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on Econometric Time Series |
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Issue of 2026–09–14
fourteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Tetsuya Takaishi |
| Abstract: | Herein, we propose a quantum circuit learning framework for modeling the realized volatility (RV) of Bitcoin and investigate the statistical properties of the predicted time series through multifractal analysis. Unlike conventional GARCH-type models, which require a pre-specified functional form for the volatility process, a parameterized quantum circuit directly approximates the volatility function from empirical data, eliminating the need for explicit model selection. Using five-minute Bitcoin price data, we construct daily RV, train a single-qubit parameterized quantum circuit, and generate a long synthetic time series from the optimized quantum circuit. Multifractal Detrended Fluctuation Analysis is applied to calculate the generalized Hurst exponent $h(q)$, the singularity spectrum $f(\alpha)$, and the multifractal scaling exponent $\tau(q)$. The predicted return series exhibits $h(2)\approx 0.5$, consistent with near-random dynamics, and both the predicted and the empirical return series display multifractality that partially persists after random shuffling. The increment series of RV shows pronounced anti-persistence with $h(2)\approx 0.05$--$0.1$, consistent with the rough volatility hypothesis. These results demonstrate that a simple single-qubit parameterized quantum circuit captures qualitatively some observed properties in Bitcoin volatility dynamics. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.04569 |
| By: | Todd Clark; Florian Huber |
| Abstract: | Estimating the dynamic effects of economic shocks in short and very short samples is impeded by a lack of degrees of freedom. We offer a solution based on a Bayesian hierarchical framework for estimating local projection (LP) impulse response functions across a panel of related time series. The framework explicitly accommodates unbalanced panels in which some series are substantially shorter than others, allowing the short series to borrow information from longer ones at horizons where the short series carry little or no own data. Since series might exhibit heterogeneous dynamics, we develop a sparse finite mixture pool that clusters units by similarity of their impulse response profiles. We show in simulations that our approach substantially improves LP estimation accuracy relative to the standard approach if the time series are short while producing similar LPs for longer time series. Using a US price dataset, augmented with survey responses, we find that supply-chain and oil shocks trigger heterogeneous reactions of different price measures, with headline price indices responding more sharply than their core counterparts and goods prices changing more than services prices. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04631 |
| By: | Prengle, Scott H. |
| Abstract: | Diebold and Li (2006) showed that a simple autoregressive model of the Nelson-Siegel yield curve factors produces genuinely useful 12-month-ahead forecasts. Replicated on an extended U.S. Treasury sample running through 2026 — roughly 26 years beyond their original 1985-2000 window — that result does not hold: the model loses to a naive random-walk forecast on every tenor tested, under both expanding and rolling estimation windows, with degradation frequently exceeding 10%. The mechanism is identifiable and consistent with independent structural work: the zero-lower-bound period introduced a materially different factor dynamic that the forecasting specification could not accommodate. Rather than treat this as a dead end, this paper develops and formally tests an alternative: a small number of recurring, economically interpretable curve-shape states — fewer in number than they first appear, once rate level and curve shape are properly separated. Seven falsifiable hypotheses are tested: bootstrap cluster stability, permutation-tested temporal persistence, chi-square correspondence with Fed policy phase and NBER recession dating (with effect sizes reported), a formally BIC-gated comparison showing a unified hidden Markov model outperforms static clustering (robust to leave-one-episode-out testing), and surrogate-tested spectral hypotheses returning informative negative results. Three analytical errors identified and corrected during testing are reported explicitly. The resulting framework is deliberately descriptive rather than predictive, and is extended into an operational monthly monitoring tool built on causal (filtering-based) inference rather than retrospective smoothing. The paper's central claim is narrower than a forecasting model would be: the curve's useful information may lie in its current state rather than its future trajectory. |
| Keywords: | term structure; treasury yields; Nelson-Siegel; Diebold-Li; hidden Markov model; regime switching; causal filtering; random-walk benchmark |
