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The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.

The paper adopts Bayesian VAR models with three different priors – independent Normal-Wishart prior, the Minnesota prior and the stochastic search variable selection (SSVS). Monte Carlo simulations are conducted to compare forecasting performances. An empirical study using US macroeconomic data are shown as an illustration.

In theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data.

The paper finds that iterated forecasts using model with the SSVS prior generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts.

This paper aims to develops an estimable hybrid model that combines the micro‐founded DSGE model with the flexibility of the atheoretical VAR model.

The model is estimated via the maximum likelihood technique based on quarterly data on real gross national product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive estimation using the Kalman filter algorithm, the out‐of‐sample forecasts from the hybrid model are then compared with the forecasts generated from the Classical and Bayesian variants of the VAR for the period 2001:1‐2005:4.

The results indicate that, in general, the estimated hybrid‐DSGE model outperforms the classical VAR, but not the Bayesian VARs in terms of out‐of‐sample forecasting performances.

The model lacks nominal shocks and needs to be extended into a small open economy framework.

The paper was able to show that, even though the DSGE model is outperformed by the BVAR, a microfounded theoretical DSGE model has a future in forecasting the South African economy.

To the best of the authors' knowledge, this is the first attempt to use an estimable DSGE model to forecast the South African economy.

This chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

We test for the presence of non‐linear dynamics in real stock return, in the American, British, and Japanese equity markets. Evidence on non‐linearities will have important implications for financial analysts. The results provide evidence of nonlinear structure in stock returns, in the three markets, suggesting that linear models, such as Ordinary Least Squares or Vector Autoregressive (VAR), may not always be appropriate for analyzing data.

This article examines predictability of returns and volatily in three major stock markets, the U.S., U.K., and Japan, using the Vector Autoregrassive and the Autoregressive Conditional Heteroskedastic (ARCH) approaches. We find that in all three markets dividendprice ratios and/or dividend growth rates predict returns. Moreover, there is persistence in the variance of stock returns attribute to the innovations related to the same variables.

This paper aims to provide a reliable statistical model for time-series prices of short-stay accommodation and overnight stays in a eurozone country.

Exploiting the unit root feature, the cointegrated vector autoregressive model solves the problem of misspecification. Subsequently, variables are modelled for a long-run equilibrium with included deterministic variables.

The empirical results confirmed that overnight stays for foreign tourists were positively associated with the prices of short-stay accommodation.

The major limitation lies in the data vector and its time horizon; its extension could provide a more specific view.

Findings can assist practitioners and hotel executives by providing the information and rationale for adopting seasonal volatility pricing. Structural breaks in price time-series have practical implications for setting seasonal-pricing schemes. Tourists could benefit either from greater price stability or from differentiated seasonal prices, which are important in the promotion of the price attractiveness of the tourist destination.

The originality of the paper lies in the applied unit root econometrics for tourism price time-series modelling and the prediction of short-stay accommodation prices.

Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events.

This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR).

The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts.

Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data.

The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts.

The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.

This study aims to link the financial cooperation in the Nordic region and the interdependence between the stock markets in this area. The main emphasis is placed on the evolution of this interdependence as the financial integration was proceeding.

Johansen’s cointegration technique and the exponential generalized autoregressive conditionally heteroskedastic model are applied to test the long-run and short-run interdependences, respectively, among Nordic stock markets. In particular, the recursive estimation approach is used to reveal the evolution of the interdependence between those markets.

The existence of two cointegrations over the sample period indicates that the markets depend on each other to some extent. The recursive estimation of Johansen’s model further reveals that the interdependence had been greatly improving until late 2008. The interdependence between those markets is also confirmed convincingly by the short-term dynamics, noting that the spillover effects between most pairs of stock volatilities are witnessed in the empirical results.

The findings show the dynamics of the long-run correlations between the Nordic stock markets, which imply the intrinsic response to the process of financial market reforms, the 2008 global financial crisis and the period after the crisis. The evidenced information about determinants of the interdependence between Nordic stock markets is sending strong signals to investors to enhance their investment strategies.

Most of the existing studies have been restricted to the static long-run and/or short-run interdependence among those markets. However, this study contributes to the literature by investigating the dynamics of interdependence among the Nordic stock markets over time; moreover, the evolution of the market interdependence is sketched closely to the process of the regional financial market reforms.

This article uses Granger‐causality tests to study the dynamic relationship between stock returns and dividend yields in the American and Japanese equity markets. The “signaling” hypothesis of dividends along with the efficient market hypothesis is considered to : a) explain the strong relationship between stock returns and the determinants of stock prices, b) show that our results cannot be considered as evidence against market efficiency.

This study examines the relationships between macroeconomic factors and stock prices in Cyprus. Estimating a reduced form Vector Autoregressive model (VAR) we determine Granger causality between stock returns and the predictor variables. We find strong evidence of predictability (which implies inefficiency) in stock returns, which is similar to the pattern observed in developed stock markets. In common with prior studies in this area, we cannot use our results as evidence of market inefficiency or deficiencies in the asset‐pricing model.