Ahmet Ihsan Kaya

The United Kingdom has experienced significantly lower growth rates of business investment and labour productivity following its decision to leave the European Union, although this lacklustre performance was affected by the economic shocks caused by the COVID-19 pandemic and the Russian invasion of Ukraine in addition to Brexit. This article aims to quantify the impact of Brexit on business investment and labour productivity in the United Kingdom using the National Institute of Economic and Social Research’s Global Macroeconometric Model. We model Brexit as a decline in trade with the European Union and associated reduction in terms of trade, a decrease in productivity and a permanent increase in uncertainty. Our estimates suggest that these shocks have led to an approximately 12–13% decline in UK business investment in 2023, which gradually declines to 7–8% by 2035 as businesses adjust to the terms of trade and productivity shocks. This corresponds to a real gross domestic product (GDP) loss of 2–3% (about £850 per capita) in 2023 and 5–6% (about £2,300 per capita) by 2035. Additionally, we find that Brexit has reduced labour productivity by around 2–2.5% as of 2023, with a projected reduction of 5–6% by 2035.

This study analyzes the role of push–pull factors on the level, volatility and comovement of capital flows in emerging markets (EMs). Taking the commonality of capital flows into account, we employ the panel Generalized Autoregressive Conditional Heteroscedasticity model developed by Cermeño and Grier for 16 EMs. This method not only accounts for country‐specific heterogeneity and cross‐section dependence but also allows the examination of the sources of the level, volatility and comovement of capital flows in a single step. The results show that domestic factors explain two‐thirds of the variation in net capital‐flow volatility. While both global and domestic factors, with the prominent ones being global risks and domestic economic growth, influence the comovement, their impacts somewhat vary by the types of capital flows.

In this study, the effect of real exchange rate on bilateral trade balance between Turkey and its 25 main trade partners is investigated for the period of 1996 - 2015 with heterogeneous panel data techniques. Trade balance model is estimated by using Mean Group (MG) estimator, which allows parameter heterogeneity, Common Correlated Effects Mean Group (CCEMG), and Augmented Mean Group (AMG) estimators, which both allow cross-section dependency and heterogeneity. Results indicate that the real exchange rate elasticity of the trade balance ranges between -0.40 and -0.45 and Marshall-Lerner (ML) condition is valid for Turkey. According to the results, the foreign income elasticity of trade balance ranges between 1.54 and 2.84, while for domestic income elasticity, it is found between -0.75 and -1.38. Country-specific results show that ML condition is valid for the USA, Belgium, Spain, Switzerland, Romania, and Russia at the bilateral level according to both CCEMG and AMG estimators.

This study investigates excessive movements in capital flows called surges or bonanzas. Contrary to the previous work that extensively uses ad‐hoc measures and discretionary thresholds; we adopt a distinctive methodology to detect capital flow surges based on right‐tailed unit root tests. Generalized supremum augmented Dickey‐Fuller (GSADF) proposed by Phillips et al. (2015) is successfully applied to identify asset price bubbles. Exploiting the technical and conceptual similarities in the formations of asset price bubbles and capital flow surges, we perform the GSADF procedure using quarterly net capital flows data from 43 developing countries. The advantages of this procedure are twofold: it can distinguish the behaviour of volatility and explosiveness and diagnose multiple surges in a series. We identified 727 individual surges, 130 different surge episodes, and 4 global capital flow waves over the periods of 1995–2017. Compared with the existing measures, the application of this surge‐detection technique provides a useful tool as a data‐driven method with no need for discretionary thresholds. We also investigate the factors triggering capital flow surges, employing the Fernandez‐Val and Weidner (2016) bias‐adjusted fixed effects probit model and find that domestic factors play a dominant role on the surge occurrences in developing countries.