| JEL: | C22 E43 G12 |
| Date: | 2026–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:130391 |
| By: | James D. Hamilton; Xinwei Ma; Jin Xi |
| Abstract: | This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. An application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23732 |
| By: | Dawis Kim; Tao Zha |
| Abstract: | We develop a unified framework that combines shock volatility with sign and narrative restrictions and provides the theoretical foundation for the computationally efficient sampler HARS. HARS preserves the heteroskedastic likelihood and can be combined with any posterior simulator for the heteroskedastic model. In monetary policy, oil market, and fiscal policy models, the same restrictions deliver substantively different economics once shock heteroskedasticity is accounted for. Homoskedastic SVARs put uncertainty in the wrong place, pushing shock-scale variation into impulse-response uncertainty. Heteroskedasticity sharpens dynamic responses, alters economic conclusions, and restores 90% credible intervals as a practical standard for economic inference. |
| JEL: | C11 C32 E52 E62 Q43 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35483 |
| By: | Jean-Marie Dufour; Tianyu He |
| Abstract: | This paper proposes asymptotically distribution-free inference methods for comparing estimators which admit asymptotically linear Gaussian functional representations across dependent samples. The framework applies to a broad range of welfare indices used in inequality, poverty, and risk analysis. Two distinct situations are considered. First, we propose asymptotic and bootstrap in- tersection methods which are valid under arbitrary dependence between two samples. Second, we focus on the common case of overlapping samples—a special form of dependent samples where sample dependence arises solely from matched pairs—and provide asymptotic and bootstrap meth- ods for comparing indices. We derive consistent estimates for asymptotic variances using the influ- ence function approach. We study the finite-sample performance of the proposed methods through Monte Carlo simulations and find that confidence intervals based on overlapping samples exhibit satisfactory coverage rates and reasonable precision. In contrast, conventional methods based on the assumption of independent samples perform poorly in terms of coverage rates and interval widths. Asymptotic inference can be less reliable when dealing with heavy-tailed distributions, while the bootstrap method provides a viable remedy, unless the variance is substantial or fails to exist. The intersection method yields reliable results with arbitrary dependent samples, including settings in which the overlapping-sample assumptions do not hold. We demonstrate the practical applicability of our proposed methods in analyzing changes in household financial inequality in Italy over time. Cet article propose des méthodes d’inférence asymptotiquement indépendantes de la distribution pour comparer des estimateurs qui admettent des représentations fonctionnelles gaussiennes asymptotiquement linéaires sur des échantillons dépendants. Ce cadre s’applique à un large éventail d’indices de bien-être utilisés dans l’analyse des inégalités, de la pauvreté et des risques. Deux situations distinctes sont examinées. Premièrement, nous proposons des méthodes d’intersection asymptotiques et par bootstrap qui sont valables en présence d’une dépendance arbitraire entre deux échantillons. Ensuite, nous nous concentrons sur le cas courant des échantillons qui se chevauchent — une forme particulière d’échantillons dépendants où la dépendance résulte uniquement de paires appariées — et proposons des méthodes asymptotiques et de bootstrap pour comparer les indices. Nous dérivons des estimations cohérentes des variances asymptotiques à l’aide de l’approche par la fonction d’influence. Nous étudions les performances en échantillon fini des méthodes proposées à l’aide de simulations de Monte Carlo et constatons que les intervalles de confiance basés sur des échantillons chevauchants présentent des taux de couverture satisfaisants et une précision raisonnable. En revanche, les méthodes conventionnelles reposant sur l’hypothèse d’échantillons indépendants affichent de mauvaises performances en termes de taux de couverture et de largeurs d’intervalle. L’inférence asymptotique peut s’avérer moins fiable lorsqu’il s’agit de distributions à queues épaisses, tandis que la méthode du bootstrap offre une solution viable, à moins que la variance ne soit importante ou n’existe pas. La méthode d'intersection fournit des résultats fiables pour des échantillons dépendants arbitraires, y compris dans les cas où les hypothèses relatives au chevauchement des échantillons ne sont pas vérifiées. Nous démontrons l'applicabilité pratique des méthodes que nous proposons en analysant l'évolution des inégalités financières entre les ménages en Italie au fil du temps. |
