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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 aim of this chapter is to examine the role of real exchange rates in the relationship between tourist arrival and economic growth in Malaysia over the period of 2000–2018. We disaggregate Malaysian tourists into six geographical regions, namely Asia, Singapore, Europe, Pacific region, Americas, and Africa. Using a non-linear autoregressive distributed lag model, we find that the appreciation of real exchange rates with positive growth of economy plays a prominent role in influencing international tourist arrivals from Singapore, other Asian countries, Pacific region, Europe, and Americas. Our study suggests that real appreciation is important in providing some insights into the effectiveness of growth-led-tourism policies. In line with this, some implications are provided at the end of this chapter.

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.

Introduction: Financial development has a direct impact on the housing market by facilitating access to credit. The increase in housing loans resulting from the relaxation of the credit constraint causes an increase in housing demand and house prices. Purpose: This study aims to examine the relationship between financial development and house prices in Turkey, using the variables: the domestic credit to the private sector and total housing and consumer credits. Methodology: To determine any long-run relationship between financial development and house prices, the autoregressive distributed lag methods are used, covering the selected variables such as real GDP, inflation, mortgage interest rate, and stock price from 2010Q1 to 2020Q2. Findings: The study’s findings show that both variables representing financial development have a statistically significant and substantial positive effect on house prices. Besides, the selected macroeconomic variables have the theoretically expected impact on house prices.

This study analyzes the impact of exchange rate volatility (ERV) on inbound tourist arrivals from four ASEAN countries namely Indonesia, the Philippines, Singapore, and Thailand during 1970–2017. Volatility in the exchange rates between the tourist currency and ringgit Malaysia is measured using the Generalized Autoregressive Conditional Heteroskedasticity model. The results from Autoregressive Distributed Lagged models indicate that ERV has no significant impact on tourist arrivals from ASEAN to Malaysia. This implies that tourists from these countries may not be sensitive to ERV when choosing Malaysia as their travel destination. There are two possible explanations for the results. First, Malaysian ringgit has been depreciating against major currencies and regional currencies in recent years, which makes ringgit relatively cheaper than other ASEAN currencies. Second, the empirical results of the study support the argument that ERV has a more serious impact on tourist spending compared to tourist arrivals.

This chapter strives to evaluate the impact of tourist arrival on energy consumption, air pollution, gross domestic product (GDP), and foreign direct investment to suggest strategies for further tourism development. Relevant data from Taiwan are analyzed, entailing tourist arrivals, GDP, carbon dioxide emissions, energy consumption, and capital investment. It tests four hypotheses using the Augmented Dickey-Fuller single root test, the Autoregressive Distributed Lag model, and time series econometrics of Granger causality. This study finds that tourist arrival is positively related to energy consumption and GDP, whereas it negatively relates to carbon dioxide emission and capital investment. In consideration of a negative relationship between tourist arrival and direct investment, this study suggests devising timely research agendas on carrying capacity and service quality in the mind of international tourists to see if additional investment in tourism infrastructures is needed.