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on Econometric Time Series |
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Issue of 2026–02–23
eight papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Bellocca, Gian Pietro Enzo; Garrón Vedia, Ignacio; Rodríguez Caballero, Carlos Vladimir; Ruiz Ortega, Esther |
| Abstract: | The research question we answer in this paper is whether the asymptotic distribution derived by Bai (2003) for Principal Components (PC) factors in dynamic factor models (DFMs) can approximate the empirical distribution of the sequential Least Squares (SLS) estimator of global and group-specific factors in multi-level dynamic factor models (ML-DFMs). Monte Carlo experiments confirm that under general forms of the idiosyncratic covariance matrix, the finite-sample distribution of SLS global and group-specific factors can be well approximated using the asymptotic distribution of PC factors. We also analyse the performance of alternative estimators of the asymptotic mean squared error (MSE) of the SLS factors and show that the MSE estimator that allows for idiosyncratic cross-sectional correlation and accounts for estimation uncertainty of factor loadings is best. |
| Keywords: | Multi-Level Dynamic Factors Models; Principal Components; Sequential Least Squares; Subsampling |
| JEL: | C13 C32 C55 F47 |
| Date: | 2026–02–16 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:49336 |
| By: | Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E. |
| Abstract: | It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In contrast, inference improves drastically when an uninformative prior is specified directly on the concentration parameter—the key nuisance parameter capturing instrument relevance. The resulting Bayesian credible intervals are asymptotically equivalent to the frequentist confidence intervals based on conditioning approaches, and remain robust to weak instruments. JEL Classification: C11, C26, C36, C55 |
| Keywords: | Bayesian analysis, concentration parameter, weak instruments |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263189 |
| By: | Ollech, Daniel; Stefan, Martin |
| Abstract: | Official statistics increasingly make use of higher-frequency time series. But when users ultimately are interested in a seasonally adjusted temporal aggregate of these data, we have to decide whether to perform seasonal adjustment or aggregation first. Consequently, we must weigh up the benefits of richer informational content against the increased computational requirements and the challenges presented by using more volatile and outlier-prone data. We examine this trade-off on simulated and real-world time series using a battery of diagnostics including revision size, tests on residual seasonal and calendar effects and linkage with target variables using leading adjustment procedures: DSA, WSA, X-13-ARIMA, and TRAMO- SEATS. We synthesise our findings into practical guidelines that help users choose the aggregation level that balances statistical quality and real-time usefulness. |
| Keywords: | higher frequency time series, temporal aggregation, official time series |
| JEL: | C13 C14 C22 C52 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:336747 |
| By: | Qian, Jingye; Marín Díazaraque, Juan Miguel; Veiga, Helena |
| Abstract: | We develop a structural VAR with Threshold Stochastic Volatility (VAR-TSV) to study state-dependent transmission among climate conditions, energy prices, and industrial activity. The model combines volatility-in-mean effects with a threshold in log-volatility dynamics that generates discrete shifts between low- and high-volatility states, while keeping VAR propagation and contemporaneous identification unchanged across regimes. The threshold is an observed Low Economic Growth indicator that shifts the level of industrial volatility. We estimate the model in a Bayesian framework and apply it to monthly data for seven European economies (1970s to 2023, varying according to availability) using temperature anomalies, CPI inflation in energy and industrial production growth. Volatility-shock impulse responses and volatility-state-conditional connectedness reveal strong cross-country heterogeneity, with high resilience in Northern Europe, high sensitivity in Central Europe, and high persistence in Southern Europe. |
| Keywords: | Bayesian VAR; Climate uncertainty; Connectedness; Energy transition; Stochastic threshold volatility; Volatility-in-mean; Volatility regimes |
| JEL: | Q43 Q54 C11 C32 |
| Date: | 2026–02–16 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:49327 |
| By: | Candelon, Bertrand (Université catholique de Louvain, LIDAM/LFIN, Belgium); Luisi, Angelo (Ghent University) |
| Abstract: | When modeling the dynamics of a large cross section of interdependent variables, the employment of small scale Vector Autoregressive (VAR) models augmented by few factors, extracted as linear combination of the variables under analysis, is the typical solution to avoid the proliferation of parameters in large heterogeneous VARs. The factors’ loadings/weights can be estimated or derived from economic literature, and are usually interpreted as interconnection channels. We propose a novel Likelihood Ratio Test procedure to empirically evaluate the chosen set of weights, and show that testing is fundamental for valid inferences. We exploit the intuition that, if the factors employed are empirically valid, no remaining information from the cross section remains statistically significant. The proposed test is intuitive, easy to implement, and presents very good finite sample properties. In the empirical exercise, we test several interconnection channels for the sovereign bond market in the euro area. |
