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on Econometric Time Series |
| By: | Aknouche, Abdelhakim; Gouveia, Sonia; Scotto, Manuel |
| Abstract: | A common approach to analyze count time series is to fit models based on random sum operators. As an alternative, this paper introduces time series models based on a random multiplication operator, which is simply the multiplication of a variable operand by an integer-valued random coefficient, whose mean is the constant operand. Such operation is endowed into auto-regressive-like models with integer-valued random inputs, addressed as RMINAR. Two special variants are studied, namely the N-valued random coefficient auto-regressive model and the N-valued random coefficient multiplicative error model. Furthermore, Z-valued extensions are considered. The dynamic structure of the proposed models is studied in detail. In particular, their corresponding solutions are everywhere strictly stationary and ergodic, a fact that is not common neither in the literature on integer-valued time series models nor real-valued random coefficient auto-regressive models. Therefore, the parameters of the RMINAR model are estimated using a four-stage weighted least squares estimator, with consistency and asymptotic normality established everywhere in the parameter space. Finally, the new RMINAR models are illustrated with some simulated and empirical examples. |
| Keywords: | integer-valued random coefficient AR, random multiplication integer-valued auto-regression, random multiplication operator, RMINAR, WLS estimators |
| JEL: | C13 C22 C25 C43 C51 C53 |
| Date: | 2023–12–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119518&r=ets |
| By: | Gianluca Cubadda (CEIS & DEF, University of Rome "Tor Vergata"); Stefano Grassi (DEF, University of Rome "Tor Vergata"); Barbara Guardabascio (University of Perugia) |
| Abstract: | Many economic variables are characterized by changes in their conditional mean and volatility, and time-varying Vector Autoregressive Models are often used to handle such complexity. Unfortunately, as the number of series grows, they present increasing estimation and interpretation issues. This paper tries to address this problem by proposing a Multivariate Autoregressive Index model that features time-varying mean and volatility. Technically, we develop a new estimation methodology that mixes switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows one to select or weigh the number of common components, and other data features, in real-time without additional computational costs. Using US macroeconomic data, we provide a forecast exercise that shows the feasibility and usefulness of this model. |
| Keywords: | Large Vector Autoregressive Models, Multivariate Autoregressive Index Models, Time-Varying Parameter Models, Bayesian Vector Autoregressive Models. |
| Date: | 2024–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:571&r=ets |
| By: | Jalal Etesami; Ali Habibnia; Negar Kiyavash |
| Abstract: | We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an information-theoretic measure rooted in a generalized version of Granger-causality, which is applicable to both linear and nonlinear dynamics. Our framework offers advancements in measuring systemic risk and establishes meaningful connections with established econometric models, including vector autoregression and switching models. We evaluate the efficacy of our proposed model through simulation experiments and empirical analysis, reporting promising results in recovering simulated time-varying networks with nonlinear and multivariate structures. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network. We focus on cryptocurrencies' potential systemic risks to financial stability, including spillover effects on other sectors during crises like the COVID-19 pandemic and the Federal Reserve's 2020 emergency response. Our findings reveals significant, previously underrecognized pre-2020 influences of cryptocurrencies on certain financial sectors, highlighting their potential systemic risks and offering a systematic approach in tracking evolving cross-sector interactions within financial networks. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16707&r=ets |
| By: | Sara Boni (Faculty of Economics and Management, Free University of Bozen-Bolzano, Italy); Massimiliano Caporin (University of Padova, Italy); Francesco Ravazzolo (@ Department of Data Science and Analytics, BI Norwegian Business School, Norway; Faculty of Economics and Management, Free University of Bozen-Bolzano, Italy) |
| Abstract: | This paper proposes a non-parametric test for Granger causality in quantiles to detect causality from a high-frequency driver to a low-frequency target. In an economic application, we examine Granger causality between inflation, as a low-frequency macroeconomic variable, and a selection of commodity futures, including gold, oil, and corn, as high-frequency financial variables. We find that logarithmic returns on given commodity futures are a prima facie cause of inflation at the lower quantiles of the distribution and marginally around the median. In the context of a nowcasting exercise, we find that incorporating commodity futures in the model with a polynomial function enhances short-term forecasting accuracy, leveraging timely data for more precise nowcasting of inflationary trends. |
| Keywords: | MIDAS Quantile, Granger Causality, Commodities, Inflation, Nowcasting. |
| JEL: | C12 C14 C58 E31 Q02 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:bzn:wpaper:bemps102&r=ets |
| By: | Liu, Jia; Maheu, John M; Song, Yong |
| Abstract: | Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross-section of state specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared to several benchmark models including univariate Markov switching models. |
| Keywords: | Markov switching, Multivariate analysis, Investment strategies, Market timing |
| JEL: | C32 C53 C58 G1 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119515&r=ets |
| By: | Tony Chernis; Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell |
| Abstract: | Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area’s Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits—in terms of improved forecast accuracy and interpretability—of modeling the synthesis function nonparametrically. |
| Keywords: | Econometric and Statistical Methods |
| JEL: | C11 C32 C53 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:23-61&r=ets |
| By: | Danilo Cascaldi-Garcia; Matteo Luciani; Michele Modugno |
| Abstract: | In economics, we need to forecast the present because reliable and comprehensive measures of the state of the economy are released with a substantial delay and considerable measurement error. Nowcasting exploits timely data to obtain early estimates of the state of the economy and updates these estimates continuously as new macroeconomic data are released. In this chapter, we describe how the framework used to nowcast GDP has evolved and is applied worldwide. |
| Keywords: | Dynamic factor models; Forecasting; Nowcasting |
| JEL: | C33 C53 E37 |
| Date: | 2023–12–18 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1385&r=ets |