This dataset provides values for GOVERNMENT DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for HOUSEHOLDS DEBT TO INCOME reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for PRIVATE DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GOVERNMENT DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Argentina. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 266,167,259,703.10 Argentine Pesos as of 12/31/2023, the lowest value since 12/31/2018. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.2747 percent compared to the value the year prior.The 1 year change in percent is -0.2747.The 3 year change in percent is -3.38.The 5 year change in percent is -10.30.The 10 year change in percent is 77.18.The Serie's long term average value is 110,659,713,518.15 Argentine Pesos. It's latest available value, on 12/31/2023, is 140.53 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +4,416.52%.The Serie's change in percent from it's maximum value, on 12/31/2019, to it's latest available value, on 12/31/2023, is -11.09%.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Public Debt and Low Interest Rates, PIIE Working Paper 19-4. If you use the data, please cite as: Blanchard, Olivier. (2019). Public Debt and Low Interest Rates. PIIE Working Paper 19-4. Peterson Institute for International Economics.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Samoa. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 432,494,880.40 Samoan Tālās as of 12/31/2023, the lowest value since 12/31/2013. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -8.12 percent compared to the value the year prior.The 1 year change in percent is -8.12.The 3 year change in percent is -9.38.The 5 year change in percent is -3.14.The 10 year change in percent is -7.56.The Serie's long term average value is 189,568,922.14 Samoan Tālās. It's latest available value, on 12/31/2023, is 128.15 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1971, to it's latest available value, on 12/31/2023, is +16,470.69%.The Serie's change in percent from it's maximum value, on 12/31/2021, to it's latest available value, on 12/31/2023, is -14.46%.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Haiti. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 2,637,609,831.70 Haitian Gourdes as of 12/31/2023, the highest value at least since 12/31/1971, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.97 percent compared to the value the year prior.The 1 year change in percent is 2.97.The 3 year change in percent is 13.77.The 5 year change in percent is 18.82.The 10 year change in percent is 68.07.The Serie's long term average value is 1,101,348,455.47 Haitian Gourdes. It's latest available value, on 12/31/2023, is 139.49 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1971, to it's latest available value, on 12/31/2023, is +5,171.32%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

Technical Debt (TD) prediction is crucial to preventing software quality degradation and maintenance cost increase. Recent Machine Learning (ML) approaches have shown promising results in TD prediction, but the imbalanced TD datasets can have a negative impact on ML model performance. Although previous TD studies have investigated various oversampling techniques that generates minority class instances to mitigate the imbalance, potentials of undersampling techniques have not yet been thoroughly explored due to the concerns about information loss. To address this gap, we investigate the impact of undersampling on ML model performance for TD prediction by utilizing 17,797 classes from 25 Java open-source projects. We compare the performance of ML models with different undersampling techniques and evaluate the impact of combining them with widely used oversampling techniques in TD studies. Our findings reveal that (i) undersampling can significantly improve ML model performance compared to oversampling and no resampling; (ii) the combined application of undersampling and oversampling techniques leads to a synergy of further performance improvement compared to applying each technique exclusively. Based on these results, we recommend practitioners to explore various undersampling techniques and their combinations with oversampling techniques for more effective TD prediction.This package is for the replication of 'Less is More: an Empirical Study of Undersampling Techniques for Technical Debt Prediction'File list:X.csv, Y.csv: - These are the datasets for the study, used in the ipynb file below.under_over_sampling_scripts.ipynb: - These scripts can obtain all the experimental results from the study. - They can be run through Jupyter Notebook or Google Colab. - The required packages are listed at the top in the file, so installation via pip or conda is necessary before running.Results_for_all_tables.csv: This is a csv file that summarizes all the results obtained from the study.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Eritrea. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 725,941,139.10 Eritrean Nakfas as of 12/31/2023, the lowest value since 12/31/2004. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.1604 percent compared to the value the year prior.The 1 year change in percent is -0.1604.The 3 year change in percent is -9.10.The 5 year change in percent is -8.97.The 10 year change in percent is -23.00.The Serie's long term average value is 662,731,768.01 Eritrean Nakfas. It's latest available value, on 12/31/2023, is 9.54 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1994, to it's latest available value, on 12/31/2023, is +2,398.35%.The Serie's change in percent from it's maximum value, on 12/31/2011, to it's latest available value, on 12/31/2023, is -30.93%.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Tanzania. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 34,597,954,343.30 Tanzanian Shillings as of 12/31/2023, the highest value at least since 12/31/1971, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 13.89 percent compared to the value the year prior.The 1 year change in percent is 13.89.The 3 year change in percent is 35.33.The 5 year change in percent is 54.65.The 10 year change in percent is 147.16.The Serie's long term average value is 9,469,630,190.17 Tanzanian Shillings. It's latest available value, on 12/31/2023, is 265.36 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +17,430.44%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

