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Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

The purpose of this paper is to present forecasts of fossil fuels prices until 2030 with spectral analysis to provide a clearer picture of this energy sector.

Fossil fuels prices time series are decomposed in simpler signals called approximations and details in the framework of the one-dimensional discrete wavelet analysis. The simplified signals are recomposed after Burg extension.

In 2019-2030 average price forecasts of: West Texas intermediate (WTI) oil ($58.67) is above its 1986-2030 long-term mean of $47.83; and coal ($81.01) is above its 1980-2030 long-term mean of $60.98. On the contrary, 2019-2030 average of price forecasts of: Henry Hub natural gas ($3.66) is below its 1997-2030 long-term mean of $4; heating oil ($0.64) is below its 1986-2030 long-term mean of $1.16; propane ($0.26) is below its 1992-2030 long-term mean of $0.66; and regular gasoline ($1.45) is below its 2003-2030 long-term mean of $1.87.

Fossil fuels prices projections may relieve participants of WTI oil and coal markets but worry participants of Henry Hub, heating oil, propane and regular gasoline markets including countries whose economy is tied to energy prices.

Studies about the determinants of the clarity of central bank communication are still scarce. To the authors’ knowledge, there are no studies regarding emerging economies. The purpose of this paper is to contribute to the literature in the following aspects: to analyze the determinants of the clarity of the central bank communication in an inflation targeting emerging economy; observe the influence of inflation volatility over the clarity; and observe the effect of the monetary policy signaling over the clarity.

The work uses readability indexes to measure the clarity of central bank communication. The empirical analysis uses ordinary least squares and the Generalized Method of Moments with one- and two-step estimations.

The findings suggest the inflation volatility reduces the clarity of central bank communication. Moreover, the monetary policy signaling also affects the clarity, but the effect depends on the direction of the signal.

This paper observes the determinants of the clarity considering an emerging economy environment. The clarity of central bank communications is an important tool to access transparency. Hence, the analysis of what determines the clarity of central bank communication is a debate about the level of transparency accessed by the central bank.

There are no studies about the determinants of the clarity of central bank communication in emerging economies. Moreover, the novelty are the effects of inflation volatility and monetary policy signaling over the clarity.

Broker-dealer leverage volatility increases during booms and crisis periods, but its impact on stock prices is relatively unexplored. This paper aims to investigate whether broker-dealer leverage volatility is a key driver for stock prices.

This paper collects the US quarterly data of broker-dealer book leverage and three leading stock market indicators (S&P 500, DJIA and Nasdaq) for the period of 1967–2018. The research uses a multivariate GARCH-in-mean VAR to examine the impact of leverage volatility on each of the stock market indicators. A split-sample analysis (pre-1990 and post-1990) has also been performed to show the robustness of the result.

The research finds that broker-dealer leverage volatility does not have any significant impact on stock prices.

Broker-dealers are important financial intermediaries, and there is a huge literature exploring the relationship between their leverage and asset prices. But, the relationship between broker-dealer leverage volatility and asset prices is not explored yet. This study fills the gap and provides the first evidence that broker-dealer leverage volatility does not play any major role in the theory of stock pricing. The research proposes that the stock holding decisions of the investors should depend only on the first moment of leverage and not on the second moment of leverage. The study concludes that high broker-dealer leverage volatility is not a sinister signal for the US stock market.

The purpose of this paper is to establish a three‐dimensional service house of quality (HOQ). The new service HOQ adds a dimension of quality economics to solve the problems of economic evaluation in the process of transferring customer requirements into service characteristics by traditional HOQ.

Based on the traditional two‐dimensional HOQ, this paper constructs a three‐dimension service HOQ by adding an economic dimension into the traditional structure, so that the transformation process from customer requirements into service characteristics can be evaluated with quality economic perspective. The key concern of this new model is to balance the quality improvement and economic gain of a service. The other improvement of this paper is that it uses structural equations to present the coefficient matrix in the new HOQ model to avoid human errors in the evaluation. A case study is used to verify the effectiveness of the new model.

Quality gains and costs should be considered in service design and quality improvement. The three‐dimensional service HOQ uses the dimension of quality economics to balance customer requirements and service characteristics, which is more effective than the traditional one.

The method exposed in the paper can be used by service companies for decision making in service design and quality improvement.

This paper establishes a new three‐dimensional HOQ, by which quality economics can be effectively analyzed in service design and quality improvement.

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

The objective of the paper is to explore the out-of-sample forecasting connections in income growth across the globe.

