JOURNAL OF QUANTITATIVE ECONOMICS最新文献

The research aims to excavate the role of global (Fed Rate, Crude, Real Dollar Index) and endogenous economic variables (GDP and Consumer Price Index) in shaping the spillover amongst the major Indian Financial indicators, viz. Nifty Index, MCX Gold, USDINR, Govt. Bond 10Y maturity and agricultural index N-Krishi. To facilitate cross-comparison decomposition of time-varying spillover output generated from Time-Varying Vector Autoregression (TVP-VAR) with aggregation at three layers is performed. The research finds that Indian Financial Indicators are vulnerable to spillover shocks from global variables predominantly driven by Fed Rate and Real Dollar Index. USDINR turns out to be most sensitive to global shocks and transgresses the shock to other financial indicators. Importantly, persistently high inflation has brought volatility spikes in the directional spillover to financial indicators. Though spillover subsidence is observed post-2014, with an all-time high during GFC, a sudden spurt in all financial indicators has been observed post-Covid-19, with Govt. bonds showing a sporadic rise. An important observation relates to staunch spillover from GDP during GFC with reoccurrence post-Covid. Additionally, a closely knit spillover tie is observed among USDINR, N-Krishi, and Crude. The study is beneficial to RBI to proactively monitor the weakening rupee along with Fed tapering to manage the rising spillover post-Covid-19. The effort of RBI has to be reciprocated by the government in inflation targeting to reinforce the curbing efforts of rising shock spillover.

Income inequality in developing countries remains a major concern. It has been established that higher inequality makes a greater proportion of the population vulnerable to poverty. This paper aimed to analyse the effect of the interaction between ICTs and human capital on income inequality in developing countries. Covering 89 developing countries for the period 2000 to 2015 and based on panel fixed effects instrumental variables technique, this study finds that the interaction between ICTs and human capital reduces overall income inequality on the one hand, and on the other, leads to an increase in the income shares of the poorest, and in particular relative to the richest in developing countries. Furthermore, the interaction between ICTs and human capital reinforces the impact of ICTs on income inequality in developing countries. These results suggest that prioritizing the acquisition of human capital by the poorest, as well as promoting access to and use of ICTs for the benefit of the poorest would significantly contribute to reduce overall income inequality and increase income shares of the poorest in developing countries.

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007-08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015-16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid-19 shock of 2020-21.

We study the impact of recent crisis episodes viz. the Great Recession of 2007-09, the Euro Area crisis of 2010-12 and the COVID-19 pandemic of 2020-21 on the Emerging Market Economies (EMEs) of China and India using data from January, 1986 till June, 2021. A Markov-switching (MS) analysis is applied to discern economy-specific cycles/regimes and common cycles/regimes in the growth rates of the economies. We apply the univariate MS Autoregressive (MS-AR) model to characterize country-specific negative growth, moderate growth and high growth regimes of China and India. We examine the extent of overlap of the identified regimes with the Great Recession, the Eurozone crisis, and the COVID-19 pandemic. Thereafter, we study the regimes depicting common phases in growth rates of China-India and China-India-US by using multivariate MS Vector Autoregressive (MS-VAR) models. The multivariate analysis shows the presence of common negative growth during the turbulent periods during the study period. These results can be explained by the existence of strong trade and financial linkages between the two EMEs and the Advanced economies. The pandemic triggered a recession in the Chinese, Indian and U.S. economies and its impact on growth is much worse than the Great Recession and the Eurozone crises.