Inside the black box: Neural network-based real-time prediction of US recessions

Feedforward neural network (FFN) and two specific types of recurrent neural network, long short-term memory (LSTM) and gated recurrent unit (GRU), are used for modeling US recessions in the period from 1967 to 2021. The estimated models are then employed to conduct real-time predictions of the Great Recession and the Covid-19 recession in US. Their predictive performances are compared to those of the traditional linear models, the logistic regression model both with and without the ridge penalty. The out-of-sample performance suggests the application of LSTM and GRU in the area of recession forecasting, especially for the long-term forecasting tasks. They outperform other types of models across 5 forecasting horizons with respect to different types of statistical performance metrics. Shapley additive explanations (SHAP) method is applied to the fitted GRUs across different forecasting horizons to gain insight into the feature importance. The evaluation of predictor importance differs between the GRU and ridge logistic regression models, as reflected in the variable order determined by SHAP values. When considering the top 5 predictors, key indicators such as the S\&P 500 index, real GDP, and private residential fixed investment consistently appear for short-term forecasts (up to 3 months). In contrast, for longer-term predictions (6 months or more), the term spread and producer price index become more prominent. These findings are supported by both local interpretable model-agnostic explanations (LIME) and marginal effects

Teollisuuden tuottajahintaindeksin ennustaminen suuriulotteisen aineiston avulla

Kansantalouden nykyistä ja tulevaa tilaa koskevan ajankohtaisen tiedon tuottaminen on tärkeää käytännön talouspolitiikan näkökulmasta: politiikkatoimien toimeenpanon ja niiden vaikutusten ilmenemisen välillä on tyypillisesti merkittäviä viiveitä, mikä luo tarpeen ennakoida kokonaistaloudellisten suureiden kehitystä. Tuottajahintaindeksi on yksi tällainen makrotaloudellinen suure: tuottajahintaindeksien avulla pyritään seuraamaan kansantaloudessa tuotettujen hyödykkeiden yleisen hintatason muutoksia tuottajien näkökulmasta, mikä tekee niistä varteenotettavan inflaatiopaineen ja suhdanneolojen indikaattorin. Tämän tutkielman pääasiallisena tavoitteena on selvittää mahdollisuuksia kotimaisen teollisuuden tuottajahintaindeksin luotettavaan ennustamiseen lyhyellä aikavälillä hyödyntäen suurta ulkoisten ennustavien muuttujien joukkoa. Ennustavien muuttujien lukumäärän kasvattaminen altistaa tavanomaiset ennustamismenetelmät epätarkkuuksille ja tekee niiden soveltamisen suoranaisen mahdottomaksi, kun muuttujien määrä ylittää mallin estimoimiseen käytettävissä olevien havaintojen lukumäärän. Ratkaisuksi tähän ongelmaan on ehdotettu lukuisia vaihtoehtoisia menetelmiä. Tutkielma tarjoaa laajan yleiskatsauksen näihin menetelmiin sekä muihin makrotaloudellisten muuttujien ennustamisen kannalta oleellisiin näkökohtiin. Koska yksikään vaihtoehtoisista menetelmistä ei ole osoittautunut käytännön sovelluksissa yksiselitteisesti muita paremmaksi, tutkielman empiiriseen osuuteen on valittu sovellettavaksi menetelmiä, jotka edustavat kahta keskenään erityyppistä lähestymistapaa suuriulotteiseen ennustamiseen: dynaamisia faktorimalleja ja regularisoituja regressioita. Dynaamisten faktorimallien vaikuttavuus perustuu oletukseen, jonka mukaan suuriulotteisen aineiston sisältämä oleellinen informaatio voidaan tiivistää huomattavasti pienempään joukkoon taustalla vaikuttavia muuttujia, faktoreita, joiden estimaatteja voidaan käyttää edelleen ennustamiseen. Regularisoitujen regressioiden tarjoama ratkaisu taas perustuu ennusteeseen liittyvän harhan ja varianssin tasapainottamiseen. Laajempaan regularisoitujen regressioiden luokkaan kuuluvista menetelmistä tutkielmassa on käytössä neljä eri muunnosta: ridge, lasso, elastinen verkko ja adaptiivinen lasso. Menetelmien empiiristä suorituskykyä arvioidaan toteuttamalla simuloitu otoksen ulkopuolinen ennustekoe, jossa kohdemuuttujalle estimoidaan historiallisen aineiston avulla sarja peräkkäisiä ennusteita verrattavaksi vastaavan ajanjakson toteutuneisiin arvoihin. Koejärjestelyn tavoitteena on tuottaa edustavaa tietoa ennustemallien tarkkuudesta jäljittelemällä tosiaikaisen ennustamisen olosuhteita: kunkin ennusteen tuottamiseksi hyödynnetään ainoastaan informaatiota, joka olisi ollut käytettävissä ennusteen laadinta-ajankohtana. Kokeessa käytettävien ennustavien muuttujien joukko koostuu eri lähteistä kerättyjen taloudellisten muuttujien kuukausittaisista aikasarjoista. Ennustekokeen perusteella suuriulotteisten mallien etu keskimääräisessä ennustetarkkuudessa yksinkertaiseen autoregressiiviseen verrokkimalliin verrattuna osoittautuu ainoastaan marginaaliseksi yhden, kahden ja kolmen kuukauden päähän tähtäävillä ennustehorisonteilla. Myöskään käytettyjen suuriulotteisten menetelmien kesken ei havaita merkittäviä eroja ennustetarkkuudessa. Suotuisampia tuloksia saavutetaan sen sijaan käyttämällä suhteellisen nopeasti saataville tulevien markkinamuuttujien havaintoja indeksin samanaikaisten arvojen ennustamiseen tulevien arvojen sijaan. Tässä tapauksessa erityisesti regularisoidut mallit esiintyvät edukseen. Tulokset antavat osviittaa, että varteenotettavimmat mahdollisuudet tuottajahintaindeksin ennakoimiseen voisivat perustua