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on Econometrics |
| 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=ecm |
| By: | Denis Chetverikov; Jinyong Hahn; Zhipeng Liao; Shuyang Sheng |
| Abstract: | We propose logit-based IV and augmented logit-based IV estimators that serve as alternatives to the traditionally used 2SLS estimator in the model where both the endogenous treatment variable and the corresponding instrument are binary. Our novel estimators are as easy to compute as the 2SLS estimator but have an advantage over the 2SLS estimator in terms of causal interpretability. In particular, in certain cases where the probability limits of both our estimators and the 2SLS estimator take the form of weighted-average treatment effects, our estimators are guaranteed to yield non-negative weights whereas the 2SLS estimator is not. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.10333&r=ecm |
| 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=ecm |
| By: | Jinyong Hahn; Zhipeng Liao; Nan Liu; Shuyang Sheng |
| Abstract: | This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test. Moreover, we find that the test statistics used in these tests can be numerically ordered, indicating their relative power properties in finite samples. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.10558&r=ecm |
| 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=ecm |
| 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=ecm |
| By: | Peter Christensen |
| Abstract: | Inspired by the activity signature introduced by Todorov and Tauchen (2010), which was used to measure the activity of a semimartingale, this paper introduces the roughness signature function. The paper illustrates how it can be used to determine whether a discretely observed process is generated by a continuous process that is rougher than a Brownian motion, a pure-jump process, or a combination of the two. Further, if a continuous rough process is present, the function gives an estimate of the roughness index. This is done through an extensive simulation study, where we find that the roughness signature function works as expected on rough processes. We further derive some asymptotic properties of this new signature function. The function is applied empirically to three different volatility measures for the S&P500 index. The three measures are realized volatility, the VIX, and the option-extracted volatility estimator of Todorov (2019). The realized volatility and option-extracted volatility show signs of roughness, with the option-extracted volatility appearing smoother than the realized volatility, while the VIX appears to be driven by a continuous martingale with jumps. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02819&r=ecm |
| By: | Sam Ganzfried |
| Abstract: | We present a nonparametric statistical test for determining whether an agent is following a given mixed strategy in a repeated strategic-form game given samples of the agent's play. This involves two components: determining whether the agent's frequencies of pure strategies are sufficiently close to the target frequencies, and determining whether the pure strategies selected are independent between different game iterations. Our integrated test involves applying a chi-squared goodness of fit test for the first component and a generalized Wald-Wolfowitz runs test for the second component. The results from both tests are combined using Bonferroni correction to produce a complete test for a given significance level $\alpha.$ We applied the test to publicly available data of human rock-paper-scissors play. The data consists of 50 iterations of play for 500 human players. We test with a null hypothesis that the players are following a uniform random strategy independently at each game iteration. Using a significance level of $\alpha = 0.05$, we conclude that 305 (61%) of the subjects are following the target strategy. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.10695&r=ecm |
| By: | Yoosoon Chang (Indiana University); Yongok Choi (Chung-Ang University); Chang Sik Kim (Sungkyunkwan University); J. Isaac Miller (University of Missouri); Joon Y. Park (Indiana University) |
| Abstract: | We employ a semiparametric functional coefficient panel approach to allow an economic relationship of interest to have both country-specific heterogeneity and a common component that may be nonlinear in the covariate and may vary over time. Surfaces of the common component of coefficients and partial derivatives (elasticities) are estimated and then decomposed by functional principal components, and we introduce a bootstrap-based procedure for inference on the loadings of the functional principal components. Applying this approach to national energy-GDP elasticities, we find that elasticities are driven by common components that are distinct across two groups of countries yet have leading functional principal components that share similarities. The groups roughly correspond to OECD and non-OECD countries, but we utilize a novel methodology to regroup countries based on common energy consumption patterns to minimize root mean squared error within groups. The common component of the group containing more developed countries has an additional functional principal component that decreases the elasticity of the wealthiest countries in recent decades. |
| Keywords: | energy consumption, energy-GDP elasticity, partially linear semiparametric panel model, functional coefficient panel model |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:inu:caeprp:2024001&r=ecm |