This dataset supports the research exploring the impact of monetary policy instruments on the Colombian economy, focusing on the classical dichotomy and monetary neutrality. The analysis delves into how monetary policy, including instruments such as interest rates and money supply, influences both nominal and real variables in the economy. It also highlights the relationship between monetary policy and economic stability, particularly how central banks manage inflation and economic growth. Key sections explore the separation between nominal and real variables as explained by the classical dichotomy, and the principle of monetary neutrality, which argues that changes in money supply affect nominal variables without impacting real economic factors.

The dataset is structured around a combination of theoretical insights and simulations that analyze the effectiveness of monetary neutrality in the Colombian context, given both domestic and international economic challenges such as the war in Ukraine and agricultural sector disruptions. Through simulations, the dataset demonstrates the effects of monetary expansion on variables like inflation, production, and employment, providing a framework for understanding current economic trends and proposing solutions to socio-economic challenges in Colombia.

These codes help to replicate all the empirical analysis in the article: “What explains monetary policy rate uncertainty? Evidence from the Americas”, Applied Economics Letters (revise and resubmit), authored by Ana Aguilar, Carlos Madeira, Alejandro Parada, Christian Upper (Bank for International Settlements).

The Stata codes use Consensus Economics monthly survey reports with forecasts for countries in the Americas. These forecasts were collected as a Stata dataset, but the files cannot be shared due to copyright concerns. Future users must collect their own Consensus Forecasts data and then use these codes to replicate the empirical analysis of the article.

The data also includes an online appendix with robustness exercises to the main article. These robustness exercises estimate the same uncertainty models, but without the past quarter's inflation rate and GDP growth as additional controls. The results are qualitatively similar to the main article.

The dataset contains several macroeconomic time-series regarding the Russian economy. The time-series were collected from the Russian Federal State Statistics Service, the Bank of Russia and Federal Reserve Economic Data. The time-series included in the dataset are: 1. Time: 1-Jan-2005 = 1, every successive step in time represents one quarter 2. Date: Quarterly dates from 1-Jan-2005 to 1-Oct-2021 5. GDP: Quarterly nominal GDP in 2016 prices, excluding seasonal factor (bln RUB) 6. GDPgr: Nominal GDP growth rate (Quarterly, %) 7. M0: Base or high-powered money (bln RUB) 8. M0gr: M0 growth rate (Quarterly, %) 9. BM: M2 measure of money supply (bln RUB) 10. BMgr: M2 growth rate (Quarterly, %) 11. Interest: 90-day interbank rate (APR, %) 12. USDRUB: USD/RUB exchange rate (RUB) 12. EURRUB: EUR/RUB exchange rate (RUB) 13. Unemployment: Unemployment rate (%) 14. PPI: Domestic producer price index (index: 2015=100) 15. PPIgr: Growth rate of producer price index (Quarterly, %) 16. OIL: Spot prices of Brent per barrel (USD) 17. OILgr: Growth rate of Brent prices (Quarterly, %) 18. WAGE: Average monthly nominal wage rate (RUB) 19. WAGEgr: Changes in nominal wage rate (Quarterly, %) 3. CPI: Change in CPI as a ratio (End of quarter to end of previous quarter, %) 4. Inflation: Percentage change in CPI, calculated as Relative CPI - 100 (Quarterly, %)

The data was used to in time-series regression modelling to explain the factors affecting inflation in Russia. Some other modelling ideas for the dataset are: 1. Shift the focus from factor analysis to predicting future inflation 2. Perform factor analyses of other key macroeconomic variables, such as the GDP growth rate, the unemployment rate or the interest rate

Due to the low number of available observations because of quarterly sampling, this dataset is probably better suited to time-series econometric analysis rather than more modern machine learning methods.

This is the latest version of the Global VAR (GVAR) dataset - a global modelling framework for analyzing the international macroeconomic transmission of shocks while accounting for drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq, as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), from 1979Q2 to 2023Q3. These 33 countries cover more than 90% of world GDP.

It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2024). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2023Q3. University of Cambridge: Judge Business School (mimeo)”.

For more details on Global VAR (GVAR) modelling, see also www.mohaddes.org/gvar

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

The research delves into the underexplored area of how production network structures influence the severity of economic downturns, particularly during the last financial crisis. Utilizing the RSTAN database from the OECD, we meticulously derived critical measures from the input-output matrices for 61 economies. Our methodology entailed a panel analysis spanning from 2008 to 2010, which is a period marked by significant recessionary pressures. This analysis aimed to correlate economic performance with various production network metrics, taking into account control factors such as interest rates and the prevalence of service sectors. The findings reveal a noteworthy positive correlation between the density of production networks and economic resilience during the crisis, which remained consistent across multiple model specifications. Conversely, as anticipated, higher interest rates were linked to poorer economic performance, highlighting the critical interplay between monetary policy and economic outcomes during periods of financial instability. Given these insights, we propose a policy recommendation emphasizing the strategic enhancement of production network density as a potential buffer against economic downturns. This approach suggests that policymakers should consider the structural aspects of production networks in designing economic stability and growth strategies, thus potentially mitigating the impacts of future financial crises.

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.

S&P 500: A Bull or a Bear?

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

This dataset examines financial inclusion and bank stability in Ethiopia, containing panel data from 17 commercial banks over the period 2015-2023. In 2015, there were 17 commercial banks in Ethiopia but to maintain confidentiality, the names of commercial banks have been anonymized and are referred to by generic labels: 1, 2, 3, 4..., and 17. This process allows the dataset to be analyzed and shared openly in support of reproducibility and transparency in research.VariablesBank Stability (ZS): Computed using the Z-score to measure stability.Financial Inclusion Index (IFI): Developed using two-stage Principal Component Analysis (PCA) with 10 conventional and 5 digital indicators.Loan to Deposit Ratio (LDR): Computed based on the loan to deposit ratio.Provision to Loan (PL): Computes the loan loss provision ratio.Natural Logarithm of Total Assets (lnTA): Logarithmic form of total assets.Capital Adequacy Ratio (CAR): Computed by Tier-1 capital and Tier-2 capital divided by risk-weighted assets.Income Diversification (IND): Computed based on the non-interest income to total income ratio.Operational Efficiency Management (EF): Measured using Data Envelopment Analysis (DEA) with five input variables (salary and benefits, provisions, general expenses, branches, and deposits) and two output variables (net interest income and non-interest income).Real Lending Interest Rate (RLIR): Inflation-adjusted interest rate.GDP Growth Rate (GDP): Annual percentage change in GDP.This dataset provides comprehensive insights into the relationships between financial inclusion and bank stability, supporting future research and policy formulation.