|
on Econometric Time Series |
|
Issue of 2025–07–14
23 papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Christian Gourieroux; Quinlan Lee |
| Abstract: | We explore the issues of identification for nonlinear Impulse Response Functions in nonlinear dynamic models and discuss the settings in which the problem can be mitigated. In particular, we introduce the nonlinear autoregressive representation with Gaussian innovations and characterize the identified set. This set arises from the multiplicity of nonlinear innovations and transformations which leave invariant the standard normal density. We then discuss possible identifying restrictions, such as non-Gaussianity of independent sources, or identifiable parameters by means of learning algorithms, and the possibility of identification in nonlinear dynamic factor models when the underlying latent factors have different dynamics. We also explain how these identification results depend ultimately on the set of series under consideration. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13531 |
| By: | Yasumasa Matsuda; Rei Iwafuchi |
| Abstract: | This paper proposes a novel framework for modeling time series of probability density functions by extending auto-regressive moving average(ARMA) models to density-valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0, 1]d is approximated by a B-spline mixture representation. Through generalized logit and softmax mappings, the space of density functions is transformed into an unconstrained Euclidean space, enabling the application of classical time series techniques. We define ARMA-type dynamics in the transformed space. Estimation is carried out via least squares for density-valued AR models and Whittle likelihood for ARMA models, with asymptotic normality derived under the joint divergence of the time horizon and basis dimension. The proposed methodology is applied to spatio-temporal human population data in Tokyo, where meaningful temporal structures in the distributional dynamics are successfully captured. |
| Date: | 2025–06–23 |
| URL: | https://d.repec.org/n?u=RePEc:toh:dssraa:146 |
| By: | David Kohns; Tibor Szendrei |
| Abstract: | Crossing of fitted conditional quantiles is a prevalent problem for quantile regression models. We propose a new Bayesian modelling framework that penalises multiple quantile regression functions toward the desired non-crossing space. We achieve this by estimating multiple quantiles jointly with a prior on variation across quantiles, a fused shrinkage prior with quantile adaptivity. The posterior is derived from a decision-theoretic general Bayes perspective, whose form yields a natural state-space interpretation aligned with Time-Varying Parameter (TVP) models. Taken together our approach leads to a Quantile- Varying Parameter (QVP) model, for which we develop efficient sampling algorithms. We demonstrate that our proposed modelling framework provides superior parameter recovery and predictive performance compared to competing Bayesian and frequentist quantile regression estimators in simulated experiments and a real-data application to multivariate quantile estimation in macroeconomics. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13257 |
| By: | Jin Seo Cho (Yonsei University) |
| Abstract: | The current study provides the Gaussian versions used to test for normal mixtures. These versions are highly practical as they can directly be used to simulate the asymptotic critical values of standard tests, for example the likelihood-ratio or Lagrange multiplier tests. We investigate testing for two normal mixtures: one having a single variance and two distinct means, and another having a single mean and two different variances. We derive the Gaussian versions for the two models by associating the score functions with the Hermite and generalized Laguerre polynomials, respectively. Additionally, we compare the performance of the likelihood-ratio and Lagrange multiplier tests using the asymptotic critical values. |
| Keywords: | Gaussian version; LR test; LM test; Hermite polynomial; Generalized Laguerre polynomial. |
| JEL: | C12 C46 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-248 |
| By: | NEIFAR, MALIKA; Gharbi, Leila |
| Abstract: | Purpose The scope of this paper is to investigate if the information and communications technology (ICT) can improve the FinTech firm performance in the BRICS countries from monthly macro time series data during 2014M01-2022M12. Design/methodology/approach Through the Bayesian VAR-X approach and the time series DYNARDL simulation models, we investigate the impact of the ICT and its components on the firm performance for both the short-run (SR) and the long-run (LR) historical and predictive trend. Besides these regression models, this study applies the Granger Causality (GC) in quantile and the frequency domain (FD) GC tests to show more details about the causality linkage. Findings From the BVAR-X approach, historical IRFs conclude that the ICT has positive effect on PI for all countries in the SR and a positive effect in the LR only for China. From the DYNARDL simulation models, predictive IRFs results corroborate with the historical IRFs results except for the China and SA in the SR and for Brazil and India in the LR. We conclude in addition that the predictive positive relationships is driven by MCS for Brazil, IUI for China, FBS for SA, and all of the ICT components for the India case. GC type test results are in accordance with previous results. Originality The novelty of this research is based on the idea of studying the effect of the ICT on FinTech firm performance by using several time series data based dynamic technics so that we can estimate and predict the SR adjustments that arise from the impact of ICT to the LR relationship with the firm profitability. |
