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Employing a bivariate regime switching model, this paper attempts to examine the regime-dependent effects of inflation uncertainty and output growth uncertainty on inflation and output growth. Using monthly data of the United Kingdom and the United States, we provide evidence that both nominal and real uncertainty exert regime-dependent impacts on inflation. Furthermore, in case of both the countries, inflation uncertainty has adverse impact on output growth mainly during the period of economic contraction. Also, for these two countries, it can be argued that higher real uncertainty significantly reduces output growth only in their respective low output growth regimes.

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It is widely proclaimed that capital account liberalization would immensely benefit developing economies because once capital controls are lifted, developing economies create a potential for movement of capital. And, this free movement of capital could possibly increase growth thereby lifting millions out of poverty. India has been gradually liberalizing since the 1980s and throughout more capital inflows were observed compared to outflows. Also, the composition of capital flows has been changing since the 1980s–with Foreign Direct Investment (FDI) inflows rising steadily post-1991compared to portfolio and debt flows. However, since 2000, FDI outflows from India were also witnessed. In this paper we empirically test the impact of FDI flows on poverty in India for 1980–2011. To provide a correct perspective to India’s performance we also analyze the link between FDI flows and poverty for SAARC countries. For a better understanding of how FDI flows impact poverty, we analyze the outflows and inflows separately. The results show both similarities and contrasts in the behaviour of India in comparison with the other SAARC countries.

The boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives of this empirical study are to i) detect the possibility of multiple structural breaks in the US house price data during 1995-2010, exhibiting very sharp upturns and downturns; ii) endogenously determine the break points and iii) conduct house price forecasting exercises to see how models with structural breaks fare with competing time series modelslinear and nonlinear. Using a very general methodology (Bai-Perron, 1998, 2003, we found four break points in the trend in the S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the forecasting performance of the model with structural breaks to four competing modelsnamely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), Self-Exciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive (STAR). Our findings suggest that house price series not only has undergone structural changes but also regime shifts. Hence, forecasting models that assume constant coefficients such as ARMA may not accurately capture house price dynamics.

This paper reexamines the role of the Federal Reserve in triggering the recent housing crisis. Specifically, we explore if the relationship between the federal funds rate and the housing variables underwent structural changes in the wake of the housing crisis. Using quarterly data spanning 1960–2017, we estimate a VAR model involving federal funds rate, real GDP growth and a housing variable (captured by house price inflation or residential investment share or housing starts) and conduct time series analysis for the pre- and post-crisis periods. While previous studies mostly set break-dates based on events known a priori to split the full sample to subsamples, we endogenously determine structural break points occurring at multiple unknown dates. Our Granger causality analysis indicates that the federal funds rate did not cause house price inflation, although it caused residential investment share and housing starts in the pre-crisis period. In the post-crisis period, the real GDP growth caused residential investment and housing starts while house price inflation had a momentum of its own. Our impulse response and forecast error variance decomposition analysis reinforce these results. Overall, our findings suggest that housing volume fluctuates more than house prices over the business cycle.

In response to the rapidly spreading COVID-19 pandemic, governments resorted to containment and closure measures to reduce population mobility and ensure social distancing. Initially, India’s state governments enacted varying social-distancing policies until the Central government overrode states to impose a nationwide lockdown on 24th March. This paper examines the relative impact of state- and central-level social-distancing policies on changes in mobility, comparing the periods before and after the national lockdown. A district-level panel dataset is formed, compiling data on social-distancing policies and changes in population mobility patterns. Panel regressions reveal that the incremental effect of each social-distancing policy varied across states in the pre-24th March period. The national lockdown led to much larger, though varying, reductions in mobility across all states. Overall, states which were able to achieve higher compliance in terms of reducing mobility in the pre-lockdown phase performed better in the national lockdown. Supplementary Information The online version contains supplementary material available at 10.1057/s41287-021-00463-4.

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