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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.

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

The search for more effective policies, choice of optimal implementation strategies for achieving defined policy targets (e.g., cost-containment, improved access, and quality healthcare outcomes), and selection among the metrics relevant for assessing health system policy change performance simultaneously pose continuing healthcare sector challenges for many countries of the world. Meanwhile, research on the core drivers of healthcare costs across the health systems of the many countries continues to gain increased momentum as these countries learn among themselves. Consequently, cross-country comparison studies largely focus their interests on the relationship among health expenditures (HCE), GDP, aging demographics, and technology. Using more recent 1980–2014 annual data panel on 34 OECD countries and the panel ARDL (Autoregressive Distributed Lag) framework, this study investigates the long- and short-run relationships among aggregate healthcare expenditure, income (GDP per capita or per capita GDP^_HCE), age dependency ratio, and “international co-operation patents” (for capturing the technology effects). Results from the panel ARDL approach and Granger causality tests suggest a long-run relationship among healthcare expenditure and the three major determinants. Findings from the Westerlund test with bootstrapping further corroborate the existence of a long-run relationship among healthcare expenditure and the three core determinants. Interestingly, GDP less health expenditure (GDP^_HCE) is the only short-run driver of HCE. The income elasticity estimates, falling in the 1.16–1.46 range, suggest that the behavior of aggregate healthcare in the 34 OECD countries tends toward those for luxury goods. Finally, through cross-country technology spillover effects, these OECD countries benefit significantly from international investments through technology cooperations resulting in jointly owned patents.

This chapter examines the long-run and short-run spillover effects of US quantitative easing (QE) on the money market in India. The study adopts the autoregressive distributed lag bounds testing co-integration approach for monthly data from September 2008 to May 2019 to investigate the spillover impact. The results reveal that a 10% rise in US QE led to a 25 bps softening of the weighted average call rate and a 2–5 bps hardening of the Treasury Bill. This impact was beyond the active participation by the Reserve Bank of India on the policy rate and liquidity in the system during the QE episodes. It suggests that the Indian money market is susceptible to US QE.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

This chapter empirically examines the relationship between defense expending, budget deficits, and income redistribution in India for the period 1970–2009. The analysis is based on an autoregressive distributed lag model (ARDL) popularized by Pesaran and Shin (1999). Empirical estimates reveal that military spending in India is indeed associated with income redistribution. The empirical approach indicates that there exists a long-run relationship between transfer payments as a percentage in GDP (TP), defense expenditures as a percentage in GDP (ME), and budget deficits as a percentage in GDP (DEF) in India. Defense expenditures as a percentage in GDP and the budget deficits as a percentage in GDP have positive and significant impacts on transfer payments in the same fiscal year. But the budget deficit as a percentage in GDP has a negative and significant impact on transfer payments as a percentage in GDP in the next fiscal year.

Recent evidences show that terrorism is becoming frequent in Nigeria, ranging from incessant Boko Haram activities in the North East; Independent People of Biafra (IPOB) activities in the South-East states, kidnapping and vandalizing oil pipes in the South-South, Fulani-herdsmen attacks in the Middle Belt, among others. In an attempt to tackle terrorism, the Federal Government at different times adopted military actions with little or no lasting solution. The Have and Have-nots hypothesis (Shahbaz, 2013) stresses the role of economic phenomenon in determining the causes of terrorism. It is on this note that this chapter investigates the linkages between economic growth proxy by gross domestic product per capita (GDPPC) and other fundamental variables such as inflation, unemployment, and inequality gaps, among others; and terrorism in Nigeria. We intend to know whether cointegration exists between the two constructs; and if it does, is there causality? The study employed both the autoregressive distributed lag (ARDL) and the vector error correction model (VECM) approaches to examine the existence of or otherwise a long-run relationship as well as causality among the constructs. Results reveal that a compelling cointegrating relationship exists among the variables. It is further revealed that unemployment, inequality, poverty, inflation, among others, Granger cause terrorism. It stresses that the Have-not hypothesis explained the causes of terrorism in Nigeria. The study therefore suggests that policy makers should, in order to prevent or combat terrorism, focus on improving the economy by creating job opportunities through provision of conducive environment that supports businesses and reduces inequality gaps.