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on Econometric Time Series |
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Issue of 2025–10–27
ten papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Bauer, Dietmar; del Barrio Castro, Tomás |
| Abstract: | Economic time series often show a strong persistency as well as seasonal variations that are appropri ately modelled using seasonal unit root models in addition to deterministic components. In many cases di¤erent variables within a vector time series are driven by identical common trends and cycles leading to cointegration. This paper investigates the consequences for the properties of vector processes when some components are aggregated in time. This may involve moving from a fully observed system that is seasonally cointegrated at a frequency !k = 2 k=S with k = 1;:::;(S 1)=2 where S is the number of seasons per year, to a system with time series sampled at high sampling rate (HSR) observed for S seasons per year and time series with low sampling rate (LSR) observed SA seasons per year, such that SA = S=Q and Q is an integer. The (partial) aggregation has implications on the unit root and cointegration properties: Aggregation potentially shifts the frequency of the unit roots. This may lead to an aliasing e¤ect wherein common cycles to di¤erent unit roots become aligned and cannot be separated any more, in turn impacting cointegrating relations. This paper uses the triangular systems representations in the bivariate case as well as the state space framework (in a general setting) to investigate the e¤ect of aggregation on the unit root properties of multivariate time series. The main results indicate under which assumptions and in which situations the analysis of the integration and cointegration properties of time series with mixed sampling rate relates to the same properties of the underyling data generating process. The results also discuss full aggregation of all components. These results lead to the proposal of an e¤ective econometric strategy for detecting cointegration at the various sampling rates, as is demonstrated in a simulation exercise. Finally an empirical application with monthly data of arrivals and departures of the Mallorca Airport, also illustrate the ndings collected in the present work. |
| Keywords: | Seasonal Cointegration, Polynomial cointegration, Periodic Cointegration, Mixed-Frequency, Aggregation, Demodulator operator |
| JEL: | C22 C32 |
| Date: | 2025–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126066 |
| By: | Gabriele Casto |
| Abstract: | We introduce the Historical and Dynamic Volatility Ratios (HVR/DVR) and show that equity and index volatilities are cointegrated at intraday and daily horizons. This allows us to construct a VECM to forecast portfolio volatility by exploiting volatility cointegration. On S&P 500 data, HVR is generally stationary and cointegration with the index is frequent; the VECM implementation yields substantially lower mean absolute percentage error (MAPE) than covariance-based forecasts at short- to medium-term horizons across portfolio sizes. The approach is interpretable and readily implementable, factorizing covariance into market volatility, relative-volatility ratios, and correlations. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.23533 |
| By: | Mar\'ia Dolores Gadea; \`Oscar Jord\`a |
| Abstract: | Bootstrap procedures for local projections typically rely on assuming that the data generating process (DGP) is a finite order vector autoregression (VAR), often taken to be that implied by the local projection at horizon 1. Although convenient, it is well documented that a VAR can be a poor approximation to impulse dynamics at horizons beyond its lag length. In this paper we assume instead that the precise form of the parametric model generating the data is not known. If one is willing to assume that the DGP is perhaps an infinite order process, a larger class of models can be accommodated and more tailored bootstrap procedures can be constructed. Using the moving average representation of the data, we construct appropriate bootstrap procedures. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.17949 |
| By: | Abhimanyu Gupta; Myung Hwan Seo |
| Abstract: | We develop a class of optimal tests for a structural break occurring at an unknown date in infinite and growing-order time series regression models, such as AR($\infty$), linear regression with increasingly many covariates, and nonparametric regression. Under an auxiliary i.i.d. Gaussian error assumption, we derive an average power optimal test, establishing a growing-dimensional analog of the exponential tests of Andrews and Ploberger (1994) to handle identification failure under the null hypothesis of no break. Relaxing the i.i.d. Gaussian assumption to a more general dependence structure, we establish a functional central limit theorem for the underlying stochastic processes, which features an extra high-order serial dependence term due to the growing dimension. We robustify our test both against this term and finite sample bias and illustrate its excellent performance and practical relevance in a Monte Carlo study and a real data empirical example. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.12262 |
| By: | Prosper Dovonon; Nikolay Gospodinov |
