Graph and download economic data for Total Credit to Private Non-Financial Sector, Adjusted for Breaks, for United States (QUSPAM770A) from Q4 1947 to Q4 2024 about adjusted, credits, nonfinancial, sector, private, and USA.

Loans to Private Sector in China decreased to 835104.95 CNY Hundred Million in July from 839997.99 CNY Hundred Million in June of 2025. This dataset provides - China Loans To Private Sector - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The usage of online marketplace in Indonesia increases due to Covid-19 pandemic and its supporting environment such as payment systems. This investigation was conducted to determine the effect of Website Quality on Online Impulsive Buying Behavior moderated by Sales Promotion and Credit Card Usage in Indonesian marketplace. This study uses quantitative methods with causal analysis. In this research, data was collected through online questionnaires and 275 respondents who used the marketplace website responded. This research uses PLS-SEM data analysis technique. The results of this study showed that three out of five hypotheses are accepted. This study shows that Website Quality, Credit Card Use, and Sales Promotion have positive significant effect on Online Impulse Buying Behavior. However, the result of this study also revealed interesting findings, that there is not enough evidence to support moderation effect of Credit Card use and Sales Promotion in the relationship between web quality and Online Impulse Buying Behavior.

This statistic shows the payment methods used to make impulse purchases in the United States as of November 2014. During the survey, 30 percent of the respondents said they had used a credit card to make an impulse purchase.

To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for inference. Applying this method, we investigate the effectiveness of U.S. macroprudential actions on bank credit growth from 1948 to 2019. Empirically, we find that the effects of macroprudential policy on credit growth are asymmetric and depend on the quantiles of credit growth. The tightening of macroprudential actions fails to rein in high credit growth, whereas easing policies do not effectively stimulate bank credit growth during low-growth periods. These findings suggest that U.S. macroprudential policies might not sufficiently address the challenges of soaring bank credit or ensure overarching financial stability.

Impulse responses to different shocks for the emerging countries.

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Background of the survey:

The middle of the 19th century is the time of the emerging economies and of the beginning of the world trade. The financial power of individual bankers was not able anymore to sustain the international economy. Therefore individual bankers formed banks as cooperation. In those times also the Berliner Handels-Gesellschaft was founded as a partnership limited by shares. In its 100 years of existence the Berliner Handels-Gesellschaft survived times of economic crisis and the Second World War. The situation after the Second World War meant new challenges for the unit branch bank. The history of the Berliner bank is uncommon, because it survived the turbulences of history and still exists with its old name and old legal form.

The banking system of a country is the result of a historical development process in which politics, economy and banks participate. While the English banking system developed in the context of the goldsmiths in London that issued receipts for the storage of precious metals, on the European continent mainly individual bankers had influence in the time of absolute principalities. Through the liberal revolution in 1948/49 the close relationship between the state and individual bankers was leaven. Simultaneously with those political changes the industrial revolution started in Europe. Finance institutions for the needs of the new industry were necessary and could only now, after the liberation of state paternalism, develop. The individual company must be understood in the context of the times and its environment. Therefor the history of the Berliner Handels-Gesellschaft also needs to be understood in the context of the respective times.

In the year of the foundation of the Berliner Handels-Gesellschaft, 1856, Germany was in an upheaval of enormous extent, shaped by the attempt of a national unity emerged from the Napoleonic Wars. Simultaneously, the European continent was influenced by the political and economic liberalism coming from England.

Due to the economic liberalization of England mobile capital was available there, initially provided by the overseas trade and the large estates available. On the European continent there was no comparable banking or credit system. The individual banks reliant on their equity capital could at most rely on the largest of them. But when the most important individual banks of the continent suffered heavy losses, the development of a new type of bank was necessary. But in the beginning the Prussian state was hostile towards the idea of capital companies, so that the actual impulse came from France.

Pereires together with Fould created three bank types in 1851, which had a major impact for the future of the European continent: the business bank, the mortgage bank and mutual banks. The economic boom of the 50s showed very soon that for the resulting financing tasks especially the type of commercial bank could come into question because this type alone was in a position to raise the necessary capital.

Because of those circumstances and because of the foresight of the individual bankers of those times, the idea to found a joint Bank together with different individual bankers, that could still act independently, emerged. In this context the Berliner Handels-Gesellschaft was founded with the explicit task to realize the ventures that emerged from the German industrialization.

Time and place of the survey:

The investigation period and the object of study relate to the development history of the Berliner Handels-Gesellschaft between 1856 and 1956.

Specification of the research question:

The development of the Berliner Handels-Gesellschaft and therein mutually reflecting back the history of the German economy and industrialization from 1856 to 1955 will be shown on the basis of annual reports from that time.

Data tables in HISTAT: 1. Annual reports of the Berliner Handels-Gesellschaft 1856 – 1955.

This repository contains supplementary material for the paper titled `Application of Machine Learning for the Spatial
Analysis of Binaural Room Impulse Responses' Available at: dx.doi.org/10.3390/app8010105 . These programs and audio files are distributed in the hopes that they will prove useful under the Creative Commons Attribution 4.0, with no warranty; or the implied warranty of merchantability or fitness for a particular problem. Please give appropriate credit for use of the material provided in this repository back to the author.

