Search results for "Vector autoregression"
showing 10 items of 15 documents
Housing market shocks in italy: A GVAR approach
2020
Abstract In this paper, we use a Global Vector Autoregression (GVAR) model to assess the spatio-temporal mechanism of house price spillovers, also known as “ripple effect”, among 93 Italian provincial housing markets, over the period 2004 − 2016 . In order to better capture the local housing market dynamics, we use data not only on house prices but also on transaction volumes. In particular, we focus on estimating, to what extent, exogenous shocks, interpreted as negative housing demand shocks, arising from 10 Italian regional capitals, impact on their house prices and sales and how these shocks spill over to neighbours housing markets. The negative housing market demand shock hitting the G…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Impact of COVID-19 on the travel and tourism industry.
2021
Abstract Our paper is among the first to measure the potential effects of the COVID-19 pandemic on the tourism industry. Using panel structural vector auto-regression (PSVAR) (Pedroni, 2013) on data from 1995 to 2019 in 185 countries and system dynamic modeling (real-time data parameters connected to COVID-19), we estimate the impact of the pandemic crisis on the tourism industry worldwide. Past pandemic crises operated mostly through idiosyncratic shocks' channels, exposing domestic tourism sectors to large adverse shocks. Once domestic shocks perished (zero infection cases), inbound arrivals revived immediately. The COVID-19 pandemic, however, is different; and recovery of the tourism ind…
Real wages-employment relationship in Finnish manufacturing: a VAR approach
1991
Granger's concept of causality and the vector autoregressive(VAR) technique is used to investigate the real wages-employment relationship in Finnish manufacturing. The stationarity of the time series is examined and a number of co-integration tests for the adequacy of a pure VAR specification performed. The results using a bivariate VAR model based on a lag structure determined by Akaike's information criterion suggests that real wages Granger-cause employment. The slight non-constancy of the model suggests, however, that the conclusion concerning the nature of the real wages-emploment relationship should be treated with causion.
Local Granger causality
2021
Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear …
Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate va…
2021
Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of ‘instantaneous’ effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index…
Assessing commodity price risks and terms of trade exposures in emerging and developing countries
2020
This paper provides novel evidence on commodity exposure (impacts of commodity price and terms of trade fluctuations) amongst 46 emerging and developing countries (EMDCs) in Africa, Asia and the Latin American and Caribbean (LAC) region. We focus on the exposures of six macroeconomic variables to the commodity prices and terms of trade, based on the real business cycle (RBC) theory. Our empirical results indicate that, overall, about 10% of the macroeconomic variation amongst the EMDCs is due to commodity market-related exposures. The Asian and LAC economies are especially sensitive to changes in commodity prices. The changes in the prices of world trade have an imminent impact on non-commo…
Has 1997 Asian crisis increased information flows between international markets
2003
Abstract The Asian crisis started on July 2, 1997 and caused turmoil in developed as well as emerging international stock markets. The objective of this paper is to analyse the effects of the crisis on the relationships of the Southeast Asian stock markets with the stock markets of three geographical areas (Europe, North America, and Latin America). We use the Morgan Stanley national and international indexes (MSCI) for two homogeneous and nonoverlapping time intervals. The econometric techniques used in this paper include the cointegration test, vector autoregression analysis, forecast error variance decomposition (FEVD), and impulse–response relationships. Our results show that: (i) there…
The beer market and advertising expenditure
2009
PurposeThe purpose of this paper is to examine the impacts of advertising expenditure on brands' market shares, utilizing a novel four‐week advertising‐sales data from the highly competitive oligopolistic Finnish beer market in which price competition among the homogeneous larger‐type beer brands is not allowed during the period of the study.Design/methodology/approachCompetition is modelled using the Lanchester model. The impacts of advertising on market shares are estimated using the impulse‐response functions from vector autoregression, and the full information maximum likelihood and advertising elasticities.FindingsSome new insights into beer market dynamics are obtained. First, the imp…
How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study
2019
Abstract This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is pref…