The paper uses machine learning to forecast macroeconomic outcomes with high-dimensional data.
problem Forecasting the full conditional distribution of macroeconomic outcomes.
method Systematically integrating three key principles: high-dimensional data with regularization, rigorous out-of-sample validation, and incorporating nonlinearities.
result Regularization via shrinkage is essential to control model complexity, while nonlinearities yield limited improvements in predictive accuracy.
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
problem Evaluating the predictive value of macroeconomic indicators for public health targets.
method Examined multiple forecasting approaches including neural networks, generalized additive models, random forests, and time series models with exogenous indicators.
result Macroeconomic indicators provide consistent and reproducible predictive signals for health workforce and infrastructure measures, but less so for other targets.
The paper identifies key macroeconomic events affecting exchange rate volatility.
problem Understanding which macroeconomic events impact exchange rate volatility.
method Data-driven approach to select relevant macroeconomic events using sparsity-based methods.
result The identified macroeconomic events significantly impact exchange rate volatility.
The study examines how market trade randomness influences price and return volatility.
problem The accuracy of predicting market-based volatilities and macroeconomic variables is limited.
method Analyzes time series of trade values and volumes, and develops econometric methodologies for predicting volatilities.
result Current macroeconomic models underestimate the accuracy of predicting market-based volatilities and macroeconomic variables.
Ensemble of MFESNs improves macroeconomic forecasting.
problem Improving accuracy in macroeconomic forecasting.
method Hedge and Follow-the-Leader schemes applied to MFESNs.
result Ensemble models outperform individual models in forecasting.
Bayesian neural networks improve macroeconomic forecasting and model nonlinearities.
problem Handling small T, big K macroeconomic datasets with temporal dependence.
method Developed Bayesian neural networks with mixture activation functions, shrinkage priors, and stochastic volatility.
result BNNs produce precise density forecasts, often better than other methods.
The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.
problem Uncertainty in predicting macroeconomic variables like prices and returns.
method Defines theoretical lower bounds of uncertainty and upper limits on forecast accuracy based on statistical moments and trade volumes.
result Accuracy of forecasts of probabilities of macroeconomic variables doesn't exceed Gaussian approximations.
Machine Learning improves macroeconomic forecasting by capturing nonlinearities.
problem Improving macroeconomic forecasting accuracy.
method Study four features (nonlinearities, regularization, cross-validation, loss function) in data-rich and data-poor environments.
result Nonlinearity is the key to improving forecasting accuracy.
Bank transactions help predict macroeconomic indexes faster and more accurately.
problem Lag in macroeconomic index availability and autoregressive models' limitations in complex scenarios.
method Use financial transactions data to estimate macroeconomic indexes using neural networks and smart sampling.
result Neural network approach outperforms baseline methods on hand-crafted features based on transactions.
The study analyzes macroeconomic factors affecting copper futures volatility and long-term correlation with S&P 500.
problem Understanding the impact of macroeconomic variables on copper futures volatility and long-term correlation.
method Employed GARCH-MIDAS and DCC-MIDAS modeling frameworks to examine the influence of low-frequency macroeconomic variables on copper futures returns and long-term correlation with S&P 500.
result PPI is the most efficient macroeconomic variable impacting copper futures returns, and MIDAS filter improves model fitness and long-run relationship.
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
Complexity science offers new insights into macroeconomics and finance.
problem Insufficient understanding of economic and financial phenomena.
method Adopting complexity science to better understand complex systems.
result Complex system characteristics can benefit financial analysts, regulators, and policymakers.
Expert system predicts credit card charge-offs using macroeconomic indicators.
problem Managing charge-off rates in the credit card industry.
method Developed an expert system using machine learning and macroeconomic indicators.
result Achieved mean squared error values of 1.15E-03 and 1.04E-03.
Payments data and machine learning improve nowcasting accuracy for macroeconomic indicators.
problem Lagged indicators in linear models are insufficient during crisis periods.
method Non-traditional payments data, nonlinear machine learning, and tailored cross-validation.
result Improved macroeconomic nowcasting accuracy up to 40% during crises.
HANET combines LSTM and attention mechanisms for better financial forecasting.
problem Lack of distinct macroeconomic regimes in financial datasets.
method Hierarchical Cross-Attention mechanism integrating long-run macro contexts with high-frequency market dynamics.
result HANET outperforms neural forecasters, especially during turbulent periods.