An autoregressive distributed lag (ARDL) framework is employed and the forecasting performance is analyzed across several horizons using different forecast combination techniques.

Results show that the foreign country's income provides superior forecasts beyond what is provided by the country's own past income movements. Superior forecasting power is particularly held by Belgium, Korea, New Zealand, the UK and the US, while these countries' income is rather difficult to predict by global counterparts. Contrary to conventional wisdom, improved forecasts of income can be obtained even for longer horizons using our approach. Results also show that the forecast combination techniques yield higher forecasting gains relative to individual model forecasts, both in magnitude and the number of countries.

The forecasting paths of income movement across the globe reveal that predictive power greatly differs across countries, regions and forecast horizons. The countries that are difficult to predict in the short run are often seen to be predictable by global income movements in the long run.

Even while it is difficult to predict the income movements at an individual country level, combining information from the income growth of several countries is likely to provide superior forecasting gains. And these gains are higher for long-horizon forecasts as compared to the short-horizon forecast.

In evaluating the forward-looking social implications of economic policy changes, the policymakers should also consider the possible global forecasting connections revealed in the study.

Employing an ARDL model to explore global income forecasting connections across several forecast horizons using different forecast combination techniques.

Managers are constantly making decisions that affect profit. One of the decision‐making areas which is crucial to all managers concerns profit planning. Attempts to show how cost‐volume‐profit (CVP) analysis, aided by the computer spreadsheet, can be applied to the practical profit planning situation in the hospitality industry. Paradoxically, CVP analysis is one of the most widely referred to techniques in managerial accounting, but all too often it is not used to its full potential in the operating environment. Aims at encouraging greater use of the CVP approach to hospitality profit planning.

The purpose of this paper is to estimate the years the European Muslim population will be majority among 30 European countries.

The methodology/approach is to forecast the population of 30 European countries with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson–Durbin recursion, then relies on an infinite impulse response prediction error filter. Three scenarios are considered: the zero-migration scenario where the authors assume that the Muslim population has a higher fertility (one child more per woman, on average) than other Europeans, mirroring a global pattern; a 2017 migration scenario: to the Muslim population obtained in the zero-migration scenario, the authors add a continuous flow of migrants every year based on year 2017; the mid-point migration scenario is obtained by averaging the data of the two previous scenarios.

Among three scenarios, the most likely mid-point migration scenario identifies 13 countries where the Muslim population will be majority between years 2085 and 2215: Cyprus (in year 2085), Sweden (2125), France (2135), Greece (2135), Belgium (2140), Bulgaria (2140), Italy (2175), Luxembourg (2175), the UK (2180), Slovenia (2190), Switzerland (2195), Ireland (2200) and Lithuania (2215). The 17 remaining countries will never reach majority in the next 200 years.

The growing Muslim population will change the face of Europe socially, politically and economically. This paper will provide a better insight and understanding of Muslim population dynamics to European governments, policymakers, as well as social and economic planners.

The purpose of the current paper is to analyze the simultaneous effects of oil sanctions and financial sanctions on Iran's macroeconomic variables in a small open economy in the dynamic stochastic general equilibrium (DSGE) framework.

A DSGE model with the new Keynesian approach has been designed for the above mentioned purpose giving consideration to households, production, trade, oil, government and central bank sectors. All of the parameters were calibrated by using geometric means of macroeconomic variables in 2004–2017 as the steady-state values of the variables in the static model.

Amplifying the intensity of the oil sanctions reduces oil production due to decreasing investment, technology and export of oil and reduces the central bank's foreign reserves ratio to the money base that leads to an increasing exchange rate. Furthermore, oil sanctions decrease the government revenues due to a decrease in oil export and by the government imposing an expansionary fiscal policy in the form of increasing current expenditure and preserving construction expenditure to prevent deepening the recession, which causes budget deficit and then the issue of more bonds with a higher nominal interest rate. On the other hand, financial sanctions raise transaction costs and marginal costs in the trade sectors that lead to inflation and a decrease in nonoil export and various kinds of imports. Due to inflation and uncertainty, consumption of a household increases and investment expenditure of a household decreases.

To the best of the author's knowledge, few studies in the world have analyzed the economic effect of the sanctions in the framework of DSGE models. There is no study in Iran to date which investigates the effects of the sanctions in the form of a DSGE model. So, this paper is the first study in Iran and one of the few studies in the world using a DSGE model for analyzing the effects of sanctions. Imposing three kinds of oil sanctions in addition to a financial sanction is another innovation of the current paper.