In order to use the MatLab code the Auditory Toolbox by Malcolm Slaney [1] and the Cochleagram function distributed by Bin Gao [2] are required.

The python scrips require the following Python libraries to be installed: Numpy[3], SciPy[4] and Tensorflow [5].

The MatLab code was tested using MatLab R2017a on a Computer running windows 7.

The python code was tested using Python 3.2.5, using an anaconda Python environment - in windows command line.

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The repository contains:

Folders:

1.) neg90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the -90° rotation neural network.

2.) pos90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the +90° rotation neural network.

3.) testData - this folder contains pre-generated test data for the different binaural dummy head microphones, speaker, and signal type combinations.

Python Scripts:

1.) AnalyseDoA.py - A python script that can be run to test the neural network using the pre-generated test data - running the script will allow the user to input the binaural dummy head, speaker, and signal type. The important variables generated by this script are DoA - the direction of arrival for each signal in the feature vector, and yDiff - the difference between the predicted DoA and the expected direction of arrival

2.) DirectionAnalysis.py - This python file contains a set of function that are used to define the neural network, and run it. The function called DoAPrediction takes the feature vector generated by the MatLab code as its input argument, these features will then be passed to the neural network, and the output of this function is the direction of arrival predicted by the neural network for each signal. The functions: DoAAnalysis_neg90 and DoAAnalysis_pos90 are called by the DoAPrediction function, these functions create the neural network using the NN function, import the weights and biases, and passes the feature matrix (provided as input) through the neural network - the output of these functions are the predicted direction of arrival.

MatLab files:

1.) runAnalysis.m - This MatLab script analyses the dataset provided as part of this repository. Users can change the variables head ('KEMAR' or 'KU100'), signalType ('directSound' or 'reflection'), and speaker ('EquatorD5' or 'Genelec8030'). This script will produce the gaussian normalised feature vector and expected direction of arrival for all signals with the defined head, signal type, and speaker combination. These variables are then saved in .mat files so they can be imported by the python scripts.

2.) BinauralModelCochlea.m - This MatLab function analyses a given binaural signal and outputs the interaural cross-correlation, interaural level difference, interaural time difference, the cochlea output for the left and right channel and the centre frequencies of the gammatone filter band. The input variables are: IR - the signal to be analysed, N - the number of gammatone filters, freqLow - the lowest centre frequency of the gammatone filter bank (centre frequency of the first gammatone filter), and freqHigh - the highest centre frequency of the gammatone filter bank (the centre frequency of the Nth gammatone filter). This function requires Malcolm Slaney's Auditory Toolbox [1] and Bin Gao's Cochleagram function [2] in order to work.

3.) generateFeatureVector.m - This MatLab function generates a feature vector from an input binaural signal x, and a version of the signal captured after the binaural dummy head has been rotated by either +90° or -90° degree (variables xPos90 and xNeg90 respectively). If the sampling frequency (Fs) isn't 44100, the signals are resampled to be at 44100. This file also contains a function 'gaussianNormalisationTestData' which gaussian normalises the data using the mean and standard deviation calculated from the data used to train the neural networks - the mean and standard deviation values are stored in the folder GMParams in the pos90 and neg90 folders.

4.) generateTestData.m - This MatLab function analyses the included binaural dataset, it takes the input variables: head - the binaural dummy head used for the measurements either 'KEMAR' or 'KU100', speaker - the speaker used for the measurements either 'EquatorD5' or 'Genelec8030', and signalType - the type of signal being analysed either 'directSound' or 'reflection'.

Text files:

1.) noLayers.txt - a text file containing the number of layers used when training the neural network - with the current version of the code the neural network contains only 1 layer.

2.) README.txt - Read me file containing information about the repository.

Audio files:

This repository contains 1152 binaural signals half of which are direct sounds segmented from a binaural room impulse responses and the other half are reflections segmented from binaural room impulse responses (detailed in the paper this material supports) the direct sounds are recorded at angles from 0° to 357.5° in steps of 2.5° and the reflections are recorded at angles of 1° to 358.5° in steps of 2.5°. In the paper only recordings relating to signals recorded with the Equator D5 are analysed.