Study improves retail demand forecasting by integrating macroeconomic data.
problem Lack of accurate demand forecasting due to incomplete data.
method Enriched time series data with macroeconomic variables; compared regression and machine learning models.
result Improved accuracy in predicting retail demand through comprehensive data integration.
Interactive tools help teach economics online.
problem Teaching macroeconomic models online.
method Browser-based simulation toolkits.
result Intuitive approach to comparative statics.
Bayesian model uses simple functions to forecast macroeconomic data.
problem Forecasting large datasets in macroeconomics with complex nonlinear relationships.
method Sum of simple two-component location mixtures, logistic function threshold, conjugate priors.
result Accurate point and density forecasts in US macroeconomic aggregates.
Educational game on crypto investment helps students grasp macroeconomics.
problem Weak connections between microeconomic decision-making and macroeconomic concepts in classroom games.
method Design and study of an educational game on cryptocurrency investment.
result Engages students in understanding macroeconomics through incentivized individual investment decisions.
SMC analysis reveals key transient effects in macroeconomic ABM.
problem Analysis of complex ABMs is challenging and often relies on ad hoc methods.
method Statistical model checking (SMC) implemented through MultiVeStA.
result Clear contrast across parameter families in macro-financial and structural sweeps.
In this paper we sketch some reflections on the pitfalls and inconsistencies of the research program - currently dominant among the profession - aimed at providing microfoundations to macroeconomics along a Walrasian perspective. We argue that such a methodological approach constitutes an unsatisfactory answer to a wel…
The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure adjustment in different macroeconomics states by focusing and discussing the relative importance of firm-specific and macroeconomic characteristics from an alternative scope in U.S. This study extends th…
News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.
problem Drivers of the excess bond premium (EBP).
method News attention to 180 topics captures up to 80% of EBP variation and forecasts macroeconomic movements.
result News attention to financial intermediaries and crises drives up the EBP and predicts macroeconomic downturns.
Deep learning improves macroeconomic forecasting and risk assessment.
problem Improving accuracy in macroeconomic forecasting and sovereign risk assessment.
method Nowcasting and forecasting using deep learning techniques.
result Deep learning methods outperform traditional econometric techniques in out-of-sample performance.
LSTM models improve macroeconomic forecasting with mixed frequency data.
problem Improving accuracy of macroeconomic forecasts using mixed frequency data.
method Adapted LSTM model to mixed frequency data, using U-MIDAS scheme.
result Proposed LSTM models outperform conventional MIDAS models in out-of-sample predictive performance.
Deep RL solves complex macroeconomic models.
problem Solving dynamic stochastic general equilibrium models with bounded rationality.
method Using deep reinforcement learning to model agents as neural networks.
result Artificially intelligent agents can solve models in all policy regimes.
We demonstrate using multi-layered networks, the existence of an empirical linkage between the dynamics of the financial network constructed from the market indices and the macroeconomic networks constructed from macroeconomic variables such as trade, foreign direct investments, etc. for several countries across the gl…
Paper improves asset allocation using machine learning for regime detection.
problem Improving asset allocation strategies in uncertain economic conditions.
method Machine learning for regime detection, modified k-means algorithm, portfolio optimization.
result Significant portfolio performance improvements over traditional benchmarks.
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.
Masanao Aoki developed a new methodology for a basic problem of economics: deducing rigorously the macroeconomic dynamics as emerging from the interactions of many individual agents. This includes deduction of the fractal / intermittent fluctuations of macroeconomic quantities from the granularity of the mezo-economic …
Model predicts operational risk using HMMs with economic covariates.
problem Predicting operational risk losses with time-dependent structures and economic covariates.
method Hidden Markov Models extended to multivariate observations with an auxiliary economic variable.
result Calibration results show relevance of including economic covariates.
In this paper, we present own point of view how the unexpected fluctuations of the long-term real interest rate can be explained. We describe a macroeconomic environment by the modification of the fundamental macroeconomic equilibrium model called the IS-LM model. Last but not least, we suggest a possible cooperation b…
Study shows how macroeconomic news affects intraday price and order flow dynamics.
problem Understanding how macroeconomic news impacts intraday price and order flow dynamics.
method Structural VAR model identified through heteroskedasticity, estimated at one-second frequency for each 15-minute interval.
result Macroeconomic news announcements reshape price-flow dynamics, with significant impacts on price and flow impacts at the one-second horizon.
