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The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

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.

Relying on Clive Granger's many and varied contributions to econometric analysis, this paper considers some of the key econometric considerations involved in estimating Taylor-type rules for US data. We focus on the roles of unit roots, cointegration, structural breaks, and non-linearities to make the case that most existing estimates are based on an unbalanced regression. A variety of estimates reveal that neglected cointegration results in the omission of a necessary error correction term and that Federal Reserve (Fed) reactions during the Greenspan era appear to have been asymmetric. We argue that error correction and non-linearities may be one way to estimate Taylor rules over long samples when the underlying policy regime may have changed significantly.

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.

During the post-globalization period, tariff imposition on manufacturing trade has a possible effect on the economy of developed and developing nations. Along with the volume and balance of trade, the study accounts for both export and import separately in order to observe their dynamisms under the tariff regime and makes comparisons between developing and developed countries. Using the World Development Indicators and World Integrated Trade Solution databases of World Bank (2020) on China (developing nation) and the United States (developed nation) over the period of 1970–2019, the co-integration tests and thereafter vector error correction models indicate that the relationship between tariff and manufacturing trade is positive and statistically significant.