Topic
Econometrics & Forecasting
Forecasting, time series, econometrics and nowcasting.
- Papers featured
- 354
- Last 12 months
- 71
- Cited 100+
- 3
- Top venue
- arXiv
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Most cited
Featured papers in this topic with the most citations today.
- 15 May 2024195cites
TKAN: Temporal Kolmogorov-Arnold Networks
The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
SSRN
- 4 Oct 2023188cites
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022
The article explores deep learning models for financial forecasting, discussing their structures, uses, pros and cons, and potential future research areas.
arXivIn Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
- 21 Feb 2024108cites
Robust agents learn causal world models
The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
Machine learningIn International Conference on Learning Representations
- 13 Mar 202446cites
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
The study reveals that deep neural networks using linear complex-valued RNNs and MLPs can accurately approximate regular causal sequence-to-sequence maps, with complex eigenvalues near unit disk aiding in information storage.
Machine learning
- 28 Aug 202443cites
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
The paper introduces the State Space Transformer model for time series forecasting, which effectively captures global and local patterns, offering superior performance with less memory and computational cost.
Machine learningIn Proceedings of the 34th ACM International Conference on Information and Knowledge Management
- 18 Dec 202437cites
Causal Diffusion Transformers for Generative Modeling
The article discusses Causal Diffusion, a framework that enhances diffusion models' performance and allows a seamless shift between autoregressive and diffusion generation modes, achieving top results on the ImageNet generation benchmark.
Machine learning
- 3 Oct 202435cites
Assumption violations in causal discovery and the robustness of score matching
The paper evaluates the performance of recent causal discovery methods on observational data, revealing that score matching-based methods excel in difficult scenarios, setting a new evaluation standard for causal discovery methods.
Machine learningIn Neural Information Processing Systems
- 12 Oct 202334cites
Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions
Quantum-Enhanced Forecasting for Time Series: The study introduces a time series forecasting method, Quantum Gramian Angular Field (QGAF), that merges quantum computing and deep learning to enhance the accuracy of time series classification and forecasting, and validates its effectiveness using major stock market datasets.
arXivIn Finance Research LettersFeatured 2×
- 3 Apr 202431cites
Supervised autoencoder MLP for financial time series forecasting
The study investigates the use of supervised autoencoders in improving financial forecasting through precise parameter tuning.
arXivIn Journal of Big Data
- 24 Aug 202331cites
Retail Demand Forecasting: A Comparative Study for Multivariate Time Series
The study creates improved retail demand prediction models using macroeconomic factors and past sales data.
arXivIn Journal of Mathematics and Statistics Studies
- 23 Jan 202427cites
The econometrics of happiness: Are We Underestimating the Returns to Education and Income?
Value Rounding Behavior: The study addresses the issue of response scale simplification in surveys, particularly by less educated respondents, and introduces a model to estimate latent subjective wellbeing.
arXiv
- 3 Jul 202426cites
Cost-aware Bayesian optimization via the Pandora's Box Gittins index
The paper suggests using the Gittins index from the Pandora's Box problem in economics as a function for cost-aware Bayesian optimization.
Machine learningIn Neural Information Processing Systems
Latest
- 6 Nov 20263fanfare
xtlp: A new Stata command for panel local projection
A new Stata command implements panel local projections with split-panel jackknife correction, removing the Nickell bias that invalidates standard inference in dynamic models across regions or countries.
arXiv
- 6 Nov 20263fanfare
When Does $N$ Buy Precision? Heterogeneous Responses to Aggregate Shocks in Micro Panels
Panel VARs converge at the faster rate with N and T combined, while panel LPs converge only in T; the paper characterizes this efficiency-robustness trade-off and provides a test to choose between them.
arXiv
- 6 Nov 20262fanfare
Identification through Heteroskedasticity in One Lemma: Partial Identification, an Eigenvalue Construction, and a Caution on Rank Tests
The study unifies identification results for structural shocks when variance paths differ, solving a generalized eigenvalue problem that nests prior work and clarifies what rank tests on second moments actually identify.
arXiv
- 6 Nov 20262fanfare
FinForesight: A Diagnostic Backtesting Benchmark for Deep Research Systems in Financial Forecasting
A diagnostic backtesting framework evaluates deep research systems on four financial forecasting tasks across three markets, with controlled information cutoffs to prevent information leakage and assess whether historical backtests predict live performance.
arXiv
- 6 Nov 20262fanfare
Aggregate Calibration Can Mask Stress-State Undercoverage: Bounded-Memory Forecast Intervals under Recurrent Financial Stress
Aggregate forecast-interval coverage can hide state-specific failures when economic regimes return after long absence; recent-only calibration undercovers during high volatility despite meeting overall targets.
arXiv
- 6 Nov 20264fanfare
How Much Data Does an Information Coefficient Need? The Sampling Distribution of a Cross-Sectional IC, and Why Breadth Does Not Buy Precision
Cross-sectional IC volatility has a floor from factor risk that does not shrink with breadth, capping effective sample size at roughly fifty observations.
SSRN
- 6 Nov 20264fanfare
Behind the curve: Reassessing inflation risks with a High-dimensional Distributional VAR (HiDVAR)
HiDVAR, a data-rich distributional framework, improves inflation tail-risk assessment by 28% in CRPS relative to standard benchmarks and performs well in real-time evaluation.
RePEc
- 6 Nov 20263fanfare
Consumers' Attention to Monetary Policy: The Importance of Having "Skin in the Game"
Survey of 170,000 consumers shows that attention to monetary policy is driven by incentives; those planning major purchases are significantly more attentive to interest rate news.