Deep RL solves complex economic models with heterogeneous agents.
problem Solving models with heterogeneous economic actors is difficult.
method Reinforcement Learning techniques for solving general equilibrium models.
result Successfully captures economic behaviors induced by age-based health risks.
Improved volatility forecasts for U.S. stocks using social media and news data.
problem Challenges in forecasting equity market volatility due to infrequency and variability of macroeconomic announcements.
method Estimating public attention and sentiment towards scheduled macroeconomic variables using various data sources and machine learning.
result Significant improvement in volatility forecasts for U.S. stocks, up to 14.99% on average.
Develops a statistical model for SOFR term structure in incomplete markets.
problem Incomplete liquidity and completeness in SOFR derivatives market.
method Statistical model incorporating macroeconomic factors and jumps in SOFR rates.
result Model is well-suited for risk management and derivatives pricing.
This paper suggests that business cycles may be a manifestation of coupled real economy and stock market dynamics and describes a mechanism that can generate economic fluctuations consistent with observed business cycles. To this end, we seek to incorporate into the macroeconomic framework a dynamic stock market model …
The study finds that low frequency macroeconomic variables are more important for short-term electricity price forecasting.
problem Improving short-term forecasting of daily electricity prices using macroeconomic variables.
method Developed a Bayesian reverse unrestricted MIDAS model to account for frequency mismatch.
result Inclusion of macroeconomic low frequency variables improves short-term forecasts more than using only surveys or industrial production data.
We show how random matrix theory can be applied to develop new algorithms to extract dynamic factors from macroeconomic time series. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N / T is fixed. In this regime the unde…
The International Trade Network (ITN) is the network formed by trade relationships between world countries. The complex structure of the ITN impacts important economic processes such as globalization, competitiveness, and the propagation of instabilities. Modeling the structure of the ITN in terms of simple macroeconom…
A model simulates how different types of traders react to macroeconomic news.
problem Understanding how various market participants respond to macroeconomic surprises.
method Developed a calibrated data generation process (DGP) with four trader archetypes and a Monte Carlo simulation.
result Higher information and lower risk-averse traders take larger positions and achieve higher average wealth.
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
problem Timeliness of write-downs for adverse macroeconomic and industry outcomes versus firm-specific issues.
method Comparative analysis of write-downs driven by macroeconomic and industry outcomes versus firm-specific outcomes.
result Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
Paper uses VAEs to model yield curves without arbitrage violations.
problem Forecasting yield curves across diverse macroeconomic regimes leads to arbitrage violations.
method Proposes a two-stage architecture with CVAEsT+LS and Neural SDEs penalized by No-Arbitrage PDE.
result Significantly reduces forecasting errors and overcomes HJM model limitations.
This work is devoted to modelling and identification of the dynamics of the inter-sectoral balance of a macroeconomic system. An approach to the problem of specification and identification of a weakly formalized dynamical system is developed. A matching procedure for parameters of a linear stationary Cauchy problem wit…
Study predicts bond yields using machine learning and ultimate forward rates.
problem Forecasting bond yields using ultimate forward rates.
method Applied de Kort-Vellekooptype methodology for UFR estimation, used linear and nonlinear machine learning techniques.
result Nonlinear machine learning models outperform linear models in bond yield forecasting.
Tabular Q-learning outperforms advanced RL methods in monetary policy.
problem Dynamic setting of short-term interest rates to stabilize inflation and unemployment under uncertain macroeconomic conditions.
method Discrete-action Markov Decision Process with tabular Q-learning, SARSA, Actor-Critic, Deep Q-Networks, Bayesian Q-learning, POMDP formulations.
result Standard tabular Q-learning achieved the best performance (-615.13 +- 309.58 mean return) compared to advanced RL methods and traditional policy rules.
Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods …
KLD token adjusts supply based on macroeconomic debt index, creating deflationary effect.
problem Managing deflationary pressures in digital assets.
method Debt-indexed supply adjustments linked to macroeconomic data.
result Deflationary mechanism strengthens as debt rises.