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Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.

The investment development path of emerging markets’ MNEs is significantly different from the developed (TRIAD) world’s MNEs; BRIC MNEs seem to have taken a different trajectory on account of various political and economic reasons, ranging from the ‘forms of entry’ to ‘country-specific advantages’ (Tulder, R. V. (2010). Toward a renewed stages theory for BRIC multinational enterprises? A home country bargaining approach. In K. P. Sauvant, G. McAllister, & W. A. Maschek (Eds.), Foreign direct investments from emerging markets: The challenges ahead (pp. 61–74). New York, NY: Palgrave Macmillan). Yet, some believe that in the long run the internationalization strategy of the developed world MNEs and BRIC MNEs will converge. Internationalization strategies as measured by OFDI depend on various macroeconomic determinants such as income, interest rate, openness of the economy, etc. The chapter intend to highlight, the significant difference between these two groups of countries on account of diverse political reforms towards internalization of firms, yet see if these different countries might converge.

Regression analysis examines the significance of the role of home government by testing the effect of governance indicators; that is voice and accountability, on OFDI. It further, tests for convergence of internationalization strategies of the two historically divergent groups, also, it tests convergence amongst the BRIC nations. Along with forecasting, time series analysis is also employed to examine convergence using univariate sigma convergence techniques.

Impact of voice and accountability is significant but it hinders OFDI for BRIC nations, while it promotes OFDI for TRIAD & ALL. Moreover, the analysis found the existence of convergence, that is BRIC will catch up with TRIAD, but though convergence exists amongst BRIC if we take a long span of time (45 years), it is absent in short span of time (19 years), as lately BRIC have shown divergent tendency.

Small sample size in multivariate regression analysis. Also, the governance indicator, that is voice and accountability, is perception based, and missing gaps in data for governance indicator is filled using interpolation.

Empirically testing the convergence of BRIC nations with the developed world. A univariate time series analysis is undertaken to understand each country’s heterogeneous FDI outflows and to address the research gap in existing forecasting literature. In addition, the comparison specifically between the Emerging Market Economies, that is the BRIC nations and the developed world gives some useful insights. This chapter ascertains the impact of governance indicator on OFDI; empirical literature shows such analysis for IFDI & FDI, but OFDI is rarely been dealt with.

Commercial real estate and office rental values, in particular, have long been the focus of research. Several forecasting frameworks for office rental values in multivariate and univariate fashions have been proposed. Recent developments in time series forecasting using machine learning and deep learning methods offer an opportunity to update traditional univariate forecasting frameworks.

With the aim to extend research on univariate rent forecasting a hybrid methodology combining both ARIMA and a neural network model is proposed to exploit the unique strengths of both methods in linear and nonlinear modelling. N-BEATS, a deep learning algorithm that has demonstrated state-of-the-art forecasting performance in major forecasting competitions, are explained. With the ARIMA model, it is jointly applied to the office rental dataset to produce forecasts for four-quarters ahead.

When the approach is applied to a dataset of 21 major European office cities, the results show that the ensemble model can be an effective approach to improve the prediction accuracy achieved by each of the models used separately.

Real estate forecasting is essential for assessing the value of managing portfolios and for evaluating investment strategies. The approach applied in this paper confirms the heterogeneity of real estate markets. The application of mixed modelling via linear and nonlinear methods decreases the uncertainty of abrupt changes in rents.

To the best of the authors' knowledge, no such application of a hybrid model updating classical statistical forecasting with a deep learning neural network approach in the field of commercial real estate rent forecasting has been undertaken.

The purpose of this study is to examine the accuracy of combined models with the individual models in terms of forecasting Euro against US dollar during COVID-19 era. During COVID, the euro shows sharp fluctuation in upward and downward trend; therefore, this study is keen to find out the best-fitted model which forecasts more accurately during the pandemic.

The descriptive design has been adopted in this research. The three univariate models, i.e. autoregressive integrated moving averages (ARIMA), Naïve, exponential smoothing (ES) model, and one multivariate model, i.e. nonlinear autoregressive distributive lags (NARDL), are selected to forecast the exchange rate of Euro against the US dollar during the COVID. The above models are combined via equal weights and var-cor methods to find out the accuracy of forecasting as Poon and Granger (2003) showed that combined models can forecast better than individual models.

NARDL outperforms all remaining individual models, i.e. ARIMA, Naïve and ES. By applying a combination of different models via different techniques, the combination of NARDL and Naïve models outperforms all combination of models by scoring the least mean absolute percentage error value, i.e. 1.588. The combined forecasting of NARDL and Naïve techniques under var-cor method also outperforms the forecasting accuracy of individual models other than NARDL. It means the euro exchange rate against the US dollar which is dependent upon the macroeconomic fundamentals and recent observations of the time series.

The findings could help the FOREX market, hedgers, traders, businessmen, policymakers, economists, financial managers, etc., to minimize the risk indulged in global trade. It also helps to produce more accurate results in different financial models, i.e. capital asset pricing model and arbitrage pricing theory, because their findings may not be useful if exchange rate fluctuations do not trace effectively.

The NARDL models have been applied previously in different time series and only limited to the asymmetric or symmetric relationships. This study is using it for the forecasting exchange rate which is almost abandoned in earlier literature. Furthermore, this study combined the NARDL with univariate models to produce the accuracy which itself is a novelty. Moreover, the findings help to enhance the effectiveness of different financial theories as well.

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

Tests for correlation between the NCREIF (NC) Index and EREIT Index. A multiple time series methodology is used to control for spurious correlation, allow for leading and lagging relationships, and to control for autoregressive moving average processes found in the time series. The underlying variables generating returns for the investor, current cash flow and capital appreciation, are analysed separately. Significant correlation is found between the NC cash flows and EREIT dividends. Significant correlation is not observed between the NC portfolio and EREIT when capital values are analysed. Suggests that one or both series are not a good measure of real estate returns.

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

The purpose of this study is to contribute to the relatively narrow existing residential real estate literature by developing and validating several univariate forecasting models, to reliably anticipate future house price dynamics across several European Union (EU) countries.

The research approach relies on the time series analysis, by using the Box–Jenkins autoregressive integrated moving average (ARIMA) methodology to explore the trends of residential property prices in selected EU countries and to obtain a snapshot of the potential signs of change to be witnessed by domestic residential markets on a short time-period. The analysis has been performed distinctly for each country in the sample, to account for country-specific past and future trends as well as similarities in their house price growth rate evolutions. The models were estimated for a broad sample of quarterly observations during 1990-2015, while the forecast horizon ranged between the third quarter of 2015 and the fourth quarter of 2016.

The findings suggested that residential property prices’ real growth rate can be modeled through the Box–Jenkins method for France, The Netherlands, Sweden and UK. The pattern of Italy’s residential property prices’ real growth rate cannot be explained by means of univariate ARIMA models, being more suited for multivariate models.

The article subscribes to the need for timely, high-frequency and quality data about house price trends in Europe, to increase the accuracy of forecasts and prevent the appearance of bubbles on real estate market. It compares residential property prices’ dynamics across European countries to identify housing markets with similar patterns of their prices.

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.