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This paper examines regime switching behaviour and dynamic linkages among currency and equity markets of Eurozone, India, Japan and U.S. using a Markov-switching framework. First, we seek to characterize the market specific and common regime shifts in international stock and currency markets. Second, we aim to study regime-dependent conditional correlations across these markets. We estimate state-dependent models for the financial markets in a univariate Markov-switching Autoregression (MS-AR) as well as a multivariate Markov-switching Vector Autoregression (MS-VAR) framework. The paper utilizes weekly data from July, 1999 to October, 2020 to model the interactions among the markets. Our univariate results identify two-states viz. bull state (bear state) characterized by high returns (low returns) and low volatility (high volatility) for the stock market indices and Euro/USD and INR/USD returns. For the Yen/USD market the bull state corresponds to depreciation accompanied by low volatility. Further, we employ a multivariate formulation to study the regimes across asset classes which provides additional insights into the common states across the markets. Using the MS-VAR model encompassing stocks and currencies, we find a tranquil regime characterized by lower volatility and higher returns and a turbulent regime depicted by higher volatility and lower returns. Contemporaneous correlations among asset market pairs are sharper during the crises. Some of the turbulent periods highlighted in the analysis include the dot-com bubble burst, South American crisis, 9/11, Iraq war, housing bubble burst, global financial crisis, Eurozone debt crisis, Taper Tantrum, Brexit, U.S. Federal Government Shutdown, U.S.-China Trade War and the recent COVID-19 pandemic.

The multiple crises that the world is facing – climate change, COVID-19 and war have halted or reversed the progress of the world towards the achievement of Sustainable Development Goals. Using a case study of Dayalbagh, a locality in metropolitan Agra, India, and headquarters of the Radhasoami faith, we examine the potential benefits of employing agroecology to achieve the United Nations Sustainable Development Goals (SDGs). The active, disciplined and cooperative community-based lifestyle followed in Dayalbagh with a strong focus on agriculture and service demonstrates how most of the SDGs can be achieved. It offers lessons for policy makers in terms of focus areas for policy support and reaching the last, lowest, least and the lost.

The primary objective of this paper is to measure persistence in overall inflation measured by WPI and CPI-IW and their components for the Indian economy over the period 1996:4 to 2017:02. The study first tests for persistence and then quantifies it using various univariate and multivariate approach. The univariate approach estimates persistence using sum of autoregressive coefficient, largest root of the autoregressive process, spectral measures such as Campbell and Mankiw measure of persistence and spectral density function at frequency zero. Estimates of persistence are also derived using variance ratio tests and rolling regressions technique. The study also uses long memory approach (ARFIMA) to measure persistence. In a multivariate approach, the study examines structural form of persistence by employing a time varying vector autoregressive model (TVP-VAR) with stochastic volatility, incorporating inflation, interest rate, demand and supply side variables as its constituents. Results of the study suggest that persistence is high in the economy at both aggregate and disaggregate level. Wholesale price inflation shows intermediate memory, however Consumer prices exhibit long-memory in a univariate analysis. However when structural persistence is studied then WPI inflation is affected by exchange rate shocks and supply shocks such as fuel but it is found to be invariant with respect to interest rate shocks. On the other hand, CPI-IW inflation exhibits time variation with respect to food shocks and exchange rate shocks but is found to be mostly invariant with respect to interest rate shocks.

Purpose The study aims to empirically investigate the trends and determinants of labour productivity of the two broad sectors –industry and services – and their components, namely, manufacturing and market services sectors, in the case of major developing and developed economies of Asia-Pacific over the period 1980-2014 and make a comparison thereof. Design/methodology/approach The study uses econometric methodology of panel unit root tests, panel cointegration and group-mean full modified ordinary least squares (FMOLS). Findings The study finds that while capital deepening, government size, institutional quality, productivity of the other sector and financial openness affect productivity of all the sectors significantly, the impact of human capital and trade openness varies across sectors in the case of developing economies. Furthermore, the impact of technological progress becomes significant in the post-liberalization reforms period in the developing economies. The study further finds that capital deepening, human capital, government size, institutional quality, productivity of the other sector, government size and trade openness are significant determinants of productivity of all sectors of developed economies under consideration. However, the impact of technological progress is stronger for manufacturing sector than services and its components. Furthermore, while both equity and debt liabilities (as measures of financial openness) influence sectoral productivity of industry and manufacturing sectors positively and significantly in case of developed economies, only equity liabilities have a significant influence on the productivity of developing economies. This may indicate existence of more developed financial markets in the case of developed economies. Originality/value The study identifies important structural differences in determinants of productivity both across sectors and across developing and developed economies of Asia-Pacific.