| Keywords: | FinTech Firm from BRICS area; Bayesian VAR-X model; DYNARDL simulation model; Historical and predictive IRFs for SR and LR effects; Granger Causality test in quantile (QGC); Frequency domain Granger causality (FDC) test |
| JEL: | C01 C11 C22 C53 D22 |
| Date: | 2025–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123778 |
| By: | Carlos Segura-Rodriguez (Departamento Investigación Económica, Banco Central de Costa Rica) |
| Abstract: | This study presents a methodology for forecasting inflation in Costa Rica using a FAVAR model that combines data from 156 relevant time series. This approach consists of two stages: first, static and dynamic factors are estimated, which are then incorporated into a VAR model along with monthly inflation to project the annual variation of the Consumer Price Index. Automatic selection criteria are employed to choose which variables to include in the factors and to determine the number of factors, lags, and restrictions on the coefficients of the VAR model. Eight inflation forecasts are generated and combined using three averages: simple, inverse mean squared error weighted, and Bayesian. The results indicate that the Bayesian forecast is the most accurate for the period between 2021 and 2023, outperforming even the most accurate of traditional VAR models that consider only inflation and individually any of the 156 variables. This suggests that the FAVAR model can effectively integrate information from available variables without requiring prior knowledge of which ones are most relevant. |
| Keywords: | Inflation; Forecasting; Dynamic Factors; FAVAR |
| JEL: | E32 R10 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:apk:doctra:2403 |
| By: | Doko Tchatoka, Firmin; Wang, Wenjie |
| Abstract: | Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares or IV-based method is appropriate. Guggenberger (2010a) shows that the second-stage test - based on the outcome of a Durbin-Wu-Hausman type pretest for exogeneity in the first stage - has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used, even under strong identification and conditional homoskedasticity. In this paper, we make the following contributions. First, we show that both conditional and unconditional on the data, standard wild bootstrap procedures are invalid for the two-stage testing and therefore are not viable solutions to such size-distortion problem. Second, we propose an identification-robust two-stage test statistic that switches between the OLS-based and the weak-IV-robust statistics. Third, we develop a size-adjusted wild bootstrap approach for our two-stage test that integrates specific wild bootstrap critical values with an appropriate size-adjustment method. We establish uniform validity of this procedure under conditional heteroskedasticity or clustering in the sense that the resulting tests achieve correct asymptotic size no matter the identification is strong or weak. |
| Keywords: | DWH Pretest; Shrinkage; Instrumental Variable; Asymptotic Size; Wild Bootstrap; Bonferroni-based Size-correction; Clustering. |
| JEL: | C12 C21 C26 |
| Date: | 2025–05–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125017 |
| By: | Li, Mengheng; Mendieta-Munoz, Ivan |
| Abstract: | We propose a factor correlated unobserved components (FCUC) model to analyze the sticky and flexible components of U.S. inflation. The proposed FCUC framework estimates trend inflation and component cycles in a flexible stochastic environment with time-varying volatility, factor loadings, and cross-frequency (trend-cycle) correlations, thus capturing how structural heterogeneity in price adjustment shapes the evolution of aggregate trend inflation over time. Using Bayesian estimation methods, we show that the FCUC model substantially reduces the uncertainty surrounding estimates of trend inflation and improves both point and density forecast accuracy. Our findings reveal that, particularly following the Global Financial Crisis and more markedly since the COVID-19 recession, transitory price shocks originating from flexible inflation have become a major driver of trend inflation, whereas sticky inflation explains only part of the variation. These results indicate that temporary price movements can have persistent effects, highlighting important policy implications regarding the cyclical dynamics of disaggregated inflation components amid evolving macroeconomic conditions. |
| Keywords: | trend inflation, sticky inflation, flexible inflation, stochastic volatility, dynamic factor model |
| JEL: | C32 C53 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:320299 |
| By: | Irma Alonso-Álvarez (BANCO DE ESPAÑA); Daniel Santabárbara (BANCO DE ESPAÑA) |