| Keywords: | inequality measures, poverty measures, influence function, asymptotic inference, intersection method, bootstrap, confidence interval, overlapping samples, dependent samples, mesures d'inégalité, mesures de pauvreté, fonction d'influence, inférence asymptotique, méthode d'intersection, bootstrap, intervalle de confiance, échantillons chevauchants, échantillons dépendants |
| JEL: | C01 C1 C12 C14 C15 D6 D63 G5 I3 I32 |
| Date: | 2026–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2026s-14 |
| By: | Dario Caldara; Haroon Mumtaz; Molin Zhong |
| Abstract: | We characterize asymmetric tail risk across over one hundred U.S. macroeconomic and financial variables using a dynamic factor model with stochastic volatility. A single mechanism unifies growth-at-risk, inflation-at-risk, and sectoral risk heterogeneity: common factors and their volatilities move together, while heterogeneous loadings transmit the resulting asymmetry unevenly across variables. We find that asymmetric tail risk is pervasive but heterogeneous. The heterogeneity is systematic: factor exposures, especially to financial conditions and inflation, explain over half of the cross-sectional variation in tail asymmetry across variables. These exposures determine where in the economy vulnerabilities concentrate and how the balance of tail risks shifts over time. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05676 |
| By: | Vance Martin; Yoshihiko Nishiyama; John Stachurski; Yiran Xie |
| Abstract: | We introduce a new density-based goodness of fit test for ergodic Markov processes. Our test compares the data against the class of models specified in the null hypothesis, and rejects if no model in the class yields a stationary density that matches with the data. No alternative needs to be specified in order to implement the test. Although our test compares densities, estimation of smoothing parameters is not required, and the test has nontrivial power against $1/\sqrt{n}$ local alternatives. The test provides new perspectives on some existing problems in econometric and financial modeling. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.03088 |
| By: | Tim Gebbie |
| Abstract: | We consider reflexivity in hierarchical causal systems in which higher-level states constrain the lower-level dynamics that remain admissible [Wilcox and Gebbie (2014), Gebbie (2026)]. We ask how such state-dependent top-down constraints are realised when local activity is event driven while causal claims are made in calendar time. If an actor-conditioned constraint governs both the admissible event transitions and their timing, the event process is carried into calendar time by a state-dependent time change. If either the admissible constraint or the compatible timing law is not unique, the induced calendar-time causal law need not be unique even when the local event dynamics admit a Markov representation. Hierarchical causality keeps explicit the cross-level constraint and event coordination from which such a calendar-time law must be constructed. Within this hierarchical representation, reflexivity is the endogenous closure of the causal loop through state-dependent top-down constraint and an event-to-calendar time map. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.22497 |
| By: | Naveed Javed; James Morley |
| Abstract: | We consider empirically how fiscal stance influences the potency of monetary policy. New Zealand provides a compelling laboratory to study this form of monetary-fiscal interaction given its stable history of inflation targeting and substantial changes in its fiscal stance due to global forces acting on a small open economy (SOE). A simulation-based Bayesian Local Projection (BLP) framework is developed to estimate the macroeconomic effects of a narrative measure of monetary policy shocks when there are many possible omitted variables, especially in this SOE setting. Our BLP approach incorporates a novel shape prior on impulse response functions to help manage the substantial bias-efficiency tradeoffs given the relatively small effective sample sizes when considering nonlinearities inherent in policy interactions. For a smooth-transition regime-switching model with an endogenously-estimated threshold parameter, we find that monetary policy is clearly more potent, especially in terms of out-put and inflation, when there is a high degree of fiscal consolidation. Consistent with the fiscal theory of the price level, our results for a more general model that also allows for sign asymmetries suggest the effects of expansionary versus contractionary monetary policy shocks on output, inflation, and the exchange rate are actually reasonably symmetric, implying that the potency of monetary policy is more related to monetary-fiscal dominance than coordination. |
| Keywords: | monetary-fiscal interactions, Bayesian local projections, fiscal theory of the price level |
| JEL: | C32 E52 E58 E63 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-75 |
| By: | Dobrislav Dobrev; Ernst Schaumburg |