The current study investigates the trends in labour productivity of the major developing and developed economies of the Asia‐Pacific region and examines its determinants over the period 1980–2014. The study analyses capital deepening, human capital, technology, share of agriculture in GDP, financial development, institutional quality, inflation as well as macroeconomic variables as potential determinants of productivity, and identifies the differences in the impact of these factors on the productivity of developing and developed countries. Using panel cointegration and group‐mean fully modified ordinary least squares estimation, the study finds that capital deepening, human capital, technology, institutional quality and macroeconomic variables (i.e. government size and openness) are significant determinants of labour productivity of both developing and developed economies of the Asia‐Pacific region. The study further finds that while both trade openness and foreign direct investment affect productivity of developing economies positively, only trade openness has a positive and significant impact on the productivity of developed economies. The share of agriculture in GDP affects the labour productivity of developing Asia‐Pacific economies significantly but not that of developed economies. Furthermore, capital deepening has a much higher impact on the productivity of developing Asia‐Pacific economies than that of developed economies.

This article examines interlinkages between four major exchange rates, namely, USD–INR, EUR–INR, GBP–INR and JPY–INR in terms of returns and volatility spillovers using a vector autoregressive-multivariate GARCH–BEKK framework. In addition, we analyse the impact of RBI intervention on the returns, volatility and covariance of these exchange rates. The study finds significant bidirectional causality-in-mean and causality-in-variance between all four exchange rates. The estimation results suggest that RBI intervention in the form of net purchase of dollars leads to depreciation of INR vis-à-vis USD, EUR, GBP and JPY. Furthermore, we find that RBI intervention not only significantly affects the volatility of INR vis-à-vis USD, EUR and GBP but also explains significant amount of covariance between USD–INR and the other three exchange rates. JEL Classification: C32, G15, E58, F31

This study conducts policy-based macro stress testing of the Indian banking sector and also assesses its resilience towards compliance with BASEL norms with the aid of an empirical macro-financial model. It uses scenario analysis and quantitative techniques to capture the impact of macroeconomic stress on the stability of the Indian banks by evaluating financial soundness indicators (credit quality, quantity and quality of capital adequacy). The scenarios are generated through policy-based shocks vis-a-vis other external shocks. The results from the estimation of the model indicate a cointegrating relationship between credit quality and key macroeconomic variables including output growth rate, interest rate, money growth rate and exchange rate. The results of the scenario analysis reveal that the Indian banking sector remains largely sound in terms of total regulatory capital adequacy ratio as per current BASEL II and even BASEL III requirement. The results also show that expansionary monetary policy impacts credit quality and capital adequacy in a positive and significant manner via its effect on the economy’s growth rate.

The existing empirical studies on the effectiveness of RBI intervention in the exchange rate market are based on single equation/GARCH models. Recognizing the interdependence between exchange rate returns, conditional volatility of exchange rate and RBI intervention, this study uses a simultaneous equations framework to examine the efficacy of intervention by RBI. Additionally, we control for the impact of macro-economic fundamental variables and micro-structure variable in our model. The econometric methodology used in the study includes two steps. In the first step, DF-GLS, Ng-Perron and, Lee and Strazicich unit root tests are used to examine the stationarity of the variables. In the second step, the simultaneous equations model is estimated using the generalized method of moments (GMM)-IV. This study is based on secondary data at monthly frequency, spreading from 1996:08 to 2016:10. The main findings of the study are as follows. The results of unit root tests suggest that all variables included in the analysis are stationary. The system GMM-IV estimates of the model indicate that exchange rate returns are influenced by RBI intervention, output growth differential, money supply growth differential, stock market returns, capital flows, EUR-USD exchange rate returns, change in trade balance differential and order flow. Additionally, the estimates of exchange rate volatility equation suggest that RBI intervention significantly decreases exchange rate volatility. Further, we find that volatility in capital flows has significant and positive impact on exchange rate volatility. The estimates of the central bank intervention equation suggests that exchange rate returns have a significant and negative impact on RBI intervention in foreign exchange rate market. We also find evidence for the presence of asymmetry in RBI intervention. The key implications of the study indicate that RBI intervention is effective in stabilizing the Indian exchange rate market in the face of both domestic and external shocks. Furthermore, exchange rate returns are significantly affected by RBI intervention, macro-economic variables and micro-structure variable. The findings of this study may be useful for formulating India's monetary policy.