| Abstract: | In this paper, we present a straightforward structural model of the oil market designed to disentangle demand and supply shocks. This model is regularly employed and updated in the Banco de España to enhance the understanding of oil market dynamics. Building on the work of Kilian and Murphy (2014), we introduce a novel business cycle measure based on the co-movement of real commodity prices to capture global demand shocks, and also include an oil-specific demand shock. Our impulse response functions and historical decomposition align with previous studies and effectively capture significant historical milestones. |
| Keywords: | oil structural model, supply, demand, global real activity, oil-specific demand, VAR, sign restrictions. |
| JEL: | Q41 Q43 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:bde:opaper:2513e |
| By: | Jin Seo Cho (Yonsei University) |
| Abstract: | The current study investigates testing the mixture hypothesis of Poisson regression models using the likelihood ratio (LR) test. The motivation of the mixture hypothesis stems from the unobserved heterogeneity, and the null hypothesis of interest is that there is no unobserved heterogeneity in the data. Due to the nonstandard conditions described in the text, the LR test does not weakly converge to the standard chi-squared random variable under the null hypothesis. We derive its null limit distribution as a functional of the Hermite Gaussian process. Furthermore, we introduce a methodology to obtain the asymptotic critical values consistently. Finally, we conduct Monte Carlo experiments and compare the power of the LR test with the specification test developed by Lee (1986). |
| Keywords: | Mixture of Poisson Regression Models; Likelihood Ratio Test; Asymptotic Null Distribution; Gaussian Process. |
| JEL: | C12 C22 C32 C52 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-254 |
| By: | Marco Zanotti |
| Abstract: | Given the continuous increase in dataset sizes and the complexity of forecasting models, the tradeoff between forecast accuracy and computational cost is emerging as an extremely relevant topic, especially in the context of ensemble learning for time series forecasting. To asses it, we evaluated ten base models and eight ensemble configurations across two large-scale retail datasets (M5 and VN1), considering both point and probabilistic accuracy under varying retraining frequencies. We showed that ensembles consistently improve forecasting performance, particularly in probabilistic settings. However, these gains come at a substantial computational cost, especially for larger, accuracy-driven ensembles. We found that reducing retraining frequency significantly lowers costs, with minimal impact on accuracy, particularly for point forecasts. Moreover, efficiency-driven ensembles offer a strong balance, achieving competitive accuracy with considerably lower costs compared to accuracy-optimized combinations. Most importantly, small ensembles of two or three models are often sufficient to achieve near-optimal results. These findings provide practical guidelines for deploying scalable and cost-efficient forecasting systems, supporting the broader goals of sustainable AI in forecasting. Overall, this work shows that careful ensemble design and retraining strategy selection can yield accurate, robust, and cost-effective forecasts suitable for real-world applications. |
| Keywords: | Time series, Demand forecasting, Forecasting competitions, Cross-learning, Global models, Forecast combinations, Ensemble learning, Machine learning, Deep learning, Conformal predictions, Green AI. |
| JEL: | C53 C52 C55 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:mib:wpaper:554 |
| By: | Astill, Sam; Magdalinos, Tassos; Taylor, AM Robert |
| Abstract: | We address the sensitivity of asset return predictability tests to the initial conditions of predictors. The IVX test of Kostakis et al. (2015, Review of Financial Studies) assumes asymptotically negligible initial conditions, which we show can result in large power losses for strongly persistent predictors. We propose a modified test that initialises the instruments at estimates of the predictors’ initial conditions, enhancing robustness and detection power. Additionally, a hybrid test is introduced, combining the strengths of the original and modified tests to deliver robust performance across varying magnitude initial conditions. Empirical and simulation results demonstrate the effectiveness of these approaches in improving predictability testing. |
| Keywords: | predictive regression; returns; initial condition; unknown regressor persistence; instrumental variable; hybrid tests |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:esy:uefcwp:41209 |
| By: | Victor Chernozhukov; Christian Hansen; Lingwei Kong; Weining Wang |
| Abstract: | Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, verify that quasi-posteriors can be used to obtain approximately optimal Bayesian decision rules under the maintained prior structure over misspecification, and provide a form of frequentist coverage results. We illustrate the approach through empirical examples where we obtain informative inference for structural objects allowing for substantial relaxations of the requirement that moment conditions hold exactly. |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:14/25 |
| By: | Brignone , Davide (Bank of England); Mazzali, Marco (University of Bologna) |
