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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.

This chapter advances a panel vector autoregressive/vector error correction model (PVAR/PVECM) framework for purposes of examining the sources and determinants of cross-country variations in macroeconomic performance using large cross-country data sets. Besides capturing the simultaneity of the potential determinants of cross-country variations in macroeconomic performance and carefully separating short- from long-run dynamics, the PVAR/PVECM framework advanced allows to capture a variety of other features typically present in cross-country macroeconomic data, including model heterogeneity and cross-sectional dependence. We use the PVAR/PVECM framework we advance to reexamine the dynamic interrelation between investment in physical capital and output growth. The empirical findings for an unbalanced panel of 90 countries over the time period from at most 1950 to 2000 suggest for most regions of the world surprisingly strong support for a long-run relationship between output and investment in physical capital that is in line with neoclassical growth theory. At the same time, the notion that there would be even a long-run (let alone short-run) causal relation between investment in physical capital and output (or vice versa) is strongly refuted. However, the size of the feedback from output growth to investment growth is estimated to strongly dominate the size of the feedback from investment growth to output growth.

A number of earlier studies have examined whether extensive labour market programmes (ALMPs) contribute to upward wage pressure in the Swedish economy. Most studies on aggregate data have concluded that they actually do. In this paper we look at this issue using more recent data to check whether the extreme conditions in the Swedish labour market in the 1990s and the concomitant high levels of ALMP participation have brought about a change in the previously observed patterns. We also look at the issue using three different estimation methods to check the robustness of the results. Our main finding is that, according to most estimates, ALMPs do not seem to contribute significantly to an increased wage pressure.

Sequel to the results of the preceding chapter that depicted positive associations of credit with the indicators of growth and development, the present chapter aims at investigating the interrelationships of credit with GDP and HDI separately in a bivariate framework for the selected countries for the period 1990–2019. For this purpose, this chapter first develops a theoretical model in line with the Barro (1991) model where bank credit is introduced as a good institutional component of endogenous growth. Then, it goes for a time series exercise to establish the long-run relations and short-run dynamics for the pairs of variables, credit-GDP and credit-HDI, to justify the linkages between the financial sector and the real sector. The study arrives at mixed results across the countries. In many cases, credit has been identified to be strongly related to income and development indicators in the long run through cointegrated stable relationships. Furthermore, credit makes a causal influence on GDP and HDI in some developed countries whereas GDP becomes a causal factor to credit in some developing countries. It is thus recommended for further aggravation of the two sectors’ linkages under the patronisations of the governments and the monetary authorities of the countries to have high growth of income and development so that a part of the sustainable development goal can be achieved through the financial sector.

Financial liberalization is assumed to be the integration of a country's local financial system with international financial markets and institutions. This integration usually requires that governments liberalize the domestic financial sector and the capital account. Financial sectors were liberalized in most of the developing countries in Asia, Africa, and Latin America within the early 1990s. Among these countries, emerging economies are those who promise huge potential for growth but also pose significant political, monetary, and social risks. Brazil and India are often compared among the major emerging economies. Despite these general similarities between them, there are notable differences in various aspects of opening the balance of payments capital account in both countries. In this chapter, we have tried to analyze the long-run as well as short-run relationship between quarterly growth rate of GDP with the stock market, real market, and money market macroeconomic variables in India and Brazil during the period from the first quarter of 1996–1997 to the second quarter of 2018–2019. To estimate the cointegration relationship between growth rate of GDP and its determinants, we employ the bounds testing procedure (modified-ARDL) developed by Pesaran, Shin, and Smith (2001, Journal of Applied Econometrics, 16(3), 289–326). According to our results, stock market plays a positive role in long-term growth in India. Although during the beginning period of the neoliberal reforms, India faced strong domestic political opposition, our study shows that liberalizing the financial market has been fruitful for long-term growth. Our results in case of Brazil show that inflation has a negative and significant impact on long-run growth rate of GDP. The results further show that the share of gross fixed capital formation in GDP in Brazil has a positive and significant long-run relation with the growth rate of GDP. The empirical results further indicate that just like India, liberalization of the financial market and allowing foreign capital inflows have been beneficial for the economy of Brazil in the long run.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