| Keywords: | Global VARs ; FAVARs ; Likelihood Ratio Test ; Interdependence |
| Date: | 2025–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:ajf:louvlf:2025005 |
| By: | Kumar, Labesh |
| Abstract: | This paper compares eight widely used methods for estimating the output gap, ranging from simple deterministic trends to state space models, using both revised and real time U.S. quarterly data from 1980 onward. The resulting measures differ heavily across approaches. Average gap estimates vary by nearly four percentage points, volatility differs by an order of magnitude, and correlations across methods span from strongly positive to negative. Stability across data vintages also varies substantially. Hamilton type filters show relatively strong agreement between real time and final estimates, while simpler trend based methods are considerably less stable. These differences matter for empirical inference. The choice of output gap measure has important implications for Phillips curve estimates and for forecasting performance. Beveridge Nelson decompositions display strong predictive power for inflation when estimated using revised data but perform less well in real time, whereas refined Beveridge Nelson and modified Hamilton filters deliver more consistent results across vintages. Time varying analysis shows that the relationship between economic slack and inflation strengthens during periods of macroeconomic stress, including the early 1990s recession, the global financial crisis, and the post-pandemic period, rather than declining monotonically. For output growth forecasting, HP filter gaps reduce forecast errors using revised data, while unobserved components models perform best in real time. Although Beveridge Nelson based measures are informative for inflation, they tend to worsen growth forecasts. Combining forecasts across gap measures, particularly using Bates Granger weights, yields more reliable performance by offsetting weaknesses of individual methods. Overall, the findings highlight that methodological uncertainty in measuring slack translates directly into policy uncertainty, cautioning against exclusive reliance on any single output gap estimate. |
| Keywords: | Output gap, trend-cycle decomposition, real-time data, Phillips curve, forecast combination |
| JEL: | C52 E32 E37 |
| Date: | 2026–01–08 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127829 |
| By: | Kishor, N. Kundan |
| Abstract: | We propose a present value model with structural breaks to explain why U.S. house prices remain persistently high relative to rents—a pattern that standard time-invariant models struggle to capture. Our model uses quarterly data from 1975 to 2023 and incorporates the time-varying volatility of the net discount factor as a key priced risk factor. We find five distinct periods with very different pricing patterns. During the Great Moderation (1981-2001), the usual textbook logic held: higher expected rent growth pushed valuations up, while higher discount rates pulled them down. But in the stagflation era of the late 1970s and again in the recent period (2016-2023), these relationships reversed-higher expected rent growth actually lowered valuations. The two big housing booms of the 2000s and the recent period arose through different forces: the 2000s boom saw prices break free from rents, while the recent period showed an unusually strong reaction to discount rates and a new positive role for volatility, suggesting that uncertainty itself made housing more attractive. These shifting patterns show that what once looked like model failures are better understood as signs of changing market regimes. |
| Keywords: | Present Value Model of Housing, Price-Rent Ratio, Structural Break, Volatility Models. |
| JEL: | C32 E44 G12 R21 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127472 |
| By: | Cimadomo, Jacopo; Giannone, Domenico; Lenza, Michele; Monti, Francesca; Sokol, Andrej |
| Abstract: | We design a Bayesian Mixed-Frequency vector autoregression (VAR) model for fiscal monitoring, i.e., to nowcast the government deficit-to-GDP ratio in real time and provide a narrative for its dynamics. The model incorporates both monthly cash and quarterly accrual fiscal indicators, together with other high-frequency macroeconomic and financial variables, as well as real GDP and the GDP deflator. Our model produces timely monthly density nowcasts of the annual deficit ratio, while governments and official institutions generally only publish their point predictions bi-annually. Based on a database of real-time vintages of macroeconomic, financial and fiscal variables for Italy, we show that the nowcasts of the annual deficit to GDP ratio of our model are similarly or more accurate than those of the European Commission, depending on the month in which the nowcast is produced. Our scenario analysis compares the dynamics of the deficit ratio associated with a monetary and a typical recession, finding a more muted response in the latter case. JEL Classification: C11, E52, E62, E63, H68 |
| Keywords: | cash data, government deficit, mixed-frequency, monetary-fiscal interactions, monetary policy shock, nowcasting |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263186 |