ulkoisten muuttujien julkaisuviiveeseen liittyvän edun hyödyntämiseen indeksin samanaikaisessa ennustamisessa.Producing timely information regarding the current and future state of the economy is important for the practice of economic policy: the delay between the implementation of policy measures and the emergence of their effects is typically considerable, which creates a need to anticipate developments in macroeconomic variables. The producer price index is one such variable: producer price indices are used to track changes in the general price level of goods produced within an economy from the point-of-view of producers, which makes them prominent indicators of inflationary pressures and business cycle conditions. The principal objective of this thesis is to investigate whether the Finnish Producer Price Index for Manufactured Goods could be reliably forecasted in the short run using large sets of external predictors. Increasing the number of predictors exposes standard forecasting methods to inaccuracies and makes their application outright infeasible once the number of variables exceeds the number of observations available for the estimation of the forecasting model. Various alternative methods have been proposed to counter this issue. This thesis provides a broad overview of these methods as well as other relevant issues pertaining to the forecasting macroeconomic variables. Given that no single framework has proven to dominate others in practical applications, a selection of methods has been chosen for the empirical section of this thesis. These methods represent two different approaches to high-dimensional forecasting: dynamic factor models and penalized regressions. The effectiveness of dynamic factor models is based on the assumption that relevant information contained in high-dimensional data can be summarized using only relatively few underlying factors, the estimates of which can, in turn, be used for forecasting. The solution offered by penalized regressions, on the other hand, is based on striking a balance between the bias and variance of the forecasts. Out of the broader class of penalized methods, four different variations will be utilized in this thesis: the Ridge, Lasso, Elastic Net, and Adaptive Lasso. The empirical performance of the methods will be assessed by conducting a simulated out-of-sample forecasting experiment, in which a series of consecutive forecasts are estimated for the target variable using historical data. These forecasts are, in turn, compared to their realized counterparts. The objective of the experimental arrangement is to produce representative information regarding the empirical accuracy of the respective forecasting models by emulating circumstances faced in real-time forecasting: only information that would have been available at the time is used to produce each forecast. The set of predictors used in the experiment is composed of monthly economic time series collected from a variety of sources. Based on the forecasting experiment, the benefit of the high-dimensional models in terms of average forecasting accuracy turns out to be only marginal in comparison to a univariate autoregressive benchmark at the one-, two-, and three-month horizons. Moreover, the differences among the respective high-dimensional methods are found to be insignificant. On the other hand, more favorable results are achieved by using relatively timely market-based variables to predict the concurrent rather than strictly future values of the index. In this case, the penalized models perform particularly well. The results indicate that leveraging the advantage in publication lag enjoyed by external predictors for the purpose of contemporaneous prediction, or nowcasting, could represent the most potential for predicting the producer price index

Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window

Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam's window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well that of other methods. Keywords: Bayesian model averaging; Model uncertainty; Nowcasting; Occam's window

Nowcasting economic time series: real versus financial common factors

In this paper we want to assess the impact of real and financial variables in nowcasting smoothed GDP. We implement the generalized dynamic factor model, on which Eurocoin indicator is based. We can assess that, during the structural break in 2008, the impact of real variables in estimating smoothed GDP becomes particularly relevant in relation to that concerning financial data as money supply, spreads

Comparing China’s GDP Statistics with Coincident Indicators

We use factor analysis to summarize information from various macroeconomic indicators, effectively producing coincident indicators for the Chinese economy. We compare the dynamics of the estimated factors with GDP, and compare our factors with other published indicators for the Chinese economy. The indicator data match the GDP dynamics well and discrepancies are very short. The periods of discrepancies seem to correspond to shocks affecting the growth process as neither autoregressive models for GDP itself nor various coincident indicators are able to forecast them satisfactorily.factor models; principal component; GDP; China

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.Comment: PhD thesis, 238 pages, 9 chapters, 2 appendices, 58 figures, 49 table