Reading between the Lines: Using Media to Improve German Inflation Forecasts

Beckers, Benjamin; Kholodilin, Konstantin A.; Ulbricht, Dirk

doi:10.2139/ssrn.2970466

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…Generality and specificity of inflation: they made a detailed discussion on the causes of inflation, the relationship between economic system and inflation, and governance and achieved certain research results. However, most of these literatures describe it from a qualitative perspective, and few systematically use quantitative analysis [19]. Nyonit used the method of combining econometrics and input-output analysis, extended linear expenditure system to analyze the total amount and structure of inflation, and established a quantitative analysis model of inflation [20].…”

Section: Related Workmentioning

confidence: 99%

Construction of Inflation Forecasting Model Based on Ensemble Empirical Mode Decomposition and Bayesian Model

Xia¹,

Huang

2022

Journal of Sensors

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The high explanatory power of the first-order lag term of inflation in the inflation explanatory factor is that, on the one hand, the calculation of annual inflation indicators makes the inflation values of adjacent months cover the high correlation caused by the common price increase, and on the other hand, it also shows that people’s perception of inflation is high. Some adaptability is expected. Although there are many Bayesian models available, due to the limitation of high-dimensional characteristics of the economy, most of the current inflation forecasting researches focus on a variety of generalized naive Bayesian models. By summarizing and analyzing the structural characteristics, learning methods, and classification principles of different Bayesian models, this paper finds out the important factors that affect the performance of the models and provides a theoretical basis for further improving the performance of Bayesian inflation forecasting. In this paper, the empirical mode decomposition method is introduced into inflation forecasting, and EEMD has obvious advantages in dealing with nonstationary and nonlinear time series and can decompose the signal according to the time scale characteristics of the data itself. Decompose the original time series step by step to generate eigenmode functions with different time scales. It is divided into high-frequency sequence and low-frequency sequence. Use rolling method and iterative method to construct subsamples for the sample data in this sample interval, forecast the inflation rate of each subsample interval in the next 12 months, and then compare the predicted value with the actual value to obtain a certain constructive conclusion. The predicted value is relatively close to the real value, which has theoretical and practical significance, and the predicted results obtained have no obvious regularity, but the root mean square error can be kept within 60%. By comparing the predicted value and the actual value, it can be seen that the prediction effect of the EEMD model is better.

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Section: Related Workmentioning

confidence: 99%

Construction of Inflation Forecasting Model Based on Ensemble Empirical Mode Decomposition and Bayesian Model

Xia¹,

Huang

2022

Journal of Sensors

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“…Consider two basic approaches: either use the sentiment indicators as exogenous features for forecasting models, and test their relevance in forecasting price movements, returns of price, or other statistics of the price, or use them as external advisors for ranking the subjects (targets) of the news-which in our case are stocks-and create a portfolio. A few selected examples from the vast amount of published research on the subject of forecasting and portfolio management with sentiment data are [3,4,6,21,29,44,45,49]. For a more extensive treatment of the building blocks for producing models based on textual data, see [1] and the tutorial for the sentometrics package in [2].…”

Section: Mechanics Of Textual Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis of Financial News: Mechanics and Statistics

Arratia

Ávalos

Cabaña

et al. 2021

Data Science for Economics and Finance

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This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general.

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“…Similar to news media, the change of sentiment in popular music can potentially help document social changes. With respect to news media, Beckers et al (2017) examined changing expectations for consumer price inflation in published news articles, and Cook et al (2020) studied how references to drinking alcohol during pregnancy within newspapers have evolved over time. We are unaware of earlier literature discussing the association between popular music and young people's attitude towards cars, but other aspects of cultural changes captured in popular music have been examined.…”

Section: Cultural Evolution In Popular Musicmentioning

confidence: 99%

Sentiment analysis of popular-music references to automobiles, 1950s to 2010s

Vine

Bengel

et al. 2021

Transportation

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In recent years, there has been a scholarly debate regarding the decrease in automobile-related mobility indicators (car ownership, driving license holding, VMT, etc.). Broadly speaking, two theories have been put forward to explain this trend: (1) economic factors whose impacts are well-understood in principle, but whose occurrence among young adults as a demographic sub-group had been overlooked, and (2) less well-understood shifts in cultural mores, values and sentiment towards the automobile. This second theory is devilishly difficult to study, due primarily to limitations in standard data resources such as the National Household Travel Survey and international peer datasets. In this study we first compiled a database of lyrics to popular music songs from 1956 to 2015 (defined by inclusion in the annual “top 40”), and subsequently identified references to automobiles within this corpus. We then evaluated whether there is support for theory #2 above within popular music, by looking at changes from the 1950s to the 2010s. We demonstrate that the frequency of references to automobility tended for many years to increase over time, however there has more recently been a decline after the late 2000s (decade). In terms of the sentiment of popular music lyrics that reference automobiles, our results are mixed as to whether the references are becoming increasingly positive or negative (machine analysis suggests increasing negativity, while human analysis did not find a significant association), however a consistent observation is that sentiment of automobile references have over time become more positive relative to sentiment of song lyrics overall. We also show that sentiment towards automobile references differs systematically by genre, e.g. automobile references within ‘Rock’ lyrics are in general more negative than similar references to cars in other music genres). The data generated on this project have been archived and made available open access for use by future researchers; details are in the full paper.

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Reading between the Lines: Using Media to Improve German Inflation Forecasts

Cited by 3 publications

References 38 publications

Construction of Inflation Forecasting Model Based on Ensemble Empirical Mode Decomposition and Bayesian Model

Construction of Inflation Forecasting Model Based on Ensemble Empirical Mode Decomposition and Bayesian Model

Sentiment Analysis of Financial News: Mechanics and Statistics

Sentiment analysis of popular-music references to automobiles, 1950s to 2010s

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