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on Forecasting |
| By: | Paponpat Taveeapiradeecharoen; Popkarn Arwatchanakarn |
| Abstract: | This study investigates the forecasting performance of Bayesian shrinkage priors in predicting Thai inflation in a univariate setup, with a particular interest in comparing those more advance shrinkage prior to a likelihood dominated/noninformative prior. Our forecasting exercises are evaluated using Root Mean Squared Error (RMSE), Quantile-Weighted Continuous Ranked Probability Scores (qwCRPS), and Log Predictive Likelihood (LPL). The empirical results reveal several interesting findings: SV-augmented models consistently underperform compared to their non-SV counterparts, particularly in large predictor settings. Notably, HS, DL and LASSO in large-sized model setting without SV exhibit superior performance across multiple horizons. This indicates that a broader range of predictors captures economic dynamics more effectively than modeling time-varying volatility. Furthermore, while left-tail risks (deflationary pressures) are well-controlled by advanced priors (HS, HS+, and DL), right-tail risks (inflationary surges) remain challenging to forecast accurately. The results underscore the trade-off between model complexity and forecast accuracy, with simpler models delivering more reliable predictions in both normal and crisis periods (e.g., the COVID-19 pandemic). This study contributes to the literature by highlighting the limitations of SV models in high-dimensional environments and advocating for a balanced approach that combines advanced shrinkage techniques with broad predictor coverage. These insights are crucial for policymakers and researchers aiming to enhance the precision of inflation forecasts in emerging economies. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05334 |
| By: | Heinisch, Katja; Scaramella, Fabio; Schult, Christoph |
| Abstract: | Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts. |
| Keywords: | accuracy, external assumptions, forecasts, forecast errors, machine learning |
| JEL: | C14 C53 E02 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iwhdps:318189 |
| By: | Shovon Sengupta (SUAD_SAFIR - SUAD - Sorbonne University Abu Dhabi, BITS Pilani - Birla Institute of Technology and Science, Fidelity Investments); Tanujit Chakraborty (SUAD_SAFIR - SUAD - Sorbonne University Abu Dhabi); Sunny Kumar Singh (BITS Pilani - Birla Institute of Technology and Science) |
| Abstract: | Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into high- and low-frequency components using wavelet transforms and incorporates additional economic factors, such as economic policy uncertainty and geopolitical risk, to enhance forecast accuracy. These wavelet-transformed series and filtered exogenous variables are input into downstream autoregressive neural networks, producing the final ensemble forecast. Theoretically, we demonstrate that FEWNet reduces empirical risk compared to fully connected autoregressive neural networks. Empirically, FEWNet outperforms other forecasting methods and effectively estimates prediction uncertainty due to its ability to capture non-linearities and long-range dependencies through its adaptable architecture. Consequently, FEWNet emerges as a valuable tool for central banks to manage inflation and enhance monetary policy decisions. |
| Keywords: | Inflation forecasting Wavelets Neural networks Empirical risk minimization Conformal prediction intervals |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05056934 |
| By: | Sona Benecka |
| Abstract: | This paper proposes a novel framework to the forecast of disaggregated producer prices using both machine learning techniques and traditional econometric models. Due to the complexity and diversity of pricing dynamics within the euro area, no single model consistently outperforms others across all sectors. This highlights the necessity for a tailored approach that leverages the strengths of various forecasting methods to effectively capture the unique characteristics of each sector. Our forecasting exercise has highlighted diverse pricing strategies linked to commodity prices, autoregressive behavior, or a mixture of both, with pipeline pressures being especially pertinent to final goods. Employing a mixture of a wide range of models has proven to be a successful strategy in managing the varied pricing behavior at the sectoral level. Notably, tree-based methods, like Random Forests or XGBoost, have shown significant efficacy in forecasting short-term PPI inflation across a number of sectors, especially when accounting for pipeline pressures. Moreover, newly proposed Hybrid ARMAX models proved to be a suitable alternative for sectors tightly linked to commodity prices. |
| Keywords: | Disaggregated producer prices, forecasting, inflation, machine learning |
| JEL: | C22 C52 C53 E17 E31 E37 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:cnb:wpaper:2025/2 |
| By: | Rincón Briceño, Juan José (Universidad de los Andes) |
| Abstract: | Economic decisions are made with high uncertainty about the current and recent past economic activity, due to the limited and imperfect available information. Therefore the following question arises: how can the accuracy of Colombian economic activity nowcasting be enhanced compared to traditional forecasting methods? This paper demonstrates: (a) using a risk-averse customized loss function that accounts for the agent disutility and penalizes directional discrepancies provides a useful alternative for assessing model performance by ensuring more accurate nowcasts, maximizing both precision and economic relevance. And (b) during periods of abrupt shocks and high volatility, such as the COVID-19 (2020–2021) and the post COVID-19 subsequent years (2022-2023), machine learning models outperform traditional nowcasting models |
