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Following the collapse of the banking system in October 2008, the Icelandic authorities attempted to restore confidence in the country’s institutions, improve their functioning and gradually improve the country’s credit rating. The authorities took ownership of an International Monetary Fund-sponsored economic programme that managed to turn the macroeconomic development around when, following a trough in the summer of 2010, an economic expansion started that has continued ever since. They applied for membership in the European Union in order to show their commitment to be part of the international economic community and to have a lender of last resort in the European Central Bank in future crises. The causes of the collapse were investigated and many bankers were prosecuted. Finally, financial regulations were made stricter and the structures of the Central Bank and the supervisory authority were changed for the better. The net effect was to lower the credit default swap rate on the government’s debt, gain access to capital markets and make the Iceland story one of resurrection rather than only hubris and collapse.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

Fiscal and monetary policies are essential to the development of a capital market. In this chapter, authors present how fiscal and monetary policy in Poland evolved and adjusted to economic challenges in 1998–2022. It is worth noticing that the Polish economy and financial market have been built from scratch after 45 years of socialism. Hence, it is scientifically interesting to study the relationship between fiscal and monetary policy, and capital market in a developing country, and in a relatively young economy.

Both – the macroeconomic policy mix and development of the capital market – are the subject of analysis how fiscal and monetary policy impacted the capital market. As so the main aim of the chapter is the assessment of the nexus and dependencies between fiscal and monetary policy and the capital market.

In the chapter, multiple linear regression was used for each dependent variable to discover which monetary and fiscal policy parameters significantly predicted selected variables describing the development of the capital market in Poland. Fiscal and monetary policy variables served as descriptors explaining capital market parameters in seven separate models.

Multiple regression models explain 77.3%–95.4% of the volatility of the capital market characteristics. The level of the central bank's reference rate is a variable that influences the capital market the most. In six out of seven models, the interest rate was a significant parameter. The development of the capital market was accompanied by a higher tax-to-GDP ratio. At the same time, a strong negative impact of the tax-to-GDP increase was noticed in domestic institutional investors' stock trading.