RePEc
- 6 Nov 20263fanfare
Improving inflation forecasting by modeling expectation de-anchoring in low-inflation periods
A model allowing expectations to shift to a low-inflation equilibrium cuts forecast errors roughly 50 percent versus central bank projections for euro area core inflation in the 2010s.
RePEc
- 6 Nov 20263fanfare
Causal State-Dependent Local Projections
The study develops nonparametric local projections to identify how economic shocks affect outcomes differently across states, uncovering a hump-shaped investment response to monetary policy that linear methods understate.
RePEc
- 6 Nov 20263fanfare
News shocks and inflation: theory and evidence from large oil discoveries
Large oil discoveries with production lags of five years show that anticipated future output raises core inflation a decade ahead, with effects concentrated in nontradables under fixed exchange rates.
RePEc
- 6 Nov 20263fanfare
Affective Sensitivity and Inflation Expectations across the Income Distribution
Lower-income households form inflation beliefs two and a half times more sensitive to their emotional state than higher-income households, affecting expectations substantially independent of fundamentals.
RePEc
- 6 Nov 20262fanfare
Stress in the Press or Stress in the Market: A New Measure of Perceived Financial Stress
A news-based stress index for Canada reveals that perceived financial stress operates through household precautionary behavior, independently of market-based financial shocks.
RePEc
- 30 Oct 20262fanfare
Information Flow and Market Risk: Transformer-Based Evidence on Sentiment and Volatility Dynamics
Transformer-based news sentiment does not improve volatility forecasts beyond persistence and market indicators in walk-forward tests across eleven stocks and multiple horizons.
arXiv
- 30 Oct 20262fanfare
Time-Varying Quantile VAR Models: Estimation and Inference
The research develops impulse response analysis for quantile VARs and finds that downside asymmetry in credit spreads and industrial production diminishes at longer forecast horizons.
arXiv
- 30 Oct 20262fanfare
Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks
The study combines survey sampling and causal activity clocks to forecast NVIDIA implied volatility surfaces, finding that stratified allocation outperforms simple random sampling for cross-sectional accuracy.
arXiv
- 30 Oct 20262fanfare
A Semiparametric Real-Time GARCH Model with Nonstationary Variance
A two-step estimator combines kernel smoothing with GARCH to capture gradual variance changes while allowing short-run volatility to respond to current information.
arXiv
- 30 Oct 20262fanfare
Fitting Dynamic Conditional Densities to Cross-Sectional Returns with Persistent Tail Intensities
Spline-mixture models detect persistent left and right tail dynamics in equity returns independent of volatility, improving one-percent quantile loss forecasts in most panels tested.
arXiv
- 30 Oct 20263fanfare
What do Earnings Prediction Markets Know?
Polymarket earnings contracts incrementally improve forecasts by incorporating public information missed by analysts, though only in the final trading day before announcements.
SSRN
- 30 Oct 20262fanfare
Official Inflation Information and Household Expectations in High-Inflation Türkiye
Türkiye survey respondents shown the official 30.6-32.6% inflation rate lower their expectations by 3.2 percentage points, though the effect is selective by age and political affiliation.
SSRN
- 30 Oct 20264fanfare
150 years of U.S. inflation expectations
A narrative intensity measure from New York Times articles since 1870 predicts inflation better than commodity-based measures and reveals that the 1930s deflation was anticipated earlier than consensus suggests.
RePEc
- 30 Oct 20263fanfare
DSGE Models in Central Banks: Progress and Prospects
A comprehensive review traces how DSGE models became standard policy tools and assesses their value for connecting theory, data and policy experiments despite known limitations.
RePEc
- 30 Oct 20263fanfare
Financial Inclusion: A Meta-analytic Evidence from Global Research
Meta-analysis of 5,172 estimates finds robust evidence for technology and infrastructure as drivers of financial inclusion, while publication bias distorts reported effects across studies.
RePEc
- 30 Oct 20262fanfare
Energy Supply Shocks and Inflation Expectations: A Functional Var Approach
Using German survey data, the paper finds that oil and gas supply shocks shift inflation expectations rightward and increase disagreement, with households overestimating inflationary effects.
RePEc
- 30 Oct 20262fanfare
Assessing Monetary Policy Globally: Evidence from LLMs and Semi-structural Models
The paper employs large language models and semi-structural models to evaluate central bank communication and its role in shaping policy expectations.
RePEc
- 30 Oct 20262fanfare
State-dependent pricing, de-anchored expectations, and the cost of looking through inflation shocks
The research finds that price-adjustment behavior and de-anchored household expectations interact super-additively to raise inflation costs, compounding risks when both channels activate.
RePEc
- 23 Oct 20260cites
Deep-$π^*$: A Neural Network Estimate of Trend Inflation
Deep-π* attributes trend inflation movements to macroeconomic indicators via neural networks, forecasting inflation more accurately than classical models and signaling the 2021 surge earlier.
arXiv
- 23 Oct 20263fanfare
Sealed Before the Open: A Verifiable Standard for Measuring AI Forecasts of Financial Markets
A transparency standard with cryptographic timestamping allows independent verification of AI forecasts before market opens, making backtests tamper-proof and comparable across models.
SSRN
- 23 Oct 20263fanfare
Do Markets Dream of Artificial Intelligence? Macroeconomic Effects and International Spillovers of AI News Shocks
The research identifies AI news shocks from equity factors and shows they trigger investment-led US growth but create monetary dilemmas via imported inflation in the euro area.
RePEc
- 23 Oct 20263fanfare
Subjective Models of the Macroeconomy and the Transmission of Monetary Policy
Evidence from a multi-country survey shows that heterogeneous beliefs about how monetary policy works significantly alter households' consumption responses to rate changes.
RePEc