| Keywords: | Colombian economic activity; nowcast; forecast; Random forests; LSTM. |
| JEL: | C45 C52 C53 E32 E37 |
| Date: | 2025–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:col:000089:021388 |
| By: | Elliot Beck; Michael Wolf |
| Abstract: | Accurately forecasting inflation is critical for economic policy, financial markets, and broader societal stability. In recent years, machine learning methods have shown great potential for improving the accuracy of inflation forecasts; specifically, the random forest stands out as a particularly effective approach that consistently outperforms traditional benchmark models in empirical studies. Building on this foundation, this paper adapts the hedged random forest (HRF) framework of Beck et al. (2024) for the task of forecasting inflation. Unlike the standard random forest, the HRF employs non-equal (and even negative) weights of the individual trees, which are designed to improve forecasting accuracy. We develop estimators of the HRF's two inputs, the mean and the covariance matrix of the errors corresponding to the individual trees, that are customized for the task at hand. An extensive empirical analysis demonstrates that the proposed approach consistently outperforms the standard random forest. |
| Keywords: | Exponentially weighted moving average, Linear shrinkage, Machine learning |
| JEL: | C21 C53 C31 E47 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2025-07 |
| By: | Philippe Goulet Coulombe (University of Quebec in Montreal); Massimiliano Marcellino (Bocconi University); Dalibor Stevanovic (University of Quebec in Montreal) |
| Abstract: | We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models are better suited to handle crosssectional nonlinearities. Results show that predictive improvements are broad-based and that even a few high-frequency state indicators contribute substantially to forecast accuracy. Our findings highlight the complementarity between flexible modeling and cross sectional pooling, making panel neural networks a powerful tool for timely and accurate fiscal monitoring in heterogeneous settings. |
| Keywords: | Machine learning, Nowcasting, Panel, Mixed-frequency, Fiscal indicators |
| JEL: | C53 C55 E37 H72 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bbh:wpaper:25-04 |
| By: | Kyungsu Kim |
| Abstract: | In this paper, I explored how a range of regression and machine learning techniques can be applied to monthly U.S. unemployment data to produce timely forecasts. I compared seven models: Linear Regression, SGDRegressor, Random Forest, XGBoost, CatBoost, Support Vector Regression, and an LSTM network, training each on a historical span of data and then evaluating on a later hold-out period. Input features include macro indicators (GDP growth, CPI), labor market measures (job openings, initial claims), financial variables (interest rates, equity indices), and consumer sentiment. I tuned model hyperparameters via cross-validation and assessed performance with standard error metrics and the ability to predict the correct unemployment direction. Across the board, tree-based ensembles (and CatBoost in particular) deliver noticeably better forecasts than simple linear approaches, while the LSTM captures underlying temporal patterns more effectively than other nonlinear methods. SVR and SGDRegressor yield modest gains over standard regression but don't match the consistency of the ensemble and deep-learning models. Interpretability tools , feature importance rankings and SHAP values, point to job openings and consumer sentiment as the most influential predictors across all methods. By directly comparing linear, ensemble, and deep-learning approaches on the same dataset, our study shows how modern machine-learning techniques can enhance real-time unemployment forecasting, offering economists and policymakers richer insights into labor market trends. In the comparative evaluation of the models, I employed a dataset comprising thirty distinct features over the period from January 2020 through December 2024. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01933 |
| By: | Lutfu Sua; Haibo Wang; Jun Huang |
| Abstract: | Unpredictability of renewable energy sources coupled with the complexity of those methods used for various purposes in this area calls for the development of robust methods such as DL models within the renewable energy domain. Given the nonlinear relationships among variables in renewable energy datasets, DL models are preferred over traditional machine learning (ML) models because they can effectively capture and model complex interactions between variables. This research aims to identify the factors responsible for the accuracy of DL techniques, such as sampling, stationarity, linearity, and hyperparameter optimization for different algorithms. The proposed DL framework compares various methods and alternative training/test ratios. Seven ML methods, such as Long-Short Term Memory (LSTM), Stacked LSTM, Convolutional Neural Network (CNN), CNN-LSTM, Deep Neural Network (DNN), Multilayer Perceptron (MLP), and Encoder-Decoder (ED), were evaluated on two different datasets. The first dataset contains the weather and power generation data. It encompasses two distinct datasets, hourly energy demand data and hourly weather data in Spain, while the second dataset includes power output generated by the photovoltaic panels at 12 locations. This study deploys regularization approaches, including early stopping, neuron dropping, and L2 regularization, to reduce the overfitting problem associated with DL models. The LSTM and MLP models show superior performance. Their validation data exhibit exceptionally low root mean square error values. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.03109 |
