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Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

Tourism is sensitive to many factors related to security, such as terrorism and armed conflict. This scenario can be considered for Colombia and its National Natural Parks. The signing of the peace agreement with the revolutionary armed forces (FARC-EP) of Colombia that would end the armed conflict in 2016 offers economic possibilities for the country where tourism represents an important option, especially in natural areas where the government has regained control. This has had an impact on the sustained growth of tourism demand. The objective of this study is to determine the sensitivity of the tourism market in Colombia and natural parks and its relationship with the evolutionary dynamics of tourism, in a context of armed conflict. Variables related to visitors and the armed conflict were associated by autoregressive vectors, Pearson correlation test and Granger causality. A correlation of −0.5474 was found for the accumulated period from 1995 to 2018. It was also determined that the number of victims of the armed conflict is the Granger cause of the number of visitors to natural parks. The study concludes that the sensitivity of tourism in natural parks in Colombia is a consequence of State policy for the partial termination of the internal armed conflict.

Purpose: The main purpose of this chapter is to thoroughly investigate the diverse literature available concerning nonperforming loans (NPLs) and its determinants by studying and analyzing the empirical studies from 1985 to 2019.

Design/Methodology: A qualitative approach is being incorporated, and by using content analysis, various previous studies are reviewed and important issues like the objectives, methodology, key findings, and variables are reported.

Findings: The study tries to compile the main findings from the various studies done concerning NPLs and its determinants. The study shows how various determinants both bank-specific and macroeconomic affect the banking structure and thus the NPLs, in different countries and at different periods of time. The study also highlights how countries’ banking structure got affected by various economic phenomena like recession, contagious effect of the financial crisis, banking Basel norms, and NPL management strategies. Further major issues like data acquisition, lack of data reporting, countries specific banking conditions, methodologies used in the analysis, scarce resources, and disclosure hindrance which are faced by previous studies were also reported.

Originality/Value: As there are very few studies that provide a detailed viewpoint on NPLs and its determinants in this area, this research will provide a concise and detailed framework for the researchers to analyses the diverse literature on NPLs and its determinates.

The basic model for estimating economic losses to a company that has some type of business interruption is well-documented in the forensic economics literature. A summary of much of this literature is contained in Gaughan (2000). The general method used to measure damages is essentially the same regardless of whether the loss occurs because of some type of natural disaster (as in insurance claims resulting from flood, fire, or hurricane) or whether it is caused by the actions of another party (as with potential tort claims). The interruption prevents the firm from selling units of product, which would otherwise have been supplied to the market. Economic damage is the loss of revenues less the incremental production costs of the units not sold, plus or minus some adjustment factors described in Gaughan (2000, 2004), and elsewhere.

In continuation to Chapter 3, the present chapter tries to quantify the impact of credit upon GDP and HDI as the first attempt and the linkages of NPA and security investments with credit, GDP and HDI of the countries as the second attempt. For these purposes, this chapter starts with the measurements of credit elasticity with respect to GDP and HDI to know the impact of credit on the private sectors upon the income and human development of the countries. Then, it focuses on the implications of common banking operating tools such as their investments in the governments’ securities in relation to credit to the private sectors, GDP and HDI of the selected countries in a panel data format. The results of the credit elasticity of GDP show that it has taken the positive sign in all of the countries and the negative changes are very little in number. Furthermore, the results on the linkages show that all the variables are mostly cointegrated and therefore maintain stable and equilibrium relationships in the long run among them. But the short-run results show that investment and credit make a cause to NPA, and investment and NPA make a cause to GDP. No variables make any interrelationships with the HDI in either the long-run or short-run systems. Thus, the countries in the list should put more emphasis on the working of the financial sectors as the key partner in the income-generating activities.