic | 12 | 0.0103832 | global factor | 12 | 0.9967989 |
| 2 | good | 13 | 0.0093590 | domestic | 13 | 0.9961340 |
| 2 | globalise | 14 | 0.0092309 | spillover | 14 | 0.9960552 |
| 2 | spillover | 15 | 0.0091029 | capital flow | 15 | 0.9957894 |
We list the 5 most representative paragraphs for each period:
| title_link | period | date | gamma | paragraphs |
|---|---|---|---|---|
| Jose Manuel Gonzalez-Paramo: Globalisation, macroeconomic stability and monetary policy at a time of financial turmoil | Period_1 | 2008-09-09 | 0.261 |
|