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Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial‐temporal interconnections between house price movements across the Australian capital cities.

Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects.

The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information.

This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.

This research applies vector autoregression to estimate a system composed of market share and relative advertising expenditures of the seven major competitors in the U. S. replacement passenger tire market between 1972 and 1983. The results of the study suggest that a company's market share in this market cannot be predicted from its relative advertising expenditures.

The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017.

To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments.

In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries.

Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings.

One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting.

Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

Advocates of quantitative easing (QE) policies have emphasized some evidence that structural models do not predict long-term asset yields as well as naive forecasts, implying that predictions of price reversals cannot be profitable and that QE effects are not transitory. The purpose of this study is to reconsider the out-of-sample forecasting performance of structural time series processes relative to that of a random walk with or without drift.

This study uses bivariate vector autoregression and Markov switching representations to generate out-of-sample forecasts of ten-year sovereign bond yields, when the information set is augmented by including the growth rate of the monetary base, and the estimation relies on monthly data from countries that have pursued unconventional policies over the last decade.

The results show that naive forecasts are not better than those of structural time series models, based on root mean squared errors, while the Markov model provides additional information on price reversals, through probabilistic inferences regarding policy regime switches, which can induce agents to counteract QE interventions and reduce their effectiveness.

The novel features of this work are the use of a large information set including the instrument of unconventional monetary policy, the use of a structural model (Markov process) that can really inform about potential asset price reversals and the use of a large sample over which QE policies have been pursued.

The purpose of this paper is to identify the bull and bear regimes in Chinese stock market and empirically analyze the dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform.

The authors investigate the price-volume relationship in the Chinese stock market before and after the Split Share Structure Reform using Shanghai Composite Index daily data from July 1994 to April 2013. Using a two-state Markov-switching autoregressive model and a modified two-state Markov-switching vector autoregression model, this study identifies bull or bear market and also examine the existence of regime-dependent Granger causality.

Using a two-state Markov-switching autoregressive model, the authors detect structural changes in the market volatility due to the reform, and find evidence of a positive rather than an asymmetric price-volume contemporaneous correlation. There is a strong dynamic Granger causal relation from stock returns to trading volume before and after the reform regardless of the market conditions, but the causal effects of volume on returns are only seen in the bear markets before the reform. The model is robust when using different stock indices and time periods.

The work is different from previous studies in the following aspects: most of the existing empirical literature focus on the well-developed economies, but our interest lies in the emerging Chinese market that has witnessed rapid growth in the past decade; in contrast to many works in the literature that examine the price-volume relationship during one market condition, the authors compare the relationship in a bull market with that in a bear market, using a two-state MS-AR model; the authors also employ a modified two-state Markov-switching vector autoregression model to examine the existence of regime-dependent Granger causality; as the most massive systematic reform for the Chinese stock market since its inception in 2005, the Split Share Structure Reform has a profound impact on the Chinese stock market, thus it is of vital importance to explore its effects on both the price-volume relationship and the market structure.

This paper aims at examining the co-movement dependent regime and causality relationships between conventional and Islamic returns for emerging, frontier and developed markets from November 2008 to August 2020.

First, the authors used the Markov-switching autoregression (MS–AR) model to capture the regime-switching behavior in the stock market returns. Second, the authors applied the Markov-switching regression and vector autoregression (MS-VAR) models in order to study, respectively, the co-movement and causality relationship between returns of conventional and Islamic indexes across market states.

Results show the presence of two different regimes for the three studied markets, namely, stability and crisis periods. Also, the authors found evidence of a co-movement relationship between the conventional and Islamic indexes for the three studied markets whatever the regime. For the Granger causality, it is proved only for emerging and developed markets and only during the stability regime. Finally, the authors conclude that Islamic indexes can act as diversifiers, or safe-haven assets are not strongly supported.

This paper is the first study that examines the co-movement and the causal relationship between conventional and Islamic indexes not only across different financial markets' regimes but also during the COVID-19 period. The findings may help investors in making educated decisions about whether or not to add Islamic indexes to their portfolios especially during the recent outbreak.

This study aims to investigate the existence of contagion between liquid and illiquid assets in the credit default swap (CDS) market around the recent financial crisis. The authors perform analyses based on vector autoregression model and the dynamic conditional correlation model. The estimation of vector autoregression models reveals that changes in liquid CDS (LCDS) spreads lead to changes in illiquid CDS spreads at least one week ahead during the financial crisis period, whereas the leading direction is reversed during the post-crisis period. Moreover, the results are robust after controlling for structural variables which are proven as determinants of CDS spreads and are empirically supported. This study interprets that information was incorporated first into the LCDSs because of the flight-to-liquidity during the recent crisis period but there is a default contagion effect by reflecting illiquidity-induced credit risk after the crisis. Finally, the dynamic conditional correlation analysis also confirms the main results.

This study examines the dynamic responses of five different daily energy prices to a pulse shock affecting the daily price of oil.

Daily data for energy prices from the Federal Reserve Economic Data (FRED) database for January 7, 1997, through February 8, 2021, are analyzed. A bivariate structural vector error correction model and generalized autoregressive conditionally heteroscedastic model are combined and extended by adding the volatility of the growth rate of daily oil prices as an explanatory variable for the growth rates of energy prices. This model is estimated and used to generate impulse responses for energy prices.

The empirical results show that the levels of the daily energy prices examined have unit roots, are integrated of order one, are cointegrated, and generally revert slowly to their long-term equilibrium relationships with the price of oil. The growth rates for the daily energy prices have autoregressive conditional heteroscedasticity, generally are positively related to the volatility of daily oil prices, respond quickly to a pulse shock to daily oil prices, and have cumulative responses that last at least one month.

This paper allows for simultaneous estimation of extended bivariate structural vector error correction and generalized autoregressive conditionally heteroscedastic models that include the volatility of oil as an explanatory variable and uses these models to generated cumulative impulse responses for the growth rates of daily energy prices to oil price shocks.