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on Econometric Time Series |
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Issue of 2026–05–04
six papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Phillips, Peter C.B.; Han, Chirok |
| Abstract: | This note introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite sample bias, are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform vn rate of convergence. En route, a useful CLT for sample covariances of linear processes is given, following Phillips and Solo (1992). The approach also has useful extensions to dynamic panels. |
| Keywords: | Autoregression, Differencing, Gaussian limit, Mildly explosive processes, Uniformity, Unit root, |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:vuw:vuwecf:33500 |
| By: | Andrew R Garcia (Universidad de Ingeniería y Tecnología); Marco Vega (Banco Central de Reserva del Perú) |
| Abstract: | This paper asks whether structural metadata from an institutional registry carries enough signal to guide variable selection in macroeconomic forecasting. We study this question using two complementary approaches. The first, Metadata ε-Greedy, is a stochastic search policy that uses permutation-invariant embeddings of registry metadata to guide a fixed-budget search over predictor subsets, with forecasting loss as the only feedback signal. The second, Metadata Bayes, performs variable selection entirely within metadata space: it constructs group-level priors from institutional descriptors, updates them via partial correlation with the target, and selects predictors through Thompson sampling, without ever evaluating a forecasting model during selection. Both methods are evaluated on forecasting Peruvian headline CPI under two forecasters, a Vector Autoregression (VAR) and a Random Forest, and benchmarked against random search, greedy forward selection, LASSO, Bayesian Ridge, PCA, and a state-of-the-art Bayesian variable selection method. Metadata Bayes, despite never observing forecasting loss during selection, achieves out-of-sample accuracy competitive with all baselines including the Bayesian benchmark. Metadata ε-Greedy further improves on these results under the VAR during the COVID shock period. Together, the results suggest that registry metadata encodes enough economic structure to serve as a meaningful proxy for predictive relevance, complementing rather than replacing existing forecasting pipelines. |
| Keywords: | Variable selection, Metadata, Economic forecasting, Bayesian methods, Stochastic search |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:rbp:wpaper:dt-2026-012 |
| By: | Burkhard Raunig (Oesterreichische Nationalbank, Economic Studies Division) |
| Abstract: | Directed acyclic graphs (DAGs) provide transparent framework for encoding causal structures and identifying causal effects. This paper demonstrates how DAGs help specify local projections (LPs) for estimating causal impulse responses. Examples illustrate how graphical rules can be used to select controls and instruments for identifying overall and path-specific effects. An empirical application to uncertainty shocks reveals substantial differences in the estimated responses of German industrial production across LP designs. The underlying DAGs help explain these differences and diagnose biases arising from violations of assumed causal structures. A DAG-based instrumental-variable LP reveals pronounced negative effects of U.S. uncertainty shocks. |
| Keywords: | Directed acyclic graph; Local projections; Impulse response; Instrumental variable; Uncertainty shocks |
| JEL: | C18 C22 C26 E27 |
| Date: | 2026–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:onb:oenbwp:271 |
| By: | Schick, Manuel; Opschoor, Anne |
| Abstract: | This paper proposes forecasting joint tail risks for key macroeconomic indicators, GDP growth, inflation, and unemployment, using the US Survey of Professional Forecasters (SPF). By incorporating SPF consensus forecasts into the conditional mean of AR-GARCH-type models, the accuracy of univariate and multivariate predictive densities is significantly improved. Modeling a constant correlation matrix captures strong dependencies, particularly between GDP growth and unemployment. Using US data from 1990 to 2024, we show that the joint modeling framework enables scenario-based analysis in which predictive densities, conditioned on adverse developments in other variables, differ substantially from the baseline marginal distributions. The framework allows for a formal out-of-sample evaluation of joint predictive densities and a transparent assessment of conditional tail risks. |
| Keywords: | Growth-at-Risk; Multivariate Predictive Densities; GARCH; Tail Risk; Macroeconomic Forecasting |
| Date: | 2026–04–24 |
| URL: | https://d.repec.org/n?u=RePEc:awi:wpaper:771 |
| By: | Nico Petz (Oesterreichische Nationalbank); Thomas Zörner (Oesterreichische Nationalbank (OeNB)) |
| Abstract: | This paper analyzes business cycle synchronization and the Phillips curve (PC) relationship in Central, Eastern, and Southeastern European (CESEE) economies relative to the euro area. We find an overall increase in business cycle synchronicity, particularly among Euro adoption candidates, with notable heterogeneities during the early 2000s, the global financial crisis, and the euro crisis. Using a Kalman filter to extract business cycles and various measures of synchronicity, we show that CESEE EU countries align more closely with the euro area than non-EU countries. The unemployment-inflation relationship, analyzed with time-varying parameter (TVP) models, reveals a steepening of the Phillips curve post-COVID-19, with negative slope coefficients across all countries. We observe a growing convergence of the PC slope toward the euro area, especially in candidate countries. These results highlight the role of EU membership in fostering economic synchronization and emphasize the importance of considering time-varying dynamics in assessing economic convergence amid major shocks. |
| Keywords: | Business cycle alignment, synchronization, EMU, euro area, CESEE, time-varying parameter model |
| JEL: | C22 E32 F15 F45 O47 |
| Date: | 2025–05–15 |
| URL: | https://d.repec.org/n?u=RePEc:onb:oenbwp:267 |
| By: | LIU JIENI (Graduate School of Economics, The University of Osaka) |
| Abstract: | This paper proposes a search-then-forecast framework for mid-term (20-trading-day) stock price forecasting and evaluates it on Chinese A-share data. The framework combines a multi-distance voting-based similar-stock search for sample augmentation, a sample-level PCA–ICA feature reconstruction, and a Transformer encoder–decoder whose decoder is initialised at inference with the single-step return at the end of the observation window rather than the conventional zero-padding. Using Luoyang Molybdenum (stock code 603993) from the SSE 180 pool as a single-stock case study, we compare six configurations—TransE, TransED (nhead = 3 and 6), BiLSTM, ARMAGARCH, and a TransED-Embed ablation—across ten observation window lengths, and introduce two baseline-referenced metrics, R2 hist and MASEnaive, to address the limited interpretability of standard R2 and MASE on non-stationary financial series. ARMAGARCH attains the lowest root mean squared error (RMSE) across all tested windows, outperforming the best deep learning model (TransE) by 1.4% to 17.0%; MASEnaive further reveals that most deep learning models fail to surpass a random-walk naive baseline. Observation window length and model architecture exhibit a clear interaction, and a smaller internal Transformer dimension does not hurt performance. Within this single-stock case study, the findings suggest that parsimonious statistical models can match or outperform highly parameterised deep learning architectures for mid-term Chinese A-share forecasting. |
| Keywords: | stock price forecasting; Transformer; ARMAGARCH; similar-stock search; Chinese A-share market. |
| JEL: | C22 C45 C53 G12 G17 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2606 |