📖 Dataset Overview

This dataset is designed for financial risk assessment and loan default prediction using machine learning techniques. It includes 300 records, each representing an individual with financial attributes that influence the likelihood of loan default.

📊 Features & Data Structure

The dataset contains the following columns:

• Highly Left-Skewed Column: Retirement Age – Most people retire at older ages, with fewer early retirees.
• Highly Right-Skewed Column: Debt Amount – Most people have low debt, but a few have very high debt.
• Totally Symmetric Column: Monthly Savings – Normally distributed around an average.

📌 Data Generation & Logic

The dataset was synthetically created using statistical distributions that mimic real-world financial behavior:

🔹 Retirement Age (Left-Skewed): Generated using a transformed normal distribution to ensure most values are high (60-85).
🔹 Debt Amount (Right-Skewed): Generated using a log-normal distribution, where most people have low debt, but a few have very high debt.
🔹 Monthly Savings (Symmetric): Normally distributed with mean $2000$ and standard deviation $500$, clipped between $500-$5000.
🔹 Loan Default Risk (Target Variable): Computed using a logistic function, where:
- Lower retirement age ⬆ default risk
- Higher debt ⬆ default risk
- Higher savings ⬇ default risk
- The probability threshold was adjusted to balance 0s and 1s.

Government Debt in the United States increased to 36916987 USD Million in July from 36211469 USD Million in June of 2025. This dataset provides - United States Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The German Internet Panel (GIP) is an infrastructure project. The GIP serves to collect data about individual attitudes and preferences which are relevant for political and economic decision-making processes. Experimental variations were used in the instruments. The questionnaire contains numerous randomisations as well as a cross-questionnaire experiment. Topics: Party preference (Sunday question); assessment of the importance of selected policy fields for the federal government (labour market, foreign policy, education and research, citizen participation, energy supply, food and agriculture, European unification, family, health care system, gender equality, internal security, personal rights, pension system, national debt, tax system, environment and climate protection, consumer protection, transport, defence, currency, economy, immigration and integration); currently most important policy areas for the respondent; satisfaction with the performance of the federal government (scalometer); satisfaction with the performance of the parties CDU/CSU, SPD, Bündnis 90/Die Grünen, Die Linke (scalometer); probability of an external event: Effects of the Ukraine crisis on the availability and price of Russian gas in Germany; Federal government should draw consequences from the Ukraine crisis and find alternatives to the purchase of Russian gas; assessment of political decisions of the Federal government on the introduction of a rent brake and a car toll, on the expansion of the digital infrastructure as well as on the re-regulation of prostitution; respective responsibility for the fact that corresponding laws have not yet been passed; expected change in unemployment due to the introduction of the minimum wage respectively in Eastern Germany, Western Germany and in Germany as a whole; opinion on the introduction of a statutory minimum wage; assessment of an alternative proposal to the minimum wage (state pays the difference between the real hourly wage and a gross wage of 8.50 euros); opinion on lowering the minimum wage in regions with high unemployment instead