| Abstract: | What drives business cycle fluctuations in the euro area (EA)? To answer this question, we build a rich, high-dimensional dataset of quarterly time series covering both EA aggregates and its major member countries. We find that just two shocks account for the bulk of the EA’s cyclical dynamics, and that they map cleanly onto standard demand and supply disturbances, consistent with textbook macroeconomic theory. Beyond this aggregate result, we uncover a high degree of synchronization in how member states respond to these shocks, highlighting the presence of a shared underlying cycle across the region. We also provide a historical decomposition of key EA macro variables based on the identified demand and supply components, with a particular focus on the recent inflation surge. Our findings show that supply-side factors dominated the initial phase of inflation through mid-2022, while demand-side pressures intensified and became increasingly important from mid-2022 onward. |
| Keywords: | Business cycle; identification; frequency domain; euro area economy; dynamic factors; inflation. |
| JEL: | C38 E32 |
| Date: | 2025–04–25 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1124 |
| By: | Daniel H. Cooper; Giovanni P. Olivei; Hannah Rhodenhiser |
| Abstract: | We provide a parsimonious setup for forecasting U.S. GDP growth and the unemployment rate based on a few fundamental drivers. This setup yields forecasts that are reasonably accurate compared with private-sector and Federal Reserve forecasts over the 1984–2019 and post COVID-19 pandemic periods. This result is achieved by jointly estimating the processes for GDP growth and the unemployment rate, with the constraint that GDP and unemployment follow Okun’s law in first differences. This setup can be easily extended to replace the variables in the information set with factors that might better capture the underlying fundamentals. |
| Keywords: | macroeconomic forecasting; small information set; forecast accuracy |
| JEL: | E27 E37 |
| Date: | 2025–06–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:101183 |
| By: | Dhanashree Somani (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Sayar Karmakar (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, Greece) |
| Abstract: | The objective of this paper is to forecast volatilities of the stock returns of China, France, Germany, Italy, Spain, the United Kingdom (UK), and the United States (US) over the daily period of January 2010 to February 2025 by utilizing the information content of newspapers articles-based indexes of supply bottlenecks. We measure volatility by employing the interquantile range, estimated via an asymmetric slope autoregressive quantile regression fitted on stock returns to derive the conditional quantiles. In the process, we are also able to obtain estimates of skewness, kurtosis, lower- and upper-tail risks, and incorporate them into our linear predictive model, alongside leverage. Based on the shrinkage estimation using the Lasso estimator to control for overparameterization, we find that the model with moments outperform the benchmark model that includes both own- and cross-country volatilities, but the performance of the former, is improved further when we incorporate the role of the metrics of supply constraints for all the 7 countries simultaneously. These findings carry significant implications for investors. |
| Keywords: | Supply Bottlenecks, Stock Market Volatility, Asymmetric Autoregressive Quantile Regression, Lasso Estimator, Forecasting |
| JEL: | C22 C53 E23 G15 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202521 |
| By: | Marco Zanotti |
| Abstract: | Forecast stability, that is the consistency of predictions over time, is essential in business settings where sudden shifts in forecasts can disrupt planning and erode trust in predictive systems. Despite its importance, stability is often overlooked in favor of accuracy, particularly in global forecasting models. In this study, we evaluate the stability of point and probabilistic forecasts across different retraining frequencies and ensemble strategies using two large retail datasets (M5 and VN1). To do this, we introduce a new metric for probabilistic stability (MQC) and analyze ten different global models and four ensemble configurations. The results show that less frequent retraining not only preserves but often improves forecast stability, while ensembles, especially those combining diverse pool of models, further enhance consistency without sacrificing accuracy. These findings challenge the need for continuous retraining and highlight ensemble diversity as a key factor in reducing forecast stability. The study promotes a shift toward stability-aware forecasting practices, offering practical guidelines for building more robust and sustainable prediction systems. |
| Keywords: | Time series, Demand forecasting, Forecasting competitions, Cross-learning, Global models, Forecast stability, Vertical stability, Machine learning, Deep learning, Conformal predictions. |
| JEL: | C53 C52 C55 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:mib:wpaper:553 |
| By: | Yuki Murakami (Graduate School of Economics, Waseda University) |
