Regularized mixtures improve inflation and interest rate forecasts, especially correcting overconfidence.
problem Improving density forecasts of Eurozone inflation and real interest rates.
method Construct regularized mixtures of density forecasts with various objectives and penalties.
result Regularized mixtures outperform individual forecasters, especially correcting overconfidence.
The paper evaluates various forecasting methods for inflation, finding ML models superior.
problem Forecasting inflation using disaggregated data and machine learning.
method Examines traditional and machine learning models, including random forest, for disaggregated and aggregated inflation forecasts.
result Aggregating disaggregated forecasts performs similarly to survey-based expectations and aggregate models.
Microdata improves inflation forecasts after major shocks, study finds.
problem Forecasting inflation in a non-stationary environment with microeconomic data.
method Developed a scan test to detect periods of micro forecast outperformance, combined with adaptive machine learning.
result Micro forecasts improve inflation predictions after major shocks, especially after 2020.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
problem Accurate inflation forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index.
method Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model that uses bidirectional information flow between levels and informative constraints on RNN parameters.
result BiHRNN significantly outperforms traditional RNN models in forecasting accuracy.
New latent variable model improves inflation forecasting accuracy.
problem Improving medium-term inflation forecasting accuracy.
method Formulated and tested a latent variable Phillips curve hypothesis using 3,968 factor combinations.
result Latent variable PC models outperform traditional models by 6-8 quarters.
New core inflation measure predicts future headline inflation.
problem Creating a better measure of core inflation for timely policy decisions.
method Assemblage Regression, a nonnegative ridge regression that optimizes subcomponent weights.
result Significant improvements in forecasting medium-term inflation developments.
We model the rate of inflation and unemployment in Austria since the early 1960s within the Phillips/Fisher framework. The change in labour force is the driving force representing economic activity in the Phillips curve. For Austria, this macroeconomic variable was first tested as a predictor of inflation and unemploym…
Paper uses LSTM to predict inflation, finds it performs well over long periods.
problem Predicting inflation using machine learning models.
method Applies LSTM, a recurrent neural network, to forecast inflation over time.
result LSTM model performs well at long horizons and during uncertain economic times.
Tree-based synthesis improves forecast accuracy in GDP and inflation.
problem Improving forecast accuracy in GDP and inflation.
method Developed a nonparametric synthesis function using regression trees.
result Tree-based synthesis leads to improved forecast accuracy.
A new neural network model predicts inflation and output gap more accurately.
problem Traditional Phillips curves struggle with unobserved inflation expectations and output gaps.
method Hemisphere Neural Network (HNN) that estimates latent states for inflation and output gap.
result HNN accurately forecasts inflation and identifies a large positive output gap starting from late 2020.
A linear and lagged relationship between inflation and labor force change rate, p(t)= A1dLF(t-t1)/LF(t-t1)+A2 was found for developed economies. For the USA, A1=4.0, A2=-0.03075, and t1=2 years. It provides a RMS forecasting error (RMFSE) of 0.8% at a two-year horizon for the period between 1965 and 2002 (the best amon…
Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In o…
An empirical model is presented linking inflation and unemployment rate to the change in the level of labour force in Switzerland. The involved variables are found to be cointegrated and we estimate lagged linear deterministic relationships using the method of cumulative curves, a simplified version of the 1D Boundary …
Kalshi prediction markets forecast cryptocurrency volatility through monetary policy and inflation signals.
problem Forecasting cryptocurrency volatility using prediction markets.
method Monetary policy and inflation signals from Kalshi prediction markets.
result Signals from Kalshi prediction markets predict cryptocurrency volatility with statistical significance.
The evolution of the rate of price inflation and unemployment in Japan has been modeled within the Phillips curve framework. As an extension to the Phillips curve, we represent both variables as linear functions of the change rate of labor force. All models were first estimated in 2005 for the period between 1980 and 2…
RainfallBench benchmarks GNSS-based precipitation nowcasting models, addressing complex meteorological challenges.
problem Evaluation of precipitation nowcasting models in meteorology is insufficient due to focus on periodic variables.
method RainfallBench dataset and specialized evaluation protocols for multi-scale, multi-resolution, and extreme rainfall events.
result Bi-Focus Precipitation Forecaster (BFPF) enhances rainfall time series forecasting by incorporating domain-specific priors.
