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The aim of this chapter is to delineate a product framework concerning managed visitor attractions (MVA), which highlights the supply-chain aspects of destinations. It first touches on the rationales for developing such a framework and then constructs a framework composed of a set of product components deriving from the extant literature. Consequently, an version of a product component framework, fastening on an accumulated sample of attraction cases, is presented through three illustrative cases. In the conclusion section, this study elaborates on the study limitation while connoting how the resultant data could shed light on the role of the components of the MVA product in the creation of visitor experiences.

This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.

Labor productivity always plays an important part in the growth of manufacturing sector of any nation, and certainly, in the growth of GDP as well. Now, the relationship between these three variables has been of interest to the researchers because few nations have experienced labor productivity–led economic growth, whereas for others it has been the other way round. In this chapter we have studied the relationship between labor productivity, manufacturing output, and growth of GDP, for 25 major economies across the globe, covering the period 2000–2015, with the help of simultaneous equation system for individual nations as well as panel data analysis, covering all the nations together. Study of this relationship has, hardly, been done before which is our prime motivation behind the study. Our findings suggest that in most of the nations, these variables have significant impact on one another but there are exceptions as well. Apart from that, there are variables like energy consumption, health status, life expectancy, foreign direct investment, etc., which are significant in influencing these variables. So, policy measure suggests that importance should be given not only on labor productivity or output of manufacturing sector but also on factors that can influence these variables.

Claims and disputes are often unavoidable in the construction industry due to its unique and complex characteristics involving the massive investment of capital, lengthy project duration, and multiple project stakeholders. This chapter intends to identify the critical construction claims attributes, compare the perceptions of major stakeholders on different claim attributes, and investigate the contrast of the top five claim attributes between this study and previous ones. The literature review resulted in 48 claim attributes responsible for the construction project schedule delays. These attributes were then presented to Vietnam construction industry (VCI) practitioners in the form of a questionnaire survey. Data analysis was done based on the collected 113 qualified samples. Relative importance index (RII) was applied to determine the ranking of claim attributes. The results were that the top five causes of claims, that is, payment delays, mistakes by contractor during construction stage, delays in work progress by the contractor, financial failure of the contractor, and frequently changing requirements by the owner, lead to the schedule delays in VCI. These findings can assist the local industry practitioners and foreign companies seeking a share in the VCI market in understanding the causes of construction claims comprehensively and formulating the countermeasures to minimise their impacts and hence reduce the unnecessary losses and raise the likelihood of success as well as maintain sustainable relationships among stakeholders.

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

IV estimation is examined when some instruments may be invalid. This is relevant because the initial just-identifying orthogonality conditions are untestable, whereas their validity is required when testing the orthogonality of additional instruments by so-called overidentification restriction tests. Moreover, these tests have limited power when samples are small, especially when instruments are weak. Distinguishing between conditional and unconditional settings, we analyze the limiting distribution of inconsistent IV and examine normal first-order asymptotic approximations to its density in finite samples. For simple classes of models we compare these approximations with their simulated empirical counterparts over almost the full parameter space. The latter is expressed in measures for: model fit, simultaneity, instrument invalidity, and instrument weakness. Our major findings are that for the accuracy of large sample asymptotic approximations instrument weakness is much more detrimental than instrument invalidity. Also, IV estimators obtained from strong but possibly invalid instruments are usually much closer to the true parameter values than those obtained from valid but weak instruments.

Purpose: Investors and portfolio managers can earn profitably when they correctly predict when stock prices will go up or down. For this reason, it is crucial to know the effect levels of the factors that affect stock prices. In addition to macroeconomic factors, the psychological behavior of investors also affects stock prices. Therefore, the study aims to reveal the different sensitivity levels of the stock index against macroeconomic and psychological factors.

Design/Methodology/Approach: In this study, dollar rate (USD), euro rate (EURO), time deposit interest rate (IR), gold price (GOLD), industrial production index (IPI), and consumer price index (CPI) (inflation (INF)) were used as macroeconomic factors, while Consumer Confidence Index (CCI) and VIX Fear Index (VIX) were used as psychological factors. In addition, the BIST-100 index, which is listed in Borsa Istanbul, was used as the stock index. The sensitivity of the stock index to macroeconomic and psychological factors was investigated using the Multivariate Adaptive Regression Spline (MARS) method using data from January 2012 to October 2020.

Findings: In the analyses performed using the MARS method, the coefficients of INF, USD, EURO, IR, CCI, and VIX Index were found to be statistically significant and effective on the stock index. Among these variables, INF has the highest effect on stocks. It is followed by USD, IR, EURO, CCI, and VIX. GOLD and IPI variables did not show statistical significance in the model. The most important difference of the MARS model from other regressions is that each factor’s effect on the stock index is analyzed by separating it according to the value of the factor. According to the results obtained from the MARS model: (1) it has been determined that USD, EURO, IR, and CPI have both positive and negative effects on the stock market index and (2) CCI and VIX have been found to have negative effects on stocks. These results provide essential information about how investors who plan to invest in the stock index should take into consideration different macroeconomic and psychological values.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the effects of factors affecting the stock index by decomposing it according to the values it takes. Also, this study provides additional information by listing the factors affecting the stock index in order of importance. These results will help investors, portfolio managers, company executives, and policy-makers understand the stock markets.

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.