of the same minimum wage throughout Germany; self-assessment of patience and willingness to take risks (scalometer); preferred date for the debt brake (from 2015, from 2020, from 2025, after 2030 or not at all); assessment of the debt brake; assessment of the probability that one´s own federal state will manage without new debt from 2020; one´s own federal state should comply with the debt brake if not all 16 federal states manage without new debt from 2020; net household income resp. personal income; own willingness to pay an additional tax amount so that the own federal state can get along without new debts from 2020 onwards and the amount of this contribution (answer scale depending on household income and personal income); debts of cities and municipalities: Willingness to pay additional fees so that the municipality of residence can manage without new debts and the amount of this contribution (classified); willingness to agree to the merger of one´s own federal state with a neighbouring federal state; opinion on self-determination of the tax level by the federal states; opinion on the financing of infrastructure costs in poor regions via a common EU budget; opinion on EU loans within the framework of the euro bailout fund for heavily indebted member states; opinion on the fiscal equalisation system between the federal states; whether one´s own federal state belongs to the donor states or the recipient states; opinion on a law on the formation of reserves by the federal states for the pensions of state civil servants; demand for state measures to reduce income disparities; acceptance of tax evasion; inflation in Germany: Assessment of the price development for food and clothing in general and measured against the expectations of the European Central Bank (ECB) (inflation expectations); expected annual inflation rate in five and in ten years (medium-term and long-term inflation); assessment of the European Central Bank with regard to price stability in the Eurozone; preferred combination of the amount of monthly expenditure and the amount of a loan repayment; reception frequency of news in general and of news on the topic of economy. Demography: sex; citizenship; year of birth (categorised); highest school-leaving qualification; highest professional qualification; marital status; household size; employment status; private internet use; federal state. Additionally coded were: Interview date; year of recruitment; questionnaire evaluation; overall interview assessment; unique ID identifier, household identifier and person identifier within household. Das German Internet Panel (GIP) ist ein Infrastrukturprojekt. Das GIP dient der Erhebung von Daten über individuelle Einstellungen und Präferenzen, die für politische und ökonomische Entscheidungsprozesse relevant sind. Es wurden experimentelle Variationen in den Instrumenten eingesetzt. Der Fragebogen enthält zahlreiche Randomisierungen sowie ein fragebogenübergreifendes Experiment. Themen: Parteipräferenz (Sonntagsfrage); Einschätzung der Wichtigkeit ausgewählter Politikfelder für die Bundesregierung (Arbeitsmarkt, Außenpolitik, Bildung und Forschung, Bürgerbeteiligung, Energieversorgung, Ernährung und Landwirtschaft, Europäische Einigung, Familie, Gesundheitssystem, Gleichstellung von Frauen und Männern, Innere Sicherheit, Persönlichkeitsrechte, Rentensystem, Staatsverschuldung, Steuersystem, Umwelt und Klimaschutz, Verbraucherschutz, Verkehr, Verteidigung, Währung, Wirtschaft, Zuwanderung und Integration); derzeit wichtigste Politikfelder für den Befragten; Zufriedenheit mit den Leistungen der Bundesregierung (Skalometer); Zufriedenheit mit den Leistungen der Parteien CDU/CSU, SPD, Bündnis 