The literature on sustainable development reveals that the financial sector and the real sector should maintain a coherent association in the long run. Thus, like that in a country-level significance, the relevance of the investigations of the interrelationships between the financial sector’s development and the growth and development of the states within a country is also required to be done. This chapter tries to examine the interrelationships between two sets of variables, bank credit and state output, and bank credit and human development, for the pre-reform and post-reform periods. Using the appropriate time series econometric analysis, the study finds no long-run relationships between credit and NSDP during the pre-reform period but it has observed a number of states where such stable relations hold during the post-reform period. Again, there are mixed results between the two in the Granger causality analysis during both the periods. There are the states like AP, Bihar, Karnataka, Kerala and WB where developments in the financial sector influence the growth of the real sector, while the reverse causality, that is, from the real sector to the financial sectors works in case of Assam, Haryana, MP and Maharashtra. Bidirectional causality between the two is observed in the states like TN, WB, etc. Further, the study finds very small number of states where credit and human development are interlinked in the long run. However, in the short run, the financial sector makes influences to the human development in case of the states like Bihar, Odisha and TN.

There have been increasing numbers of studies on the role of energy and environmental pollution upon income of the economies across the countries after the development of the endogenous growth literature. As standard practice, the factors which make explanations of why the developed countries are growing more compared to the common list of developing countries are the knowledge capital, role of institutions, etc. But, the roles of energy use and environmental resources upon economic growth have also been identified as additional source of endogenous growth. Sometimes, in certain economies, it is observed that both the factors maintain bidirectional causal relations with the level and growth of income. The present chapter aims to investigate whether energy use and environmental pollution make a cause to the level of income measured by gross domestic product, in both the long run and short run, for the world's highly developing group of economies in the nomenclature of BRICS. The period of study is taken to be 1990–2016 for which the data on all the indicators are available for the five member countries of the group. Using a VAR model, the study arrives at the conclusion that all the three are cointegrated for Brazil and Russia only. Further, there are short-run causal interplays among the three in different combinations across the member countries. While GDP and energy use are the common cause of CO2 emission for Brazil, India, and China; energy use and CO2 emissions are to GDP in India, South Africa, and China.

The purpose of this paper is to provide an extensive review of the monetary model of exchange rate determination which is the main theoretical framework on analyzing exchange rate behavior over the last 40 years. Furthermore, we test the flexible price monetarist variant and the sticky price Keynesian variant of the monetary model. We conduct our analysis employing a sample of 14 advanced economies using annual data spanning the period 1880–2012.

The theoretical background of the paper relies on the monetary model to the exchange rate determination. We provide a thorough econometric analysis using a battery of unit root and cointegration testing techniques. We test the price-flexible monetarist version and the sticky-price version of the model using annual data from 1880 to 2012 for a group of industrialized countries.

We provide strong evidence of the existence of a nonlinear relationship between exchange rates and fundamentals. Therefore, we model the time-varying nature of this relationship by allowing for Markov regime switches for the exchange rate regimes. Modeling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. These results show that linearity is rejected in favor of an MS-VECM specification which forms statistically an adequate representation of the data. Two regimes are implied by the model; the one of the estimated regimes describes the monetary model whereas the other matches in most cases the constant coefficient model with wrong signs. Furthermore it is shown that depending on the nominal exchange rate regime in operation, the adjustment to the long run implied by the monetary model of the exchange rate determination came either from the exchange rate or from the monetary fundamentals. Moreover, based on a Regime Classification Measure, we showed that our chosen Markov-switching specification performed well in distinguishing between the two regimes for all cases. Finally, it is shown that fundamentals are not only significant within each regime but are also significant for the switches between the two regimes.

The results are of interest to practitioners and policy makers since understanding the evolution and determination of exchange rates is of crucial importance. Furthermore, our results are linked to forecasting performance of exchange rate models.

The present analysis extends previous analyses on exchange rate determination and it provides further support in favor of the monetary model as a long-run framework to understand the evolution of exchange rates.