| Abstract: | This paper focuses on the time-varying volatility of aggregate fluctuations in emerging markets. Both Latin American and Asian emerging economies experience volatility spikes during financial crises; however, only the latter group exhibits a long-run decline in volatility. Using business cycle data from South Korea, we estimate a small open economy real business cycle model with Markov-switching shock variances. We compare the model fit across alternative specifications of shock volatility structures and investigate the underlying drivers of volatility changes. The results indicate that the data favor the model in which all shock variances switch regimes synchronously. The estimated model captures both the declining trend in volatility over time and temporary volatility spikes during episodes of financial turmoil. It suggests that the long-run decline in volatility is not primarily driven by a reduction in the variance of the interest rate premium shock, though this shock contributes to temporary volatility spikes during crises. The model replicates key business cycle features of emerging markets and highlights that the drivers of aggregate fluctuations depend on the volatility regime. |
| Keywords: | Small open economy; real business cycles; regime switching |
| JEL: | E32 F41 C13 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:wap:wpaper:2514 |
| By: | Guglielmo Maria Caporale; Luis Alberiko Gil-Alana; Nieves Carmona-González |
| Abstract: | This paper analyses trends and persistence in atmospheric pollution in ten US cities over the period from January 2014 to January 2024 using fractional integration methods. The results support the hypothesis of long memory and mean reversion in atmospheric pollution in all cities examined. They also indicate that Boston is the only city in the sample where atmospheric pollution exhibits a significant positive linear trend, though it is also characterised by the lowest degree of integration, which implies that shocks have transitory effects and mean reversion occurs at a fast rate. |
| Keywords: | atmospheric pollution, particular matter (PM2.5), fractional integration, long memory, persistence |
| JEL: | C22 Q53 Q58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11957 |
| By: | Yuliya Mishura; Andrey Pilipenko; Kostiantyn Ralchenko |
| Abstract: | We investigate the Gatheral model of double mean-reverting stochastic volatility, in which the drift term itself follows a mean-reverting process, and the overall model exhibits mean-reverting behavior. We demonstrate that such processes can attain values arbitrarily close to zero and remain near zero for extended periods, making them practically and statistically indistinguishable from zero. To address this issue, we propose a modified model incorporating Skorokhod reflection, which preserves the model's flexibility while preventing volatility from approaching zero. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.09184 |
| By: | Michael Balzer |
| Abstract: | Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatial regression models have been introduced. In this article, a novel model-based gradient boosting algorithm for spatial regression models with autoregressive disturbances is proposed. Due to the modular nature, the approach provides an alternative estimation procedure which is feasible even in high-dimensional settings where established quasi-maximum likelihood or generalized method of moments estimators do not yield unique solutions. The approach additionally enables data-driven variable and model selection in low- as well as high-dimensional settings. Since the bias-variance trade-off is also controlled in the algorithm, implicit regularization is imposed which improves prediction accuracy on out-of-sample spatial data. Detailed simulation studies regarding the performance of estimation, prediction and variable selection in low- and high-dimensional settings confirm proper functionality of the proposed methodology. To illustrative the functionality of the model-based gradient boosting algorithm, a case study is presented where the life expectancy in German districts is modeled incorporating a potential spatial dependence structure. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13682 |
| By: | Garabedian, Garo (Central Bank of Ireland) |
| Abstract: | We disentangle macroeconomic surprises in a structural Bayesian VAR, and show that common measures of the short-term neutral rate underreact to shocks that affect the near term productive capacity of the economy. In contrast, these measures overreact to transitory demand shocks, such as monetary policy shocks. Their impact is persistent, making short term shocks hard to distinguish from secular trends. Our findings are robust across a large array of r-star measures. Particularly when the economy is near the effective lower bound, expansionary monetary policy has a forceful downwards impact on r-star. Hence, the neutral rate is not exogenous as in the Neo-Wicksellian paradigm. For our main analysis, we extend the Holston-Labauch-Williams estimate back to the 1920s, thus revealing a non-monotonic time-series. We add to the debate on the use of r-star in the policy realm, and the effectiveness of monetary policy tools when rates are low. |
| Keywords: | Equilibrium real interest rate, R*, long-term rates, cyclical drivers, macroeconomic shocks, monetary policy, structural Bayesian VAR, sign restrictions. |
| JEL: | C11 E43 E52 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:cbi:wpaper:4/rt/25 |
| By: | Quinlan Lee, Stephen Snudden |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:wlu:lcerpa:jc0157 |