90/Die Grünen, Die Linke (Skalometer); Wahrscheinlichkeit eines von außen wirkenden Ereignisses: Auswirkungen der Ukraine-Krise auf die Verfügbarkeit und den Preis von russischem Gas in Deutschland; Bundesregierung sollte Konsequenzen aus der Ukraine-Krise ziehen und Alternativen zum Bezug von russischem Gas finden; Beurteilung von politischen Entscheidungen der Bundesregierung zur Einführung einer Mietpreisbremse und einer Pkw-Maut, zum Ausbau der digitalen Infrastruktur sowie zur Neuregulierung von Prostitution; jeweilige Verantwortlichkeit für die bisher nicht erfolgte Verabschiedung entsprechender Gesetze; erwartete Veränderung der Arbeitslosigkeit durch die Einführung des Mindestlohns jeweils in Ostdeutschland, Westdeutschland und in Deutschland insgesamt; Meinung zur Einführung eines gesetzlichen Mindestlohns; Bewertung eines Alternativvorschlags zum Mindestlohn (Staat zahlt Differenz zwischen dem realen Stundenlohn und einem Bruttolohn von 8,50 Euro); Meinung zur Senkung des Mindestlohns in Regionen mit hoher Arbeitslosigkeit statt gleicher Mindestlohn in ganz Deutschland; Selbsteinschätzung der Geduld und der Risikobereitschaft (Skalometer); präferierter Zeitpunkt für die Schuldenbremse (ab 2015, ab 2020, ab 2025, nach 2030 oder überhaupt nicht); Bewertung der Schuldenbremse; Einschätzung der Wahrscheinlichkeit, dass das eigene Bundesland ab 2020 ohne neue Schulden auskommt; eigenes Bundesland sollte Schuldenbremse einhalten, falls nicht alle 16 Bundesländer ab 2020 ohne neue Schulden auskommen; Haushaltsnettoeinkommen bzw. persönliches Einkommen; eigene Bereitschaft zur Zahlung eines zusätzlichen Steuerbetrages, damit das eigene Bundesland ab 2020 ohne neue Schulden auskommt und Höhe dieses Beitrags (Antwortskala abhängig vom Haushaltseinkommen und dem persönlichen Einkommen); Schulden von Städten und Gemeinden: Bereitschaft zur Zahlung zusätzlicher Gebühren, damit die Wohngemeinde ohne neue Schulden auskommt und Höhe diese Betrages (klassiert); Bereitschaft, dem Zusammenschluss des eigenen Bundeslandes mit einem benachbarten Bundesland zuzustimmen; Meinung zur Selbstbestimmung der Steuerhöhe durch die Bundesländer; Meinung zur Finanzierung der Infrastrukturkosten in armen Regionen über einen gemeinsamen EU-Haushalt; Meinung zu EU-Krediten im Rahmen des Euro-Rettungsschirms für stark verschuldete Mitgliedsstaaten; Meinung zum Länderfinanzausgleich; Zugehörigkeit des eigenen Bundeslandes zu den Geberländern oder Nehmerländern; Meinung zu einem Gesetz zur Bildung von Rücklagen durch die Bundesländer für die Pensionen von Landesbeamten; Forderung nach staatlichen Maßnahmen zur Verringerung von Einkommensunterschieden; Akzeptanz von Steuerhinterziehung; Inflation in Deutschland: Einschätzung der Preisentwicklung für Lebensmittel und Kleidung allgemein und gemessen an den Erwartungen der Europäischen Zentralbank (EZB) (Inflationserwartung); erwarte jährliche Inflationsrate in fünf und in zehn Jahren (mittelfristige und langfristige Inflation); Beurteilung der Europäischen Zentralbank im Hinblick auf die Preisstabilität in der Eurozone; präferierte Kombination der Höhe von monatlichen Ausgaben und der Höhe einer Kreditrückzahlung; Rezeptionshäufigkeit von Nachrichten allgemein und von Nachrichten zum Thema Wirtschaft. Demographie: Geschlecht; Staatsbürgerschaft; Geburtsjahr (kategorisiert); höchster Schulabschluss; höchste berufliche Qualifikation; Familienstand; Haushaltsgröße; Erwerbsstatus; private Internetnutzung; Bundesland. Zusätzlich verkodet wurde: Interviewdatum; Jahr der Rekrutierung; Fragebogenevaluation; Beurteilung der Befragung insgesamt; eindeutige ID-Kennung, Haushalts-Kennung und Personen-Kennung innerhalb des Haushalts.

