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on Econometric Time Series |
| By: | Helmut Lütkepohl; Fei Shang; Luis Uzeda; Tomasz Woźniak |
| Abstract: | We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions that ensures the matrix containing structural parameters is either partially or globally unique; (ii) a shrinkage prior distribution for the conditional variance of structural shocks, centred on the hypothesis of homoskedasticity; and (iii) a statistical procedure for assessing the validity of the conditions outlined in (i). Our shrinkage prior ensures that the evidence for identifying a structural shock relies predominantly on the data and is less influenced by the prior distribution. We demonstrate the usefulness of our framework through a fiscal structural model and a series of simulation exercises. |
| Keywords: | Econometric and statistical methods; Fiscal policy |
| JEL: | C11 C12 C32 E62 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-14 |
| By: | Joshua Chan; Christian Matthes; Xuewen Yu |
| Abstract: | Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To address this, we introduce a new numerically efficient algorithm that facilitates the estimation of impulse responses and related measures in large structural VARs identified with a large number of structural restrictions on impulse responses. The methodology is illustrated using a 35-variable VAR with over 100 sign and ranking restrictions to identify 8 structural shocks. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.20668 |
| By: | Jos\'e Luis Montiel Olea; Mikkel Plagborg-M{\o}ller; Eric Qian; Christian K. Wolf |
| Abstract: | What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in finite samples lie on opposite ends of a bias-variance trade-off. While the low bias of LPs comes at a quite steep variance cost, this cost must be paid to achieve robust uncertainty assessments. VARs should thus only be used with long lag lengths, ensuring equivalence with LP. For LP estimation, we provide guidance on selection of lag length and controls, bias correction, and confidence interval construction. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.17144 |
| By: | Masamune Iwasawa |
| Abstract: | Parameter estimation can result in substantial mean squared error (MSE), even when consistent estimators are used and the sample size is large. This paper addresses the longstanding statistical challenge of analyzing the bias and MSE of ridge-type estimators in nonlinear models, including duration, Poisson, and multinomial choice models, where theoretical results have been scarce. Employing a finite-sample approximation technique developed in the econometrics literature, this study derives new theoretical results showing that the generalized ridge maximum likelihood estimator (MLE) achieves lower finite-sample MSE than the conventional MLE across a broad class of nonlinear models. Importantly, the analysis extends beyond parameter estimation to model-based prediction, demonstrating that the generalized ridge estimator improves predictive accuracy relative to the generic MLE for sufficiently small penalty terms, regardless of the validity of the incorporated hypotheses. Extensive simulation studies and an empirical application involving the estimation of marginal mean and quantile treatment effects further support the superior performance and practical applicability of the proposed method. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.19018 |