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This paper aims to examine the integration of housing markets in Canada by examining housing price data (1999–2016) of six metropolitan areas in different provinces, namely, Calgary, Vancouver, Winnipeg, Toronto, Montreal and Halifax. The authors test for cointegration, driver cities of long-run relationships, long-run Granger causality and instantaneous causality in light of the global financial crisis (GFC) (2007–2008).

The authors use Johansen’s system cointegration approach with structural breaks. Moving average representation is used for common stochastic trend(s) analysis. Finally, the authors apply vector error correction model-based Granger causality and instantaneous causality.

Cities’ housing prices are in long-run equilibrium. Post-crisis Canadian housing markets became more integrated. The Calgary, Vancouver, Toronto and Montreal markets drive the Canadian housing market, leading all cities toward long-run equilibrium. Strong long-run Granger causality exists, but the authors observe no instantaneous causality. Price information takes time to disseminate, and long-run price adjustments play a significant role in causation.

The findings of cointegration increasing after the GFC and strong lead–lag can be used by investors to arbitrage and optimize portfolios. This can also help national and local policymakers in mitigating risk. Incorporating these findings can lead to better price forecasting.

This study presents many novelties for the Canadian housing market: it is the first to use repeat-sales regional pricing indices to test long-run behaviors, conduct common stochastic trend analyzes and present causality relations.

We investigate the convergence of Chinese real GDP per capita at regional and provincial levels, looking separately at the sub‐periods before and after major economic reforms and paying attention to the possibility of structural breaks induced by the ‘Great Leap Forward’. At the regional level we reject convergence pre‐ and post‐reform. At the provincial level we find evidence of a common regional trend for the Eastern region and again for the Central region, but not for the Western region. We conclude that, contrary to the policy objectives of the Chinese government, the regions of China have not shared a common development path.

We analyze the interaction among the common and country-specific components for the inflation rates in 12 euro area countries through a factor model with time-varying parameters. The variation of the model parameters is driven by the score of the predictive likelihood, so that, conditionally on past data, the model is Gaussian and the likelihood function can be evaluated using the Kalman filter. The empirical analysis uncovers significant variation over time in the model parameters. We find that, over an extended time period, inflation persistence has fallen and the importance of common shocks has increased relatively to that of idiosyncratic disturbances. According to the model, the fall in inflation observed since the sovereign debt crisis is broadly a common phenomenon since no significant cross-country inflation differentials have emerged. Stressed countries, however, have been hit by unusually large shocks.

The aim of the study is to utilize cointegration techniques and analyze the degree of linkages among four key property types (retail, office, industrial, and residential) of eight major countries throughout North America and Europe. Additionally, the study evaluates whether investors can attain greater diversification benefits by investing across specific property sectors within their own nations in the long‐run. Finally, the study examines whether certain property sectors can be considered the “leader” that drives the remaining sectors over time.

Multivariate cointegration tests developed by Johansen and Johansen and Juselius are utilized to evaluate whether long‐run equilibrium relationship(s) exist among the four property sectors. If evidence of cointegration is found, hypothesis tests are implemented to separate out the markets that can be excluded from the cointegrating relationships and to identify the markets that are the sources of the common trends (weakly exogenous), respectively.

Long‐run cointegration results indicate that the four property sectors of the USA, Canada, Netherlands, and the UK have fully converged implying limited diversification possibilities. The property sectors of Finland, France, Germany and Sweden, however, have only partially converged. Further analysis reveals that for these four countries, the industrial sectors provide the greatest long‐run diversification benefits. Finally, weak exogeneity tests indicate that for an overwhelming majority of the countries under consideration, the residential sectors are the sources of the common stochastic trends, that “lead” the remaining property types towards the long‐run equilibrium relationships.

The conclusions from this study should be beneficial to investors, portfolio managers, pension fund managers and other institutional investors in the USA and abroad who are contemplating to invest across property sectors within their own countries in making more informed portfolio allocation decisions. The findings also highlight the importance of implementing time‐series econometric techniques to accurately and appropriately model interactions among property sectors over time.

This is one of the few studies that utilize modern‐day timeseries techniques to analyze the dynamic interactions among the property sectors of eight major nations throughout North America and Europe. Prior studies, have been limited to modeling interrelationships between the property sectors of the USA and UK, with little attention given to other major real estate markets.

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

The purpose of this paper is to explore dynamics of stock price movements of an emerging market, Bangladesh with that of USA, Japan and India.

The long‐term relationships among the markets are analyzed using the Johansen and Juselius multivariate cointegration approach. Short‐run dynamics are captured through vector error correction models. Further investigation on short‐run dynamics is carried out through impulse response analysis.

There is evidence of cointegration among the markets demonstrating that stock prices in the countries studied here share a common stochastic trend. Impulse response analysis shows that shocks to the US market do have an impact on the Bangladesh market. The evidence of Bangladesh stock market responding to shocks in the Indian market is weak. Shocks to the Japanese market do not generate a response in the Bangladesh market.

As these markets share a common stochastic trend no diversification benefit is possible from cross‐border investments. Investors could further enhance their understanding of market behaviour by comparing the observations here with those of studies that adopt technical analysis, fundamental analysis and consider financial anomalies.

The evidence of cointegration and the short run dynamic relationship help investors in making efficient investment decisions in the Bangladesh stock market.

The purpose of this paper is to examine financial market integration in major South Asian financial markets: Bangladesh, India, Pakistan and Sri Lanka. Also to identify the required policy interactions and structural changes vital for broader economic integration.

This research opted for an empirical study employing co‐integration and causality techniques using a sample of stock and bond market data for major South Asian countries.

Empirical results show that both stock and bond returns are co‐integrated, indicating common stochastic trends. Stock market integration appears to be much stronger compared to the less developed and data deficient bond markets.

The study relies on widely cited empirical methodology. However, adopting alternative specifications and also allowing for time variant factors while examining inter‐linkages between stock and bond markets seem to be appropriate for robustness of results.

Increased integration would help in reducing arbitrage opportunities in these financial markets, having implications for market participants and promoting economic growth through financial deepening, in general. Since the degree of integration is dependent on policy and institutional infrastructure, ongoing efforts to develop financial sectors and reforms would need to be accelerated to further strengthen the degree of convergence between securities markets.

The paper fulfills an identified need to examine financial market integration in the SAARC region, using data for both stock and bond markets. This is the first study to use bond market data for SAARC countries and it also adds to the limited literature of bond market integration.

The purpose of this paper is to re‐examine the effect of population ageing on private saving, taking into account the fact that ageing is brought about by not only rising old‐aged dependency but also expanding longevity.

The study uses panel data of 22 OECD countries from 1961 to 2010. Linear and non‐linear panel regression methods are used. The study takes into account the time series characteristic of the data, such as the deterministic trend present in old‐age dependency ratio.

Longevity consistently has a significant positive impact on savings, while old‐aged dependency rate has no discernible impact once country‐specific time trends in the data are accounted for. The general finding within the literature where old‐age dependency exerts a negative impact on savings is sensitive to the manner in which the data is handled and/or the sample selected.

First, the authors jointly consider rising old‐aged dependency and expanding longevity on savings, thus avoiding potential omitted variable bias in previous studies. Second, they explore non‐linearity in the savings‐ageing relationship which was ignored previously. Third, they identify whether saving rate and demographic measures are sharing common stochastic trends or driven by individual deterministic trends to avoid spurious regression results.