Evolution of Debt Vulnerability Classifications in Sub-Saharan African Heavily Indebted Poor Countries

This data contains debt distress vulnerability classifications for thirty Sub-Saharan African countries that have been granted debt relief under the Heavily Indebted Poor Countries (HIPC) initiative. At the turn of the century, heavily indebted countries (most of which were located in Sub-Saharan Africa) were granted large-scale cancellations of external debt owed to the World Bank, International Monetary Fund, and African Development Bank. Since then, the debt sustainability of these countries has been closely monitored by the IMF and World Bank under the Debt Sustainability Analysis for Low Income Countries (DSA for LIC). This DSA has been conducted in Low-Income countries since 2005.

This dataset contains the external debt distress classifications for 30 Sub-Saharan African countries that have been granted debt reductions under the HIPC scheme from 2005 to 2019. If there was no DSA conducted in a year, the DSA classification of the previous year is shown.

Acknowledgements

Data collected by me from documents on https://www.imf.org/en/Publications/DSA.

Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: Total: Turkey data was reported at -4,335.601 CZK mn in 2023. This records a decrease from the previous number of 3,749.638 CZK mn for 2022. Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: Total: Turkey data is updated yearly, averaging -757.317 CZK mn from Dec 2013 (Median) to 2023, with 11 observations. The data reached an all-time high of 3,749.638 CZK mn in 2022 and a record low of -5,178.544 CZK mn in 2021. Czech Republic CZ: Foreign Direct Investment Financial Flows: Outward: Total: Turkey data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Financial Flows: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Amplify Energy has been written three times before and the previous write-ups and related comments give a good overview of the history of the Company and the quality of its asset base. DO EM GO’s write up in October 2020 was particularly well timed and the stock is up over 8X since that time, however the enterprise value is only 20% higher. Ray Palmer wrote it up in April of 2022 and the stock is up 12% since then but the enterprise value is 20% lower. The muted change in enterprise value has occurred as the Company has paid down over $100MM of debt while extending its reserve to production ratio. I believe the stock is cheaper and more derisked now than it has ever been (less than 1X debt/EBITDA) and on the cusp of major catalysts over the next 3-6 months that will uncover the tremendous value of Amplify’s assets. This write-up will focus specifically on two items which we believe haven’t been fully flushed out and create a path to significant cash flow inflection and share price gains which I expect to be above and beyond what has been discussed so far: 1) clarity on the enormous value of Beta and 2) specific actions planned by management to realize the massive undervaluation of its asset base. COMPANY OVERVIEW Amplify’s assets are mature properties that are generally past the higher decline stages typically characterized by newer production. Its production decline rate is only ~6% per year for the next decade, translating to a less capital-intensive business relative to most E&P companies, especially those in the unconventional/shale business that can have corporate decline rates of 25%-35%+. Amplify is more resilient against commodity price volatility and provides for higher FCF. This FCF is highly predictable with 85%-90% hedged for natural gas until year end 2025 and 45%-50% in 2026. The oil hedge position in 70-75% for 2024, 45%-50% in 2025 and 10-15% in 2026. A screenshot of a map Description automatically generated As the slide below shows, the Company is quite cheap based on its current proved, producing assets even with fairly draconian long term commodity price assumptions. The PV 10 analysis is very sensitive to long term strip prices, which for oil prices is currently in the mid $60s, however, I am of the opinion that long term prices will trend higher not lower in the long term. This undervaluation, however, is even more severe when one considers that the Beta PV10 is dinged for decommissioning liabilities that may be delayed by decades as discussed later. Based on a FCF valuation, the Company has guided to $20-$40 million of FCF in 2024 after $33-$40 million of growth expenditures. FCF yield to equity at midpoint is 12% with fully loaded capex and 27%, excluding Beta related growth capex. Amplify is one of the longest reserve lives and highest free cash flow yielding energy Company in my universe based on the just the existing asset base. A screenshot of a screen Description automatically generated THE BETA OPPORTUNITY The following slide gives an overview of the Beta asset: A map of oil and gas waters Description automatically generated Beta is a world-class oilfield initially discovered and developed by Shell in the 1980’s drilling low angle wells through the massive, highly permeable, stacked sandstones. The last significant drilling program in the asset consisted of 7 wells drilled by Amplify’s predecessor company. Three of these wells were drilled horizontally targeting the D-Sand and delivered 1st year average production of approximately 350 gross Bopd per well. The current development plan is designed to sidetrack out of existing, shut-in wells and horizontally target the D-Sand, utilizing the latest in rotary steerable and mapping well drilling technology to optimally place wells in areas with the highest remaining oil saturation. The Beta field has the potential to be a large growth asset for decades as there are still significant resources remaining to be recovered. The original oil in place estimates of the field range from 600 million to 1 billion barrels of oil and, with only approximately 100 million barrels recovered to date, the implied recovery factor is only between 11 to 16%. There are many analogue fields in the southern California basin with very similar reservoir properties that have recovered between 30 to 40% of the original oil in place. Implication being that there is 70 million to 260 million barrels of recoverable oil in place with the midpoint of estimates being 165 million barrels. These analogous fields generally have much tighter well spacing compared to the Beta field, which presents the opportunity for significant infill drilling. The key for faster drilling is to get your website indexed instantly by Google. BETA ECONOMICS AND VALUE The Company plans to increase production from Beta starting this year and 66% of its $50-$60 million 2024 capex budget is allocated to the Beta development and one time Beta facility upgrade. The remainder of the budget,...

