In this paper, we develop a bivariate unobserved components model for inflation and unemployment. The unobserved components are trend inflation and the non-accelerating inflation rate of unemployment (NAIRU). Our model also incorporates a time-varying Phillips curve and time-varying inflation persistence. What sets this paper apart from the existing literature is that we do not use unbounded random walks for the unobserved components, but rather bounded random walks. For instance, NAIRU is assumed to evolve within bounds. Our empirical work shows the importance of bounding. We find that our bounded bivariate model forecasts better than many alternatives, including a version of our model with unbounded unobserved components. Our model also yields sensible estimates of trend inflation, NAIRU, inflation persistence and the slope of the Phillips curve.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Simon Property Group Acquisition Holdings Inc. Class A Common Stock is assigned short-term Ba3 & long-term Ba3 estimated rating.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dated phylogenetic trees are important for studying mechanisms of diversification, and molecular clocks are important tools for studies of organisms lacking good fossil records. However, studies have begun to identify problems in molecular clock dates caused by uncertainty of the modeled molecular substitution process. Here we explore Bayesian relaxed-clock molecular dating while studying the biogeography of ca. 200 species from the global cicada tribe Cicadettini. Because the available fossils are few and uninformative, we calibrate our trees in part with a cytochrome oxidase I (COI) clock prior encompassing a range of literature estimates for arthropods. We show that tribe-level analyses calibrated solely with the COI clock recover extremely old dates that conflict with published estimates for two well-studied New Zealand subclades within Cicadettini. Additional subclade analyses suggest that COI relaxed-clock rates and maximum-likelihood branch lengths become inflated relative to EF-1α intron and exon rates and branch lengths as clade age increases. We present corrected estimates derived from (1) an extrapolated EF-1α exon clock derived from COI-calibrated analysis within the largest New Zealand subclade, (2) post-hoc scaling of the tribe-level chronogram using results from subclade analyses, and (3) exploitation of a geological calibration point associated with New Caledonia. We caution that considerable uncertainty is generated due to dependence of substitution estimates on both the taxon sample and the choice of model, including gamma category number and the choice of empirical versus estimated base frequencies. Our results suggest that diversification of the tribe Cicadettini commenced in the early- to mid-Cenozoic and continued with the development of open, arid habitats in Australia and worldwide. We find that Cicadettini is a rare example of a global terrestrial animal group with an Australasian origin, with all non-Australasian genera belonging to two distal clades. Within Australia, we show that Cicadettini is more widely distributed than any other cicada tribe, diverse in temperate, arid and monsoonal habitats, and nearly absent from rainforests. We comment on the taxonomic implications of our findings for thirteen cicada genera.

In einer Umfrage gaben rund 36 Prozent der befragten Kunden in der DACH-Region an, dass die derzeitige wirtschaftliche Entwicklung und Inflation einen (ziemlich) umfangreichen Einfluss auf ihre Kaufentscheidungen bei Telekommunikationsdienstleistungen habe. Die Daten stammen aus einer Studie von Simon-Kucher & Partners, hierzu wurden Telko-Kunden in Deutschland, Österreich und der Schweiz befragt.

Die Bedeutung des Preises als Gewinntreiber für Unternehmen hat in den letzten Jahren deutlich zugenommen. 41 Prozent der für eine Erhebung der Unternehmensberatung Simon-Kucher befragten Entscheider:innen in Unternehmen aus 13 europäischen Ländern nannten den Preis als wichtigsten Faktor für das künftige Wachstum des Unternehmensgewinns, 40 Prozent nannten die Verkaufsmenge. Noch zwei Jahre zuvor wurden die Verkäufe von 50 Prozent der Unternehmen als wichtigster Wachstumsfaktor genannt (27 Prozent nannten den Preis). Laut Simon-Kucher steht dies im Einklang mit der gesamtwirtschaftlichen Situation, die durch Kostenüberwälzung und steigende Inflation gekennzeichnet ist.

Weltweit tätigen 92 Prozent der Konsumenten regelmäßig Einkäufe während Winter- und Sommer-Sales (online und/oder stationär), wie die Big Promo Days Study von Simon-Kucher & Partners ergeben hat. Grundsätzlich erfreuen sich Schnäppchenaktionen in Zeiten von Inflation und Kaufzurückhaltung großer Beliebtheit, da hier notwendige Anschaffungen für einen guten Preis getätigt werden können.

Die ************** sowie die ************ mit hohen Energiepreisen waren im Jahr 2023 die größte Hemmnisse für das Wachstum von Unternehmen. Dies geht aus einer Befragung der Unternehmensberatung Simon-Kucher hervor, bei der im Sommer 2023 mehr als 1.300 Entscheider:innen in Unternehmen aus 13 europäischen Ländern befragt wurden. Als weitere Hemmnisse für das Unternehmens-Wachstum wurden u.a. politische Instabilitäten, wie etwa der Krieg in der Ukraine oder der Handelsstreit zwischen den USA und China, sowie der Fachkräftemangel genannt.