This paper analyzes the impact of the Eurozone debt crisis on China and India. Using Markov-switching analysis, we discern regimes in economic growth as well as financial markets and study the impact of the global financial crisis and Eurozone crisis on the same. We identify vulnerability and robustness factors governing the degree of exposure and resilience to the crisis for both these economies. In view of strong trade and financial linkages, the Eurozone crisis may have marred prospects of recovery in the aftermath of the recent Great Recession in both China and India. China, however, is found to be more resilient to the crisis possibly due to stronger macroeconomic fundamentals.

This study examines how various bank groups operating in India have fared macro stress events and conduct macro stress testing (MST) to trace the impact of certain macroeconomic stress scenarios on the credit quality of five Indian bank groups, that is, the State Bank of India (SBI) and its associates (SBGs), nationalised banks (NBs), old private sector banks (OPBs), new private sector banks (NPBs) and foreign banks (FBs), using panel data from 1997 to 2014. Credit quality is modelled as a function of both macroeconomic variables (output growth, interest rate, inflation rate and exchange rate) and idiosyncratic variables (profitability and size indicator of bank business activity). The model is estimated by employing a panel cointegration approach, and the impact of adverse scenarios on the estimated credit quality is computed. Empirical findings show that credit quality is pro-cyclical in nature and rises in the event of a slowdown in the economy. In general, the credit quality of Indian bank groups is found to be inversely and significantly related to the economy’s growth rate, inflation rate, exchange rate and profits of banks and positively and significantly related to the interest rate. Shock analysis also reveals that a downturn in the economy through certain adverse scenarios has a significant adverse impact on the credit quality. The shocks are quickly propagated across banks with substantial heterogeneities present in different bank groups. Thus, macroeconomic policy measures promoting growth with price stability are expected to impact credit quality positively. Further, measures at the bank level can improve credit quality by enhancing their profitability. JEL Classifications: C32, C58, E170, G21

This paper discusses the evolution of macroeconomic modelling. In particular, it focuses on Bayesian methods and provides some applications of the Bayesian Vector Autoregression methods to the Indian economy.

This paper investigates contagion across stock and currency markets of China, Eurozone, India, Japan and US during global financial crisis and Eurozone crisis. The crisis periods are selected using Markov-switching models for US and Eurozone markets. We, then, utilize the DCC-GARCH model to estimate conditional correlation among the assets and test for contagion/flight to quality effects during the crises. The results show significant contagion as well as flight to quality effects both across and within asset classes. We examine the impact of financial stress index on the correlation across markets and find that portfolio diversification benefits for equity markets may be non-existent.

INTRODUCTION According to Reinhart and Rogoff (2009), financial crises are essentially triggered by a collapse of investor confidence, especially in the case of highly leveraged financial markets. Economists believe that the introduction of more innovative financial instruments aimed at increasing the depth of the markets along with flexible monetary policy could contain the risk of occurrence of financial crises, as these could tackle the underlying business cycle downturns. However, the ‘subprime financial crisis’ that started in the US in 2007 could not be tamed and led to a recession in the largest economy of the world. Further, it snowballed into a global financial crisis, which had a cascading effect on financial markets around the world in 2008 and triggered the ‘Second Great Contraction’ in many economies. The global recession was extraordinary due to its massive coverage, extreme severity, long duration and huge repercussion effects. From Figure 6.1a, we observe that the gross government debt of the US was in the range of 60–70 per cent of the GDP till 2007. However, in a bid to revive the economy post the crisis, the US government resorted to fiscal expansion, which worsened the exchequer as the gross government debt soared to more than 100 per cent in 2012–2013. Current account balance (Figure 6.1b) of the US had been deteriorating till 2006. However, due to a dampening of the demand for exports in the face of a recession in the US economy, the current account balance steadily improved since 2007 and now stands at less than 3 per cent of the GDP. Unemployment rate (Figure 6.1c) peaked in the aftermath of the crisis to about 9.6 per cent in 2010, but stood at about 7 per cent in 2013. Figure 6.1d depicts the growth rate of GDP in the US and shows that the growth rate was negative during 2008–2010 and according to the latest data stands at about 2 per cent in 2013. US being the largest economy of the world, the impact of the global recession of 2008–2009 crisis was widespread with several countries of the world tumbling into a recession. This subsequently strained governments around the world as they had to overstretch in an attempt to tackle the real effects of the crisis on their economies by undertaking fiscal expansion.