Repossession is where a court order has been granted ordering a debtor to hand back a property to a creditor where the property was either used as collateral (for a mortgage, loan or an unsecured debt or loan which has been secured by an order charging land) or rented or leased in a previous contract between the creditor and the debtor. The court order can be made by the High Court (for mortgage repossessions), the County Court (for ejectment cases where a property has been rented) or the Magistrates Court (involving squatter cases). If the debtor fails to obey the terms of the court order, the creditor may apply to the Enforcement of Judgments Office (EJO) to enforce it. Physical repossession occurs when the EJO remove all persons in occupation of the property and their goods. In some occasions, repossession also occurs when there are no persons in occupation of the property and there are no goods are removed. Repossession is recorded as completed when all persons in occupation of the property have been removed, their goods removed and the property is handed over to the creditor. Repossession is also recorded as completed if there are no persons and or goods to remove and the property is handed over to the creditor. For the majority of cases, repossession will relate to a single property, but a court order can sometimes refer to more than one property. Property that may be repossessed include private dwellings or business premises that are either leased, rented or owner occupied. It can also relate to a piece of land that contains no dwellings such as agricultural land or wasteland. Property tenures that may be repossessed are those that are rented from a social housing authority or landlord (such as the Northern Ireland Housing Executive, or a Housing Association), those that are rented or leased from a private landlord, owner occupied properties that have a mortgage or secured loan registered against their property or properties that have a debt secured by way of an order charging land. Rented properties are repossessed by way of an ejectment order obtained at the County Court, with mortgaged properties repossessed by way of a mortgage possession order obtained at the High Court. On occasions, an order may be sought from the Magistrates' Court to repossess a property inhabited by a squatter. The postcode recorded for each repossession refers to the correspondence address of the person to whom enforcement has been sought. This is not always the address of the property to be repossessed as the property may not have a postal address (if it is a piece of land) or it may relate to the correspondence address of a landlord or a second home. A slight change to the methodology used to generate these data occurred during the period of this series. From 2007 to March 2014, the EJO have used the same methodology for recording repossessions (based on the recorded ‘return date’ repossession case held by enforcement officers (who manage a repossession case). Since April 2014, a different methodology has been used (based upon the date the repossession was completed which is marked against a case file). The change was made to make the methodology a more accurate reflection of the date the repossession was completed. Users of this data may have been able to self-identify themselves due to the low values in some cells. Primary and secondary disclosure control methods have been applied to this data, denoted by cells with missing data in the tables. Values of less than four, but not zero, were initially suppressed, but some of these values could have been calculated using some row and column totals and thus secondary suppression was applied to the next lowest value in the row and column. The dataset was created using the Central Postcode Directory (CPD). Unknown/missing postcodes are not shown but are included in the Northern Ireland totals. The data contain the number of cases disposed by each Assembly Area and have the following proportions of postcode coverage: 2012, 97.8%; 2013, 97.2%; 2014, 97.3%; 2015, 97.7%; 2016, 97.6%; 2017, 98.5%; 2018, 97.4%; 2019, 97.2%; 2020, 96.4%; 2021, 100%; 2022, 95.9%; 2023, 97.5%; 2024, 97.9%.