| Abstract: | This paper develops a model-free measurement and inference framework for high-frequency cross-market trading activity and provides empirical evidence of its importance in financial markets. We represent trading activity in two markets as temporal point processes and count the number of time bins in which both processes register activity at a given lag. Under the null of cross-process independence, without global stationarity, a Chen–Stein approximation yields Poisson convergence in total variation for this simple bin-based measure of cross-activity under mild regularity conditions. A local-stationarity framework for marginal intensities, allowing finitely many jumps, then yields a feasible blockwise estimator of the null Poisson mean. The resulting feasible asymptotic theory leads to three easy-to-implement statistical tests for independence at a given lag. It further yields consistent score-driven identification of dependence lags caused by lagged common components in market activity, characteristic of linked order executions across markets. This novel identification framework exploits that common components induce a singular line mass and Poisson score divergence at latency-determined dependence lags. Monte Carlo experiments with nonstationary superpositions of Hawkes processes confirm satisfactory test size, power, and lag-identification performance in finite samples. Empirical validation using transaction data for U.S. Treasury and equity cash-futures markets from 2010 to 2024 reveals sharply localized cross-activity dependencies at lags matching microwave latency, while not rejecting independence at more distant lags. High-frequency cross-market trading generally intensifies during market stress episodes and exhibits pronounced intraday surges after FOMC announcements, confirming its role in information propagation and efficient price discovery across linked markets. |
| Keywords: | High-frequency trading; cross-market activity; temporal point processes; nonstationarity; Chen–Stein method; Poisson approximation; lead–lag identification; market microstructure. |
| JEL: | C12 C14 C32 C41 C46 C58 G12 G14 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2026-011 |
| By: | Gabriela Miyazato Szini |
| Abstract: | I develop an estimation and inference framework for distribution regression in dyadic network settings with two-way fixed effects that vary across thresholds of the outcome. I show that identification of the structural parameters is achieved through binarization of the outcome at each threshold, and estimate the model by conditional maximum likelihood, which "differences out" the fixed effects and circumvents the incidental parameter problem. The estimator remains asymptotically unbiased under sparsity, whether from the network structure or binarization at extreme thresholds. The second novelty is to establish the joint asymptotic distribution of the estimators across multiple thresholds with different convergence rates, and to develop simultaneous confidence bands and tests for equality of coefficients across thresholds. Monte Carlo simulations confirm small bias, valid inference, and correct simultaneous coverage under sparsity. An application to bilateral trade finds that coefficients vary substantially across the distribution, with equality rejected for key trade barriers. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04983 |
| By: | Daniyal Ali Hameedi |
| Abstract: | In this paper we propose a new formulation of the Bayesian Filter as used in the discrete-time Markov-Switching-Multifractal (MSM) model of volatility based on existing permutation symmetry within the likelihood structure. We show both analytically and empirically that such a formulation leads to a reduction in time complexity from $O(D^k)$ to $O(k^D)$ thereby significantly reducing the computational bottleneck associated with dimensionality. We compare the agreement between the naive and sector filters and find that while there are significant disagreements, the ground-truth recovery of the latter seems to improve on the former. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.22864 |
| By: | Guillaume Flament; Christophe Hurlin; Quentin Lajaunie; Yoann Pull |
| Abstract: | Credit stress testing requires impulse responses of portfolio default probabilities, not only macro-financial drivers. We derive closed-form generalized impulse responses for the mean, quantiles (PD-at-Risk), and expected shortfall in a modular framework combining a Bayesian VAR, a Gaussian satellite, and the Merton-Vasicek model underlying Basel IRB regulation. Results extend to any probit-Gaussian mapping of a latent factor. Nonlinearity makes responses depend on conditional means and variances; plug-in evaluations understate projected default probability levels by 6-8% and miss tail quantiles. For U.S. geopolitical risk shocks, 99%-quantile responses exceed mean responses by 50%, and peak responses vary 4.6-fold across the credit cycle. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04469 |