We re-estimate statistical properties and predictive power of a set of Phillips curves, which are expressed as linear and lagged relationships between the rates of inflation, unemployment, and change in labour force. For France, several relationships were estimated eight years ago. The change rate of labour force was u…
We develop a new DTSM with nonlinearities using Gaussian Processes for better interest rate forecasting.
problem Linear DTSMs fail to capture nonlinear relationships between macroeconomic variables and interest rates.
method We propose a Gaussian Process-based sequential Monte Carlo estimation and forecasting scheme.
result Nonlinear models outperform linear ones in forecasting core inflation, leading to significant economic value gains.
In this paper, we propose a stochastic investment model for actuarial use in South Africa by modelling price inflation rates, share dividends, long term and short-term interest rates for the period 1960-2018 and inflation-linked bonds for the period 2000-2018. Possible bi-directional relations between the economic seri…
Using an analog of the boundary element method in engineering and science, we analyze and model unemployment rate in Austria, Italy, the Netherlands, Sweden, Switzerland, and the United States as a function of inflation and the change in labor force. Originally, the model linking unemployment to inflation and labor for…
LLM forecasting benchmarks suffer from information leakage, which confounds model performance.
problem LLM forecasting benchmarks suffer from information leakage.
method A retrieval-augmented LLM forecaster observes only decision-time information.
result The full pipeline obtains a median monthly Spearman rank IC of +0.154.
The analysis of dollar inflation performed by the authors through the approximation of empirical data for 1913-2012 with a power-law function with an accelerating log-periodic oscillation superimposed over it has made it possible to detect a quasi-singularity point around the 17th of December, 2012. It is demonstrated …
New framework detects time-varying economic persistence.
problem Time-varying persistence in economic shocks.
method Localized regression techniques to identify evolving heterogeneity.
result Substantial persistence variations align with macroeconomic events.
The M5 competition tackles overdispersed retail sales forecasting with GAMLSS.
problem Overdispersed and zero-inflated retail sales data.
method Distributional forecasting using GAMLSS framework.
result GAMLSS provides better probabilistic forecasting for count data.
The paper uses CPI growth rates to improve LGD predictions for CRE loans.
problem Challenges in forecasting LGD for CRE loans due to extended resolution times and restricted data.
method Combines internal and public data, including CPI growth rates, to forecast CRE LGD.
result Incorporating CPI at the time of default improves LGD prediction accuracy.
Develops ML tool for macroeconomic forecasting with clear interpretations.
problem Forecasting and understanding macroeconomic parameters over time.
method Macroeconomic Random Forest (MRF) algorithm, Generalized Time-Varying Parameters (GTVPs).
result Clear forecasting gains and accurate predictions of unemployment and inflation.
Model predicts volatility and dependencies in EUA and energy prices.
problem Analyzing uncertainty and dependencies in European carbon and energy prices.
method Probabilistic multivariate conditional time series model with VECM-Copula-GARCH structure.
result Forecasting performance evaluated in an extensive rolling-window study.
New methods for scoring function decomposition improve forecast evaluation.
problem Improving forecast evaluation and understanding forecast components.
method Linear recalibration of forecasts for miscalibration, discrimination, and uncertainty.
result Enhanced statistical power and deeper insights into forecast components.
Paper proposes efficient methods for forecasting with large datasets.
problem Forecasting with large, high-dimensional economic data sets.
method Bayesian hierarchical priors, factor graphs, message passing algorithms, Generalized Approximate Message Passing (GAMP).
result The proposed methods outperform traditional approaches in forecasting U.S. price inflation.
New method interprets machine learning forecasts as historical analogies.
problem Interpreting machine learning predictions as a sum of predictor contributions.
method Expressing predictions as a linear combination of in-sample values with weights based on pairwise proximity scores.
result The approach provides sparser interpretations in settings with many regressors and little training data.
A TTA framework improves forecasting accuracy in non-stationary time series.
problem Improving forecasting accuracy in non-stationary time series.
method Normalization-based test-time adaptation for causal timeseries forecasting and direction classification.
result Normalization-based TTA improves forecasting error in synthetic gradual drift and can even hurt in aggressive norm-only adaptation in financial markets.
News on inflation and monetary policy impacts US household inflation expectations.
problem Understanding how news affects inflation expectations.
method Monthly disaggregated US data from 1978 to 2016, controlling for various factors.
result News on rising inflation and easier monetary policy has a stronger impact on inflation expectations.
