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This paper revisits the cross-country growth empirics debate using a novel Limited Information Bayesian Model Averaging framework to address model uncertainty in the context of a dynamic growth model in panel data with endogenous regressors. Our empirical findings suggest that once model uncertainty is accounted for there is strong evidence that initial income, investment, life expectancy, and population growth are robustly correlated with economic growth. We also find evidence that debt, openness, and inflation are robust growth determinants. Overall, the set of our robust growth determinants differs from those identified by other studies that incorporate model uncertainty, but ignore dynamics and/or endogeneity. This underscores the importance of accounting for model uncertainty and endogeneity in the investigation of growth determinants.

Exports and Imports --- Inflation --- Macroeconomics --- Demography --- Demographic Trends, Macroeconomic Effects, and Forecasts --- Health: General --- Personal Income, Wealth, and Their Distributions --- Empirical Studies of Trade --- Price Level --- Deflation --- Population & migration geography --- Health economics --- International economics --- Population growth --- Health --- Personal income --- Terms of trade --- Population --- Income --- Economic policy --- nternational cooperation --- Prices --- Nternational cooperation

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This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.

Econometrics --- Foreign Exchange --- Bayesian Analysis: General --- Simulation Methods --- 'Panel Data Models --- Spatio-temporal Models' --- Model Evaluation and Selection --- Econometric Modeling: General --- Estimation --- Bayesian inference --- Econometrics & economic statistics --- Currency --- Foreign exchange --- Bayesian models --- Gravity models --- Estimation techniques --- Exchange rate arrangements --- Econometric models --- Panel Data Models --- Spatio-temporal Models

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This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Artificial intelligence --- Banks --- Capital and Ownership Structure --- Computer security --- Cyber risk --- Depository Institutions --- Diffusion Processes --- Econometric and Statistical Methods: Special Topics: General --- Economic sectors --- Finance --- Finance: General --- Financial Institutions and Services: General --- Financial Institutions and Services: Government Policy and Regulation --- Financial Instruments --- Financial Risk and Risk Management --- Financial sector policy and analysis --- Financial sector stability --- Financial sector --- Financial services industry --- Financial services --- Financing Policy --- General Aggregative Models: Forecasting and Simulation --- General Financial Markets: Government Policy and Regulation --- Goodwill --- Industries: Financial Services --- Industries: Information Technololgy --- Information technology industries --- Information technology --- Innovation --- Institutional Investors --- Intellectual Property Rights: General --- Intelligence (AI) & Semantics --- Large Data Sets: Modeling and Analysis --- Machine learning --- Micro Finance Institutions --- Model Construction and Estimation --- Mortgages --- Non-bank Financial Institutions --- Online Safety & Privacy --- Pension Funds --- Research and Development --- Security measures --- Technological Change --- Technological Change: Choices and Consequences --- Technological innovations --- Technology --- Value of Firms --- Hong Kong Special Administrative Region, People's Republic of China