| By: | Mohammadhossein Rashidi; Mohammad Modarres |
| Abstract: | Predicting the price that has the least error and can provide the best and highest accuracy has been one of the most challenging issues and one of the most critical concerns among capital market activists and researchers. Therefore, a model that can solve problems and provide results with high accuracy is one of the topics of interest among researchers. In this project, using time series prediction models such as ARIMA to estimate the price, variables, and indicators related to technical analysis show the behavior of traders involved in involving psychological factors for the model. By linking all of these variables to stepwise regression, we identify the best variables influencing the prediction of the variable. Finally, we enter the selected variables as inputs to the artificial neural network. In other words, we want to call this whole prediction process the "ARIMA_Stepwise Regression_Neural Network" model and try to predict the price of gold in international financial markets. This approach is expected to be able to be used to predict the types of stocks, commodities, currency pairs, financial market indicators, and other items used in local and international financial markets. Moreover, a comparison between the results of this method and time series methods is also expressed. Finally, based on the results, it can be seen that the resulting hybrid model has the highest accuracy compared to the time series method, regression, and stepwise regression. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01402 |
| By: | Nurbanu Bursa |
| Abstract: | Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and T\"urkiye), are gaining influence in global stock markets, although they remain susceptible to the economic conditions of developed countries like the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This interconnectedness and sensitivity of financial markets make understanding these relationships crucial for investors and policymakers to predict stock price movements accurately. To this end, we examined the main stock market indices of G7 and MINT countries from 2012 to 2024, using a recent graph neural network (GNN) algorithm called multivariate time series forecasting with graph neural network (MTGNN). This method allows for considering complex spatio-temporal connections in multivariate time series. In the implementations, MTGNN revealed that the US and Canada are the most influential G7 countries regarding stock indices in the forecasting process, and Indonesia and T\"urkiye are the most influential MINT countries. Additionally, our results showed that MTGNN outperformed traditional methods in forecasting the prices of stock market indices for MINT and G7 countries. Consequently, the study offers valuable insights into economic blocks' markets and presents a compelling empirical approach to analyzing global stock market dynamics using MTGNN. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.01945 |
| By: | Fourie, Jurgens; Steenkamp, Daan |
| Abstract: | We identify South African business cycles using the algorithm of Bry-Boschan and show that the identified turning points are very similar to those from other approaches. We demonstrate that South Africa has a very volatile business cycle that makes it particularly difficult to predict turning points in the economic cycle. South Africa’s business cycle is characterised by relatively long downswings and short upswing phases with low amplitude. We find that the South African Reserve Bank (SARB)’s Leading Indicator does not substantive improve predictions of the business cycle relative to GDP itself. We assess the performance of a range of potential leading indicators in identifying economic downturns and consider whether alternative indicators and estimation approaches can produce better predictions than those of the SARB. We demonstrate that using a larger information set produces substantially better business cycle predictions, especially when using machine learning techniques. Our findings have implications for the creation of composite leading indicators, with our results suggesting that many of the macroeconomic variables considered by analysts as leading indicators do not provide good signals of GDP growth or developments in the South African business cycle. |
| Keywords: | business cycle, forecast, leading indicator, economic downturns |
| JEL: | E32 E37 |
| Date: | 2025–05–07 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124709 |
| By: | Lin, Ziqi (Rachel) |