| Abstract: | This paper studies the limiting behavior of the test for instrument exogeneity in linear models when there is uncertainty about the strength of the identification signal. We consider the test for conditional moment restrictions with an expanding set of constructed instruments. We establish the uniform validity of the standard normal asymptotic approximation, under the null, of this specification test over all possible degrees of model identification. As a result, this allows the researcher to use standard inference for testing instrument exogeneity without the need of any prior knowledge if the instruments are strong, semi-strong, weak, or completely irrelevant. Furthermore, we show that the test is consistent regardless of the instrument strength; i.e., even in cases (weak and completely irrelevant instruments) where the standard tests fail to exhibit asymptotic power. To obtain these results, we characterize the rate of the estimator under a drifting sequence for the identification signal. We illustrate the appealing properties of the test in simulations and an empirical application. |
| Keywords: | linear instrumental variables (IV) model; conditional test for instrument exogeneity; uniform inference; instrument strength; generalized method of moments (GMM) estimator; drifting sequences; expanding set of basis functions |
| JEL: | C12 C14 C26 C52 |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedawp:101963 |
| By: | David Schenck (StataCorp) |
| Abstract: | In time-series analysis, researchers are often interested in estimating dynamic causal effects. These effects are estimated using impulse–response functions. In this talk, I describe several methods for estimating impulse–response functions with a focus on instrumental-variables approaches. I describe the theory and then show how to estimate effects using Stata's lpirf, ivsvar, and ivlpirf commands. I also demonstrate tools to graph, tabulate, and compare impulse responses across models. |
| Date: | 2025–10–05 |
| URL: | https://d.repec.org/n?u=RePEc:boc:cand25:14 |
| By: | Christoph Breunig; Ruixuan Liu; Zhengfei Yu |
| Abstract: | We develop a semiparametric framework for inference on the mean response in missing-data settings using a corrected posterior distribution. Our approach is tailored to Bayesian Additive Regression Trees (BART), which is a powerful predictive method but whose nonsmoothness complicate asymptotic theory with multi-dimensional covariates. When using BART combined with Bayesian bootstrap weights, we establish a new Bernstein-von Mises theorem and show that the limit distribution generally contains a bias term. To address this, we introduce RoBART, a posterior bias-correction that robustifies BART for valid inference on the mean response. Monte Carlo studies support our theory, demonstrating reduced bias and improved coverage relative to existing procedures using BART. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.24634 |
| By: | Zhuoxun Li; Clifford M. Hurvich |
| Abstract: | In this paper, we propose a new heteroskedasticity and autocorrelation consistent covariance matrix estimator based on the prewhitened kernel estimator and a localized leave-one-out frequency domain cross-validation (FDCV). We adapt the cross-validated log likelihood (CVLL) function to simultaneously select the order of the prewhitening vector autoregression (VAR) and the bandwidth. The prewhitening VAR is estimated by the Burg method without eigen adjustment as we find the eigen adjustment rule of Andrews and Monahan (1992) can be triggered unnecessarily and harmfully when regressors have nonzero mean. Through Monte Carlo simulations and three empirical examples, we illustrate the flaws of eigen adjustment and the reliability of our method. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.23256 |
| By: | Yiannis Karavias (Brunel University London); Joakim Westerlund (Lund University); Jan Ditzen (Free University of Bozen-Bolzano) |
| Abstract: | Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the 2007–2008 Rnancial crisis and the 2020 COVID-19 outbreak. Detecting the existence of breaks, and dating them is therefore necessary, not only for estimation purposes but also for understanding drivers of change and their effect on relationships. This talk will introduce an updated version of xtbreak and discuss use, options, and capabilities of xtbreak. First, the relevant econometric theory will be revisited followed by empirical examples. Emphasis will be put on challenges using xtbreak in panel data and how to interpret results and speed improvements using Python. |
| Date: | 2025–09–04 |
| URL: | https://d.repec.org/n?u=RePEc:boc:lsug25:06 |
| By: | Alfonso Ugarte-Ruiz (BBVA Research) |
| Abstract: | I review all the possible alternatives of specifying nonlinear impulse– response functions (IRF) through local-projections that are available using the community-contributed command, locproj. For instance, the command allows easily specifying shocks that include basic nonlinearities such as state-dependent impacts, quadratic effects, interactions between continuous variables, etc. Moreover, it allows nonlinearities in the dependent variable, such as when we are interested in estimating the response of the probability of a binary outcome or when one wants to uncover nonlinear effects of a shock by letting the parameters of the local projection regressions vary across the conditional distribution of the dependent variable through the use of quantile regression. I explain how to use all the available options in locproj to accommodate all of these different methodological alternatives and discuss the advantages that the command offers, for instance, that the command facilitates introducing lags of the dependent or the shock variables when using the Stata command qreg, which in principle does not allow time-series operators. |
| Date: | 2025–09–04 |
| URL: | https://d.repec.org/n?u=RePEc:boc:lsug25:05 |