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on Econometric Time Series |
| By: | Eric Beutner (Vrije Universiteit Amsterdam); Julia Schaumburg (Vrije Universiteit Amsterdam); Barend Spanjers (Vrije Universiteit Amsterdam) |
| Abstract: | This study reflects on the inconsistency of the fixed-design residual bootstrap procedure for GARCH models under dependent innovations. We introduce a novel recursive-design residual block bootstrap procedure to accurately quantify the uncertainty around parameter estimates and volatility forecasts. A simulation study provides evidence for the validity of the recursive-design residual block bootstrap in the presence of dependent innovations. The resulting bootstrap confidence intervals are not only valid but also potentially narrower than the ones obtained from the inconsistent fixed design bootstrap, depending on the underlying data-generating process and the sample size. In an application to financial time series, we illustrate the empirical relevance of our proposed methods, showing evidence for the residual dependence and demonstrating notable differences between the confidence intervals obtained by the fixed- and the recursive-design bootstrap procedure. |
| Keywords: | GARCH, Dependent Innovations, Residual Block Bootstrap |
| Date: | 2024–01–25 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240008 |
| By: | Lenin Arango-Castillo; Francisco J. Martínez-Ramírez; María José Orraca |
| Abstract: | Persistence is the speed with which a time series returns to its mean after a shock. Although several measures of persistence have been proposed in the literature, when they are empirically applied, the different measures indicate incompatible messages, as they differ both in the level and the implied evolution of persistence. One plausible reason why persistence estimators may differ is the presence of data particularities such as trends, cycles, measurement errors, additive and temporary change outliers, and structural changes. To gauge the usefulness and robustness of different measures of persistence, we compare them in a univariate time series framework using Monte Carlo simulations. We consider nonparametric, semiparametric, and parametric time-domain and frequency-domain persistence estimators and investigate their performance under different anomalies found in practice. Our results indicate that the nonparametric method is, on average, less affected by the different types of time series anomalies analyzed in this work. |
| Keywords: | Persistence;Monte-Carlo simulations;time series |
| JEL: | C15 C53 C22 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bdm:wpaper:2024-11 |
| By: | Anton Skrobotov |
| Abstract: | This review discusses methods of testing for a panel unit root. Modern approaches to testing in cross-sectionally correlated panels are discussed, preceding the analysis with an analysis of independent panels. In addition, methods for testing in the case of non-linearity in the data (for example, in the case of structural breaks) are presented, as well as methods for testing in short panels, when the time dimension is small and finite. In conclusion, links to existing packages that allow implementing some of the described methods are provided. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.08908 |
| By: | Francisco Blasques (Vrije Universiteit Amsterdam); Noah Stegehuis (Vrije Universiteit Amsterdam) |
| Abstract: | This paper proposes a score-driven model for filtering time-varying causal parameters through the use of instrumental variables. In the presence of suitable instruments, we show that we can uncover dynamic causal relations between variables, even in the presence of regressor endogeneity which may arise as a result of simultaneity, omitted variables, or measurement errors. Due to the observation-driven nature of score models, the filtering method is simple and practical to implement. We establish the asymptotic properties of the maximum likelihood estimator and show that the instrumental-variable score-driven filter converges to the unique unknown causal path of the true parameter. We further analyze the finite sample properties of the filtered causal parameter in a comprehensive Monte Carlo exercise. Finally, we reveal the empirical relevance of this method in an application to aggregate consumption in macroeconomic data. |
| Keywords: | observation-driven models, time-varying parameters, causal inference, endogeneity, instrumental variables |
| JEL: | C01 C22 C26 |
| Date: | 2024–02–29 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240016 |
| By: | Rangika Peiris; Minh-Ngoc Tran; Chao Wang; Richard Gerlach |
| Abstract: | A long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating a Recurrent Neural Network (RNN) to handle non-linear dynamics. Loss-based generalized Bayesian inference with Sequential Monte Carlo is employed for model estimation and sequential prediction in RNN HAR. The empirical analysis is conducted using daily closing prices and realized measures from 2000 to 2022 across 31 market indices. The proposed models one step ahead VaR forecasting performance is compared against a basic HAR model and its extensions. The results demonstrate that the proposed RNN-HAR model consistently outperforms all other models considered in the study. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.13588 |
| By: | Martín Almuzara (Federal Reserve Bank of New York); Víctor Sancibrián (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | We study panel data regression models when the shocks of interest are aggregate and possibly small relative to idiosyncratic noise. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when units have heterogeneous responses and how to obtain valid standard errors and confidence intervals. A simple recipe leads to robust inference: including lags as controls and then clustering at the time level. This strategy is valid under general error dynamics and uniformly over the degree of signal-to-noise of macro shocks. |
| Keywords: | Panel data, local projections, impulse responses, aggregate shocks, inference, signal-to-noise, heterogeneity. |
| JEL: | C22 C23 C32 C33 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cmf:wpaper:wp2024_2412 |
| By: | Martin, Ertl (Institute for Advanced Studies Vienna, Austria); Fortin, Ines (Institute for Advanced Studies Vienna, Austria); Hlouskova, Jaroslava (Institute for Advanced Studies Vienna, Austria); Koch, Sebastian P. (Institute for Advanced Studies Vienna, Austria); Kunst, Robert M. (Institute for Advanced Studies Vienna, Austria); Sögner, Leopold (Institute for Advanced Studies Vienna, Austria and Vienna Graduate School of Finance (VGSF)) |
| Abstract: | Recently, many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia’s invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with t-distributed innovations or with stochastic volatility. While inflation, industrial production, oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency framework using the forward-filtering-backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries which extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of our mixed-frequency BVAR model, we compare these inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score. |
| Keywords: | Bayesian VAR, mixed-frequency, forward-filtering-backward-sampling, inflation forecasting |
| JEL: | C5 E3 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ihs:ihswps:number56 |
| By: | Zhuohan Wang; Carmine Ventre |
| Abstract: | Financial time series often exhibit low signal-to-noise ratio, posing significant challenges for accurate data interpretation and prediction and ultimately decision making. Generative models have gained attention as powerful tools for simulating and predicting intricate data patterns, with the diffusion model emerging as a particularly effective method. This paper introduces a novel approach utilizing the diffusion model as a denoiser for financial time series in order to improve data predictability and trading performance. By leveraging the forward and reverse processes of the conditional diffusion model to add and remove noise progressively, we reconstruct original data from noisy inputs. Our extensive experiments demonstrate that diffusion model-based denoised time series significantly enhance the performance on downstream future return classification tasks. Moreover, trading signals derived from the denoised data yield more profitable trades with fewer transactions, thereby minimizing transaction costs and increasing overall trading efficiency. Finally, we show that by using classifiers trained on denoised time series, we can recognize the noising state of the market and obtain excess return. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.02138 |