This paper reconsiders how labor market competition shapes skill development - integrating the perspectives of both firms and workers. While existing models often predict that firms will underinvestment in training due to a fear of poaching, we show that competition can instead serve as a...

This paper studies the transition to high inflation during the COVID-19 pandemic, using a behavioral version of the New Keynesian model, which replaces the conventional assumption of Rational Expectations with subjective and heterogeneous expectations. Different shares of agents in the economy...

This paper investigates potential nonlinearities in the gain function, which, under adaptive learning, regulates the updating of agents' beliefs in response to recent forecast errors. I use data on professional survey forecasts to estimate nonparametric functional-coefficient regression models....

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in...

Financial markets (and more generally the real economy) display a wide range of important nonlinearities. This paper focuses on stock returns, which are skewed left - generating crashes - and have volatility that moves over time, is itself skewed, is strongly related to the level of prices, and...

Aligning academic research with policymaking is vital for addressing China's energy challenges. This study introduces an AI-driven framework combining BERTopic, semantic similarity analysis, and deep reinforcement learning (DRL) to evaluate alignment between 106,661 English-language academic...

The purpose of this study is to identify the most effective supervised machine learning models for predicting the financial performance of companies listed on the BIST100 index. In the rapidly evolving field of financial forecasting, machine learning techniques offer robust predictive...