2,438 research outputs found
Simulation, Estimation and Selection of Mixed Causal-Noncausal Autoregressive Models: The MARX Package
No full textThis paper presents the MARX package for the analysis of mixed causal-noncausal autoregressive processes with possibly exogenous regressors. The distinctive feature of MARX models is that they abandon the Gaussianity assumption on the error term. This deviation from the Box-Jenkins approach allows researchers to distinguish backward (causal) and forward-looking (noncausal) stationary behavior in time series (see e.g. Hecq et al., 2016, for an overview). The MARX package offers functions to simulate, estimate and select mixed causal-noncausal autoregressive models, possibly including exogenous regressors. The procedures for this are discussed in Hecq et al. (2016) for the MAR, and Hecq et al. (2017) for the MARX respectively
Reduced Rank Regression Models in Economics and Finance
No full textReduced rank regression (RRR) has been extensively employed for modelling economic and financial time series. The main goals of RRR are to specify and estimate models that are capable of reproducing the presence of common dynamics among variables such as the serial correlation common feature and the multivariate autoregressive index models. Although cointegration analysis is likely the most prominent example of the use of RRR in econometrics, a large body of research is aimed at detecting and modelling co-movements in time series that are stationary or that have been stationarized after proper transformations. The motivations for the use of RRR in time series econometrics include dimension reductions, which simplify complex dynamics and thus make interpretations easier, as well as the pursuit of efficiency gains in both estimation and prediction. Via the final equation representation, RRR also makes the nexus between multivariate time series and parsimonious marginal ARIMA (autoregressive integrated moving average) models. RRR’s drawback, which is common to all of the dimension reduction techniques, is that the underlying restrictions may or may not be present in the data
Detecting Common Bubbles in Multivariate Mixed Causal-Noncausal Models
No full textThis paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal–noncausal vector autoregressive models, we suggest statistical tools to detect the common locally explosive dynamics in a Student t-distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria were investigated in a Monte Carlo study. Finally, we evaluated the practical value of our approach via an empirical application on three commodity prices
Measuring the Sources of Cyclical Fluctuations in the G7 Economies.
No full textWe analyze herein the importance of four types of shocks in contributing to the business cycles of the G7 economies. After disentangling the common permanent and transitory shocks in the G7 outputs, we identify the domestic and foreign components of such shocks for each country. This provides us with quite a flexible palette for understanding the degree of openness of the G7 countries, useful information for the analysis of the strengths and weaknesses of each national economy. Our empirical analysis reveals that the cycles of most of the G7 outputs are dominated by their domestic components and that the foreign components are almost entirely due to permanent [email protected]
Testing for common autocorrelation in data‐rich environments
No full textThis paper proposes a strategy to detect the presence of common serial cor- relation in large‐dimensional systems. We show that partial least squares can be used to consistently recover the common autocorrelation space. Moreover, a Monte Carlo study reveals that univariate autocorrelation tests on the factors obtained by partial least squares outperform traditional tests based on canonical correlation analysis. Some empirical applications are presented to illustrate concepts and methods. Copyright (C) 2010 John Wiley & Sons, Ltd.serial correlation common feature , high‐dimensional systems , partial least squares , reduced‐rank regression ,
Mr Alain Elkann Author and Journalist Italian Republic
No full textVisit by Mr Alain Elkann Author and Journalist Italian Republi
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