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Economic and financial integration (hereafter, economic integration) among economies has been a fertile area of research. Yet, what we argue is that economic integration needs new thoughts to adequately model the recent challenges to the global economy by developing a new index/measure of economic integration. The new index will not only shed invaluable insights into the drivers of economic integration between Australia and the Middle East but will also help craft economic, trade, and commercial policies to achieve the desired type of integration with Australia's trading partners. Our analysis is undertaken on a cross section of 140 countries for the year 2011, to understand the causes and indicators of integration. Our model combines changes in real GDP, per capita GDP, percentage of educational expense, and gender inequality as causal factors to explain integration as a latent variable. We use three indicators of integration: (1) a standard measure of economic integration, (2) exports and imports as a percentage of GDP, (3) flows of foreign direct investment. We then explore the linkages between these indicators, or manifestations of integration, and a number of its possible causes. In terms of the new index we rank 140 nations and note that Australia is ranked among the top 20 nations in terms of integration with the global economy. Except Israel and Oman, Australia's trade partners in the Middle East have little integration with the global economy. In a similar vein, we also find that Australia's northern neighbors – especially Indonesia, Malaysia, Thailand, Cambodia, Myanmar, Sri Lanka, India – are yet to get well-integrated with the global economy. As a result, we argue, Australia can lead these countries from Southeast Asia and the Middle East to form closer ties with the global economy via Australia and, by doing so, Australia can create unprecedented economic and social benefit.

The present study aims to determine the existence of simultaneous relationship between economic growth, income inequality, fiscal policy, and total trade of the 13 emerging market economies as a group for the period 1980–2010. After establishing the existence of simultaneity between the above relationships, a simultaneous panel model has been formulated and estimated incorporating the nonlinearity among the variables as suggested by the existing literature. An inverted U-shape relationship is evident between (1) economic growth, income inequality, and total trade in economic growth equation, (2) income inequality, economic growth, and per capita income in income inequality equation, and (3) total trade and economic growth in total trade equation. Thus, the existence of a two-way nonlinear relationship is highlighted between economic growth, income inequality, and total trade. Apart from these nonlinear relationships, positive and significant effect of (1) gross capital formation, inflation, population growth, human capital, fiscal policy, monetary policy, and domestic credit to private sector on economic growth; (2) civil liabilities on income inequality; (3) gross capital formation and inflation on total trade; (4) total trade, population growth of those aged 65 years and above, political system on fiscal policy is highlighted. Also, negative and significant effect of (1) fiscal policy on income inequality and (2) income inequality on fiscal policy is revealed.

A novel approach to modeling exchange rates is presented based on a set of models distinguished by the drivers of the rate and regime duration. The models are combined into a “meta model” using model averaging and non-nested hypothesis-testing techniques. The meta model accommodates periods of stability and slowly evolving or abruptly changing regimes involving multiple drivers. Estimated meta models for five exchange rates provide a compelling characterization of their determination over the last 40 years or so, identifying “phases” during which the influences from policy and financial market responses to news succumb to equilibrating macroeconomic pressures and vice versa.

We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of UK productivity and US 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.

The purpose of the chapter is to focus on the global industrial robotics market and trends of its development. In the framework of this chapter, the authors made the forecast of industrial robots' market future values in this chapter with the linear regression method and an econometric model. This analysis has provided a conclusive answer to the question about the prospects of the industrial robotics market and the leading countries. The completed forecast showed that the global robotics market will continue to grow, thanks to the wider adoption of industrial robots, which will be used in new industries, the development of contactless user interfaces, which, among other things, will be implemented in the automotive applications, the focus on predictive maintenance and remote monitoring of equipment, as well as the transition of a large number of enterprises to digital management and full automation of existing equipment to improve the quality and productivity of processes. The authors show that in 2020 the global robotics market volume decreased due to the COVID-19 pandemic and major shift in production value chains, but in 2021 the indicator will grow again, but not so rapidly, at a more moderate pace. By 2025, the global industrial robotics market may exceed $61.4 billion, with a growth rate of 8.5%.

We propose a methodology for estimating preference parameters in matching models. Our estimator applies to repeated observations of matchings among a fixed group of individuals. Our estimator is based on the stability conditions in matching models; we consider both transferable (TU) and nontransferable utility (NTU) models. In both cases, the stability conditions yield moment inequalities which can be taken to the data. The preference parameters are partially identified. We consider simple illustrative examples, and also an empirical application to aggregate marriage markets.

The presence of economic power of BRICS nations could be felt from the late of nineteenth and beginning of twentieth century and during this period inflow of FDI also began to go up and spread across all the sectors. FDI has not only looked to capture the huge market of these economies, but while doing so, it has helped these nations in their economics progress. Our main contribution in this paper consists of analyzing both short-run and long-run interactions between status of knowledge and FDI in the form of inflow of FDI and proportion of GDP used for R&D activities accounting for possible development of knowledge in BRICS nations. For this purpose, our work is based on a sample of these five nations during the period 2006–2017. By the help of panel data analysis and having performed all the necessary tests, we have introduced both dynamic OLS and fully modified OLS to get the efficient long-run impact of FDI on knowledge. Our empirical results support long-run and short-run causality running from FDI to knowledge in all BRICS nations. Our policy recommendation includes encouragement of more FDI in development of knowledge-related activities as well as increase in proportion of GDP spent on R&D in BRICS nations.

In this paper, we search to evaluate the systemic risk of the Moroccan banking sector. Indeed, we concentrate on the analysis and the evaluation on transverse dimension of the systemic. From this point of view, two approaches were used. First is based on the estimate on value at risk conditional allowing to measure the systemic importance of each banking institution. In addition, the second approach uses the heteroscedasticity models in order to consider the conditional correlations, making it possible, to measure the dependence between the Moroccan banks and with the whole of the financial system. The results obtained with through these two approaches confirm that ATW, BMCI and the BMCE are the most systemic banks in Moroccan banking system and who can initiate a systemic crisis. On another register and by using the conditional correlations of each bank we built an index of systemic risk. Moreover, a macrofinancial model was developed, connecting the index of the systemic risk and the principal macroeconomic variables. This model affirmed that the contagion dimension of systemic risk is procyclical.

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.

This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.