This study examines the macroeconomic factors underlying FDI flows to India using cointegrating VAR with I(1) exogenous variables. The results indicate that conventional determinants such as a depreciating exchange rate, higher domestic returns, higher domestic output and better infrastructure are conducive to FDI flows to India. The results also indicate that macroeconomic instability has adverse effects while credit worthiness is conducive to FDI flows. A negative relation between trade openness and FDI is observed suggesting that FDI flows to India may be tariff jumping in nature. Empirical estimates also indicate that an increase in global FDI flows to other emerging economies reduces FDI flows to India indicating that India competes with other emerging economies in receiving FDI. Further, the results show that higher foreign output is conducive to FDI flows indicating procyclicality of FDI with economic performance of foreign countries.

This article examines the determinants of government yields in India using weekly data from April 2001 through June 2012. The analysis covers treasury bills with residual maturity of 15–91 days and government securities of residual maturity 1, 5 and 10 years. The empirical estimates show that a long-run relationship exists between each of these interest rates and the policy rate, rate of growth of money supply, inflation, interest rate spread, foreign interest rate and forward premium. At the same time, the empirical results show that the relative importance of the determinants varies across the maturity spectrum. The normalised generalised variance decompositions suggest that the policy rate and the rate of growth of high powered money are more important in explaining the proportion of variation in shorter-term interest rates than the longer-term rates. The weight of the forward premium also diminishes as we move towards higher maturity interest rates. The inflation rate becomes relatively less important in explaining variations in the yields as the maturity of the security increases. The yield spread, on the other hand, is more important in explaining the longer-term rates. The results also show that a large proportion of the variation in the rates on the 5-year and 10-year government securities is attributed to the interest rate itself, suggesting that the unexplained variation may be a result of cyclical factors that are relatively more important for longer-term rates but are not captured by the yield spread and are omitted from the estimations in this article due to the high frequency of data employed here. JEL Classification: C5, E43

Summary This paper analyzes the macroeconomic determinants of portfolio flows to India and finds that lower exchange rate volatility and greater risk diversification opportunities are conducive to portfolio flows. However, higher equity returns of other emerging markets discourage these flows. Other conventional determinants of portfolio flows are domestic equity performance, exchange rate, interest rate differential and domestic output growth. An analysis of disaggregated portfolio flows shows that determinants of FIIs are similar to aggregate portfolio flows, while ADR/GDRs are significantly influenced only by domestic equity returns, exchange rate, domestic output growth, and foreign output growth.

This paper examines the relationship between real exchange rate and the level as well as volatility of capital flows for the Indian economy for the period 1993Q2 to 2010Q4. Other variables include fiscal policy, monetary policy and external balance indicators. Estimation results indicate that the variables are co-integrated and each Granger-causes the real exchange rate. The generalized variance decompositions show that determinants of the real exchange rate, in descending order of importance include net capital inflows and their volatility (jointly), government expenditure, money supply and the current account surplus. An analysis on similar lines is also performed for the foreign exchange reserves held by the Reserve Bank of India.

This paper examines various measures of synchronisation of recessions, including clustering of the onset of recession across economies, proportion of economies in expansion and the diffusion index of international coincident indexes, and shows that the recent global recession is possibly the most concerted in the post-world-war period. Factors that contributed to the synchronisation and severity of the recession, such as trade and financial linkages and timing of policy actions, are analysed.

This paper investigates the determination of inflation in the framework of an open economy forward-looking as well as conventional backward-looking Phillips curve for eight Asian countries – Japan, Hong Kong, Korea, Singapore, Philippines, Thailand, China Mainland and India. Using quarterly data from the 1990s to 2005 and applying the instrumental variables estimation technique, we find that the output gap is significant in explaining the inflation rate in almost all the countries. Furthermore, at least one measure of international competitiveness has a statistically significant influence on inflation in all the countries. The differences in the developed and developing world are highlighted by the significance of agriculture related supply shocks in determining inflation in the case of developing countries. For all countries, the forward-looking Phillips curve provides a better fit compared to the backward-looking variant.