The combination of audio files include:

1.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker
2.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker
3.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker
4.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker
5.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker
6.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker
7.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker
8.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker

The files are stored using the following file naming convention:
head_Test3_speaker_signalType_000_0_Degrees.wav - where _000_0 defines the azimuth direction of arrival so for example for a direct sound measured with the KEMAR unit and the Genelec8030 at 5 degrees would be 'KEMAR_Test3_Genelec8030_directSound_005_0Degrees.wav' and for a reflection measured with the KU100 and the Equator D5 at 298.5 degrees would be 'KU100_Test3_EquatorD5_reflection_298_5Degrees.wav'

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Bibliography:
[1] Slaney, M. (1998). Auditory Toolbox. Palo Alto, CA. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/ [Accessed: Oct. 27, 2017]

[2] Gao, B. (2014). Cochleagram and IS-NMF2D for Blind Source Separation. [Online] Available: http://uk.mathworks.com/matlabcentral/fileexchange/48622-cochleagram-and-is-nmf2d-for-blind-source-separation?focused=3855900&tab=function [Accessed: Oct. 27, 2017]

[3] NumFocus. (n.d.). NumPy. [Online]. Available: http://www.numpy.org/ [Accessed: Oct. 27, 2017]

[4] SciPy. (n.d.). SciPy. [Online]. Available: https://www.scipy.org/ [Accessed: Oct. 27, 2017]

[5] Google. (n.d.). TensorFlow. [Online] Available: https://www.tensorflow.org/ [Accessed: Oct. 27, 2017]

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All code and audio produced by: Michael Lovedee-Turner, PhD candidate in Music Technology at the Audio Lab, Department of Electronic Engineering, University of York

Contact: mjlt500@york.ac.uk

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Snapshot img

Buy Now Pay Later Market Size 2025-2029

The buy now pay later market size is forecast to increase by USD 90.29 billion, at a CAGR of 37.7% between 2024 and 2029.

The Buy Now Pay Later (BNPL) market is experiencing significant growth, driven by the increasing adoption of online payment methods and the affordability and convenience these services offer. Consumers are increasingly drawn to BNPL solutions as they enable impulse purchases without the immediate financial burden, fostering a shift from traditional credit cards and cash transactions. This trend is particularly prominent among younger demographics, who are more likely to shop online and value flexibility in payment options. However, the BNPL market faces challenges that require careful navigation.
Additionally, the lack of standardization across providers and platforms may create confusion for consumers, necessitating clear communication and transparency from companies. Addressing these challenges will be crucial for BNPL providers seeking to build trust and establish long-term relationships with customers. Payment processing and fraud prevention are essential components, ensuring secure transactions through system architecture, data encryption, and risk assessment models. Companies that successfully navigate these obstacles will be well-positioned to capitalize on the market's potential and meet the evolving needs of consumers in the digital economy. Regulatory scrutiny is intensifying, with concerns around consumer protection and potential risks associated with excessive borrowing and debt accumulation.

What will be the Size of the Buy Now Pay Later Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market dynamics shaping its applications across various sectors. Point-of-sale financing and deferred payment plans are increasingly popular, integrating seamlessly with software development and e-commerce platforms. Credit utilization and user experience (UX) are crucial factors, with business intelligence and predictive modeling optimizing conversion rates. KYC/AML compliance and customer onboarding streamline operations, while financial education and debt management tools foster customer loyalty. Currency exchange, international payments, and late fees are common considerations, with interest rates and repayment schedules influencing consumer behavior.

Fraud detection systems and technical support address potential risks, while loan origination and targeted advertising leverage data analytics and consumer segmentation. API integration, merchant services, and performance monitoring enable efficient operations, with promotional offers and debt collection tools enhancing customer engagement. Cross-border transactions and retail partnerships expand market reach, while marketing automation and spending habits analysis inform strategic decision-making. The financial technology (fintech) landscape is characterized by continuous innovation, with ongoing activities unfolding in areas such as churn rate reduction, risk management, and transaction fees optimization. System architecture, dispute resolution, and loan origination remain key focus areas, ensuring a robust and adaptive market response.

How is this Buy Now Pay Later Industry segmented?

The buy now pay later industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Business Segment

  Large enterprise
  Small and medium enterprise


Channel

  Online
  POS


End-user

  Retail and e-commerce
  Fashion and garment
  Consumer electronics
  Healthcare
  Travel and tourism


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Business Segment Insights

The Large enterprise segment is estimated to witness significant growth during the forecast period. The Buy Now Pay Later (BNPL) market experienced significant growth in 2024, with large enterprises leading the adoption of this payment solution. BNPL solutions, which include point-of-sale financing and deferred payment plans, have become increasingly popular among large businesses due to their ability to enhance customer experience and boost sales. By offering installment payment options, BNPL enables consumers to make high-value purchases more affordably and manage their spending more effectively. Credit scoring algorithms and predictive modeling are integral components of BNPL, ensuring a streamlined customer onboarding process and effective risk assessm

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT .

Abstract:

Quantitative analysis of the growth cycles of the German economy in the take-off stage of industrialization based on indicators in the form of longer time series.

Topics: a) Combination of macro- and differentiated trade analysis, ascertainment of typical time-lags, estimation of investment functions, turning points of reference cycles, average cycle length, amplitude of quantity series and price series in the most important sectors (areas of business), impulse propagation through the network of sector cycles, real basis of the reference cycles, impulse initiator or cycle-leader, typical time-lags of impulse propagation, investment function in textile industry and railway construction;

b) time series for population, bankruptcies, financial and credit institutions, stock exchanges, exports, imports, passenger and merchandise traffic, agriculture, textile industry, food and fine food industry, mining, iron and steel industry, mechanical engineering, capital stock, investments, wages, prices.

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