Repossession is where a court order has been granted ordering a debtor to hand back a property to a creditor where the property was either used as collateral (for a mortgage, loan or an unsecured debt or loan which has been secured by an order charging land) or rented or leased in a previous contract between the creditor and the debtor. The court order can be made by the High Court (for mortgage repossessions), the County Court (for ejectment cases where a property has been rented) or the Magistrates Court (involving squatter cases). If the debtor fails to obey the terms of the court order, the creditor may apply to the Enforcement of Judgments Office (EJO) to enforce it. Physical repossession occurs when the EJO remove all persons in occupation of the property and their goods. In some occasions, repossession also occurs when there are no persons in occupation of the property and there are no goods are removed. Repossession is recorded as completed when all persons in occupation of the property have been removed, their goods removed and the property is handed over to the creditor. Repossession is also recorded as completed if there are no persons and or goods to remove and the property is handed over to the creditor. For the majority of cases, repossession will relate to a single property, but a court order can sometimes refer to more than one property. Property that may be repossessed include private dwellings or business premises that are either leased, rented or owner occupied. It can also relate to a piece of land that contains no dwellings such as agricultural land or wasteland. Property tenures that may be repossessed are those that are rented from a social housing authority or landlord (such as the Northern Ireland Housing Executive, or a Housing Association), those that are rented or leased from a private landlord, owner occupied properties that have a mortgage or secured loan registered against their property or properties that have a debt secured by way of an order charging land. Rented properties are repossessed by way of an ejectment order obtained at the County Court, with mortgaged properties repossessed by way of a mortgage possession order obtained at the High Court. On occasions, an order may be sought from the Magistrates' Court to repossess a property inhabited by a squatter. The postcode recorded for each repossession refers to the correspondence address of the person to whom enforcement has been sought. This is not always the address of the property to be repossessed as the property may not have a postal address (if it is a piece of land) or it may relate to the correspondence address of a landlord or a second home. A slight change to the methodology used to generate these data occurred during the period of this series. From 2007 to March 2014, the EJO have used the same methodology for recording repossessions (based on the recorded ‘return date’ repossession case held by enforcement officers (who manage a repossession case). Since April 2014, a different methodology has been used (based upon the date the repossession was completed which is marked against a case file). The change was made to make the methodology a more accurate reflection of the date the repossession was completed. Users of this data may have been able to self-identify themselves due to the low values in some cells. Primary and secondary disclosure control methods have been applied to this data, denoted by cells with missing data in the tables. Values of less than four, but not zero, were initially suppressed, but some of these values could have been calculated using some row and column totals and thus secondary suppression was applied to the next lowest value in the row and column. The dataset was created using the Central Postcode Directory (CPD). Unknown/missing postcodes are not shown but are included in the Northern Ireland totals. The data contain the number of cases disposed by each Local Government District and have the following proportions of postcode coverage: 2010, 97.0%; 2011, 97.8% 2012, 97.8%; 2013, 97.2%; 2014, 97.3%; 2015, 97.7%; 2016, 97.6%; 2017, 98.5%; 2018, 97.4%; 2019, 97.2%; 2020, 96.4%; 2021, 100%; 2022, 95.9%; 2023, 97.5%; 2024, 97.9%.

Germany recorded a Government Debt to GDP of 62.50 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Germany Government Debt to GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.