This paper evaluates estimates of the variance of U.S. inflation based on popular conditional heteroscedasticity (ARCH) models, by comparing them with the variances of subjective probability distributions for inflation provided in the ASA-NBER surveys of U.S. economic forecasters. The results are not encouraging for the ARCH model. Except in the early 1950s, ARCH effects in U.S. inflation are weak; there are no significant correlations between the ARCH and survey estimates of inflation uncertainty; and the ARCH measures give a misleading picture of the causes of inflation uncertainty and its effects on interest rates. Copyright 1993 by Blackwell Publishing Ltd

This paper develops univariate (ARIMA and ARCH/GARCH) and multivariate models (VAR, VECM and Bayesian VAR) to forecast short- and long-term rates, viz., call money rate, 15–91 days Treasury Bill rates and interest rates on Government securities with (residual) maturities of one year, five years and 10 years. Multivariate models consider factors such as liquidity, repo rate, yield spread, inflation rate, foreign interest rates and forward premium. The paper finds that multivariate models generally outperform univariate ones over longer forecast horizons. Overall, the paper concludes that the forecasting performance of Bayesian VAR models is satisfactory for most interest rates and their superiority in performance is marked at longer forecast horizons.

This paper investigates the effects of the East Asian crisis on the Indian economy and exchange rate movements. Despite the contagion effects that profoundly affected the other crisis-hit countries, the Indian economy and the rupee were found less affected. Reforms after the 1990–1991 crisis, control of capital flows, weak economic linkages with crisis-affected countries and stabilization policies that include intervention in the foreign exchange market and tightening of monetary policy are reasons for the insulation of the Indian economy from the crisis.

This paper examines the determinants of consumers’ buying attitudes for houses from January 1984 through June 2005. Data on buying attitudes are from responses to the Surveys of Consumer Attitudes conducted by the Survey Research Center, University of Michigan. The determinants considered include current and expected interest rates, wealth, expected real disposable income, expected change in financial status and house prices. The empirical estimates show that a long-run relationship exists between buying attitudes for houses and each of the above variables. Each of these determinants also Granger cause buying perceptions. Generalized impulse responses show that shocks to each of the above variables have a predictable and permanent impact on buying attitudes. Furthermore, generalized variance decompositions suggest that both current and expected interest rates explain a large proportion of the variation in consumers’ perceptions towards buying houses. Since consumers’ attitudes towards buying houses are likely to be translated into actual purchases, this study shows that in order of importance, interest rates—both current and future—have the maximum impact on decisions to purchase houses followed by expectations of real disposable income.

This paper evaluates the performance of 11 vector autoregressive models in forecasting 15 macroeconomic variables for the Indian economy over the 2007:01 to 2011:10 out-of-sample period. We consider 3 classical VARs, 4 Bayesian VARs and 4 Bayesian Factor Augmented VARs. Comparing the performance by minimum average RMSEs of the models to the benchmark random walk model, we find that in general, the 11 models outperform the random walk model. Although, there is no specific model that outperforms others at all horizons for any of the variables, the Bayesian VARs and Bayesian Factor Augmented VAR models on average outperform the classical VARs. We also provide an ex ante forecast using the selected `best' models and find that these models do not perfectly capture the turning points in each of the series pointing to the importance of conducting future research in a non-linear framework.

This paper examines the determinants of interest rates in India in the post-reform period in the context of a model that takes into account both domestic and external factors. The short- and long-run behavior of interest rates (commercial paper rate, 3-month Treasury bill rate, 12-month Treasury bill rate) is studied. The empirical results are robust across interest rates and indicate the existence of a cointegrating relationship between real interest rates, real government expenditure, real money supply, foreign interest rates and the forward premium. The estimations also show that movements in interest rates are Granger caused by both domestic and external factors.

This paper uses Leamer's sensitivity test in a VAR framework and examines the robustness of the relationship between different monetary and output variables. Output variables at the aggregated level include GDP, consumption, and gross private investment. Disaggregated variables comprise components of consumption (durables, non-durables and services) and investment (business inventories, fixed residential, and fixed nonresidential). All aggregated variables are robustly Granger caused by M2, the federal funds rate and the federal funds 3-months treasury rate spread. At the disaggregated level, only consumption of durables is Granger caused by these variables. Consumption of services, business inventories, and non-residential fixed investment are only Granger caused by money supply variables, while consumption of non-durable goods and residential investment are Granger caused by interest rates and/or interest rate spreads only.

This article uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. The benchmark Bayesian model includes home sales, price of homes, mortgage rate, real personal disposable income, and unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict U.S. home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

The failure of economic forecasters to predict the most recent US recession has renewed interest in the idea of supplementing model-based forecasts with information from other, more qualitative, indicators. This paper tests whether one such variable, the consumer confidence index, could have improved these forecasts; and whether improvements are greatest for forecasts generated by econometric models with little judgmental adjustment. We find that consumer confidence would have been helpful in predicting the 1991 recession. But the result does not generalize to other years, and appears to reflect the special nature of the recession rather than a persistent weakness in forecasting technique.