| Abstract: | In this paper, a kernel Extreme Learning Machine (KELM) model based on vector weighted average algorithm is proposed for the prediction of national tax revenue ratio, which provides a new way of thinking and method for tax revenue prediction. By analyzing the correlation between each index and tax share, it is found that gasoline price and life expectancy are significantly positively correlated with tax share, while fertility rate and birth rate are significantly negatively correlated. The model shows excellent predictive performance on both training set and test set, with an R² of 0.995 in training set and 0.994 in test set, indicating that the model has excellent generalization ability. In addition, the root mean square error (RMSE) of the training set and the test set are 0.185 and 0.177, respectively, and the relative prediction deviation (RPD) is 14.234 and 13.178, respectively, which further verifies the high accuracy and stability of the model. Scatter plots of actual predicted versus actual values show that the model is able to accurately capture trends in tax shares with little prediction error. In summary, the optimized KELM model proposed in this paper not only has excellent performance on known data, but also has good expansion ability, and can be effectively applied to the tax share prediction of unknown data, providing a reliable tool for relevant policy making and economic analysis. The research of this paper provides a new technical path for the field of tax forecasting, which has important theoretical significance and practical value. |
| Date: | 2025–05–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:ymjw9_v1 |
| By: | Philippe Goulet Coulombe; Massimiliano Marcellino; Dalibor Stevanovic |
| Abstract: | We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models are better suited to handle cross-sectional nonlinearities. Results show that predictive improvements are broad-based and that even a few high frequency state indicators contribute substantially to forecast accuracy. Our findings highlight the complementarity between flexible modeling and cross-sectional pooling, making panel neural networks a powerful tool for timely and accurate fiscal monitoring in heterogeneous settings. Nous étudions le nowcasting des variables budgétaires des États américains à l’aide de modèles d’apprentissage automatique (machine learning) et de prédicteurs à fréquence mixte, dans un cadre en panel. Les réseaux de neurones intégrant des variables continues et des identifiants catégoriels surpassent systématiquement les alternatives linéaires, en particulier lorsqu’ils sont combinés à des structures en panel mutualisé. Ces architectures permettent de capter les différences entre les États tout en tirant parti des régularités partagées. Les gains de prévision sont particulièrement importants pour les variables volatiles comme les dépenses et les déficits. Le regroupement des données améliore la stabilité des prévisions, et les modèles d’apprentissage automatique sont mieux adaptés pour traiter les non-linéarités transversales. Les résultats montrent que les améliorations prédictives sont généralisées et que même quelques indicateurs infranuels spécifiques aux États contribuent de manière significative à la précision des prévisions. Nos résultats soulignent la complémentarité entre la modélisation flexible et le regroupement transversal, faisant des réseaux de neurones en panel un outil puissant pour un suivi budgétaire rapide et précis dans des contextes hétérogènes. |
| Keywords: | Machine learning, Nowcasting, Panel, Mixed-frequency, Fiscal indicators, Apprentissage automatique, Panel, Fréquences mixtes, Indicateurs budgétaires, Prévisions à court terme |
| JEL: | C53 C55 E37 H72 |
| Date: | 2025–05–27 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2025s-15 |
| By: | Mert Gokcu; Eren Sezer |
| Abstract: | [TR] Akaryakit fiyatlari esas olarak urun fiyati, kar marjlari, gelir payi ve vergilerin toplamindan olusmaktadir. Nihai akaryakit fiyatlarinin olusumunda urun fiyati ve vergi kalemleri on plana cikmaktadir. Tarihsel olarak, vergi kaleminin nihai fiyat icindeki payinin gerilemesi ve urun fiyatinin payinin artmasi akaryakit fiyatlari acisindan urun fiyat gelismelerinin daha yakindan incelenmesi ihtiyacini dogurmaktadir. Nitekim, maliyet ve beklenti kanallariyla tuketici enflasyonunu etkileme potansiyeline sahip akaryakit fiyatlarinin tahmin modellerinde dogru degiskenler ile temsil edilmesi oldukca onemlidir. Bu calisma, oncelikle ulkemizdeki akaryakit fiyat olusumu hakkinda bilgi saglamaktadir. Ayrica, akaryakit fiyat tahmin modellerinde her akaryakit grubuna ozgu urun fiyatlarinin kullanilmasinin Brent petrol fiyatlarinin kullanimindan daha fazla bilgi icerigi olup olmadigini ekonometrik ve istatistiksel yontemler ile test etmektedir. Aciklama gucu ile birlikte orneklem disi tahmin performanslari dikkate alindiginda model sonuclari akaryakit fiyatlarini aciklamada Brent petrol yerine her akaryakit grubuna ozgu urun fiyatlarinin modellerde kullanilmasinin ilave bir bilgi sagladigina isaret etmektedir. Dolayisiyla, akaryakit urunlerinin fiyatlarina yonelik ongoruler olusturulurken her akaryakit grubuna ozgu urun fiyat gelismelerinin dikkate alinmasinin onem arz ettigi degerlendirilmektedir. [EN] Fuel prices are essentially the sum of product price, profit margins, portion of income and taxes. Product price and tax items come to the fore in the formation of final fuel prices. Historically, the decline in the share of taxes in the final price and the increase in the share of product price in fuel price formation necessitate a closer analysis of product price developments. As a matter fact, it is crucial that fuel prices, which have the potential to affect consumer inflation through cost and expectation channels, are represented by the proper variables in forecasting models. This study provides information on fuel price formation in Türkiye. Moreover, it is tested with econometric and statistical methods whether the use of product prices specific to each fuel group in fuel price forecasting models is more informative than the use of Brent oil prices. Considering the explanatory power and out-of-sample forecasting performances, the model results indicate that the use of product prices specific to each fuel group in the models instead of Brent oil in explaining fuel prices provides additional information. Therefore, it is considered important to take into account product price developments specific to each fuel group when formulating forecasts for the prices of fuel products. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:econot:2507 |