The study forecasts hourly intraday electricity prices using ensemble methods.
problem Weak-form efficiency of hourly German Intraday Continuous Market prices.
method Probabilistic forecasting with ensemble trajectories, generalized additive model, and lasso penalty.
result The mixture model outperforms benchmarks in forecasting price distribution and volatility.
FinStressTS creates synthetic benchmarks for financial forecasting, revealing model weaknesses.
problem Limited failure attribution in real-world financial benchmarks.
method Synthetic benchmark with 30 diagnostic environments linked to six mechanism families.
result Model performance varies by mechanism type, with autoregressive models often outperforming Transformers.
This paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation.…
Bitcoin reacts negatively to inflation surprises, contrary to belief.
problem Bitcoin's ability to hedge inflation is questioned.
method Examined cryptocurrency responses to macroeconomic news announcements.
result Bitcoin's price decreases by 24 bps in response to inflationary surprises.
Study finds relevance of exchange and inflation rates to economic factors.
problem Determining the relevance of exchange and inflation rates to economic factors.
method Introduced concept of adequacy, established positive relation between exchange and inflation rates and other economic factors.
result Close positive relation found between exchange and inflation rates and other economic factors.
The paper discusses the role of monetary policy when potential output depends on the inflation rate. If the intention of the central bank is to maximize actual output growth, then it has to be credibly committed to a strict inflation targeting rule, and to take the MOGIR (the Maximizing Output Growth Inflation Rate) as…
ZICO learns DAGs from zero-inflated count data efficiently.
problem Learning network structures from zero-inflated count data.
method ZICO uses node-wise likelihoods with canonical links and a differentiable surrogate constraint for acyclicity.
result ZICO achieves superior performance and faster runtimes on simulated data.
Model financial dynamics using 2-manifold geometries, revealing the torus as best for cyclical data.
problem Financial forecasting using complex market data.
method Embedding market data onto 2-manifolds (S2, R2, H2, T) guided by uniformization theorem, inferring latent curvature.
result The torus geometry best predicts cyclical financial data, aligning with IS-LM theory.
The evolution of inflation, p(t), and unemployment, UE(t), in Japan has been modeled. Both variables were represented as linear functions of the change rate of labor force, dLF/LF. These models provide an accurate description of disinflation in the 1990s and a deflationary period in the 2000s. In Japan, there exists a …
New ZIPLN model accounts for zero-inflation in multivariate count data.
problem Zero-inflation in multivariate count data.
method Introduced Zero-Inflated PLN (ZIPLN) model with variational inference.
result ZIPLN significantly improves log-likelihood and reduces dispersion.
Generative models' evaluation scores can be misleading, leading to inflated grades.
problem Misleading evaluation scores for generative models.
method Analyzed and compared various scores for evaluating synthetic vs. ground-truth data.
result The Eden score avoids grade inflation and better aligns with human perception.
Study enhances financial forecasting with machine learning and fuzzy MCDM.
problem Increasing financial uncertainty and market complexity.
method Integrates machine learning (XGBoost, LSTM, GNN) and intuitionistic fuzzy MCDM.
result High forecasting accuracy with low MAPE and narrow confidence intervals.
Paper proposes copula-based models for analyzing multivariate zero-inflated continuous data.
problem Challenges in analyzing multivariate zero-inflated continuous data with mixed discreteness and continuity.
method Proposes two copula-based density estimation models and rectified Gaussian copula.
result Demonstrates superior performance compared to conventional methods.
We test for the long-run relationship between stock prices, inflation and its uncertainty for different U.S. sector stock indexes, over the period 2002M7 to 2015M10. For this purpose we use a cointegration analysis with one structural break to capture the crisis effect, and we assess the inflation uncertainty based on …
Interest rate market models, like the LIBOR market model, have the advantage that the basic model quantities are directly observable in financial markets. Inflation market models extend this approach to inflation markets, where zero-coupon and year-on-year inflation-indexed swaps are the basic observable products. For …
Starting with an ideal triangulation of the interior of a compact 3-manifold M with boundary, no component of which is a 2-sphere, we provide a construction, called an inflation of the ideal triangulation, to obtain a strongly related triangulations of M itself. Besides a step-by-step algorithm for such a construction,…