| By: | Michael Ehrmann |
| Abstract: | Trust in the central bank is an essential ingredient for a successful conduct of monetary policy. However, for many central banks trust has recently declined, for instance in the wake of the post-pandemic inflation surge, due to large errors in central banks’ inflation forecasts, or given problems when exiting from forward guidance. The rapid, substantial and persistent erosion of trust makes it clear that trust needs to be earned continuously. This paper reviews why trust is important, what determines it and how central banks can enhance it. It also argues that it is important for central banks to improve the measurement and monitoring of trust. It ends by highlighting some future challenges for maintaining trust. |
| Keywords: | trust; credibility; reputation; central banks; monetary policy; inflation expectations |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:rba:rbaacp:acp2024-04 |
| By: | Nikolaos Giannakis (Democritus University of Thrace); Periklis Gogas (Democritus University of Thrace); Theophilos Papadimitriou (Democritus University of Thrace); Jamel Saadaoui (University Paris 8); Emmanouil Sofianos (University of Strasbourg) |
| Abstract: | This study aims to predict currency, banking, and debt crises using a dataset of 184 crisis events and 2896 non-crisis cases from 79 countries (1970-2017). We tested eight machine learning methods: Logistic Regression, KNN, SVM, Random Forest, Balanced Random Forest, Balanced Bagging Classifier, Easy Ensemble Classifier, and Gradient Boosted Trees. The Balanced Random Forest had the best performance with a 72.91% balanced accuracy, predicting 149 out of 184 crises accurately. To address machine learning’s black-box issue, we used Variable Importance Measure (VIM) and Partial Dependence Plots (PDP). International reserve holdings, inflation rate, and current account balance were key predictors. Depleting international reserves at varying inflation levels signals impending crises, supporting the buffer effects of international reserves. |
| Keywords: | Currency crises, banking crises, debt crises, international reserve holdings, inflation, machine learning, forecasting |
| JEL: | F G |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:inf:wpaper:2025.6 |
| By: | Rocío Elizondo; Julio A. Carrillo |
| Abstract: | This paper compares inflation expectations from surveys and markets across different horizons for Mexico, between 2000 and 2024. The analysis evalues descriptive statistics, bias, efficiency, discrepancy, convergence level, and response to inflation surprises. The results suggest that both expectations are generally biased, with exception of short-term market expectations. The two types of expectations are inefficient as they include past forecast errors and do not seem to incorporate all available information. The discrepancy between the two types of expectations is negatively related to the short-term interest rate across all horizons and positively related to inflation, its quadratic change, and exchange rate volatility for some horizons. Short- and medium-term expectations respond to inflation surprises, while long-term expectations do not. Finally, their estimated convergence level is more stable and with lower levels when information from both types of expectations is combined. |
| Keywords: | Survey-based inflation expectations;Market-based inflation expectations;Forecast accuracy;Bias and efficiency;Discrepancy. |
| JEL: | C12 C22 E31 E52 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bdm:wpaper:2025-07 |
| By: | Stefano Fasani; Valeria Patella; Giuseppe Pagano Giorgianni; Lorenza Rossi |
| Abstract: | This paper investigates the macroeconomic effects of a belief distortion shock—an unexpected increase in the wedge between household and professional forecaster inflation expectations. Using survey and macro data alongside machine-learning techniques, we identify this shock and examine its effects within and outside the ZLB, while conditioning on the degree of inflation disagreement. The shock increases unemployment during normal times, whereas it reduces it in the ZLB, when the monetary stance is accommodative. Inflation disagreement instead dampens the expansionary effects of the shock. A New Keynesian model with belief distortion shocks replicates these dynamics and reproduces the inflation disagreement empirical patterns. |
| Keywords: | Inflation, Belief Distortion Shock, Inflation Disagreement, Households Expectation, Machine Learning, Local Projections, New Keynesian model, Monetary Policy, ZLB |
| JEL: | E31 C22 D84 C32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:lan:wpaper:423478673 |
| By: | Brooke Hathhorn; Michael T. Owyang |
| Abstract: | While recession probabilities can provide a snapshot of current economic conditions, relying on them to judge whether a recession is underway can be risky. |
| Keywords: | recession forecasts |
| Date: | 2025–05–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:100021 |