Search

We investigate the relation between electricity consumption and economic growth by incorporating trade openness, capital and labour in production function of Kazakhstan using annual data for 1991–2014. We apply the autoregressive distributed lag bounds testing and the VECM Granger causality approach to examine long‐run and causality relation between the variables. Our results confirm the existence of a long‐run relation among the series. The empirical evidence reveals that electricity consumption adds in economic growth. Trade openness stimulates economic growth, and capital and labour promote economic growth, as well. The causality analysis shows that electricity consumption Granger causes economic growth and trade openness. We also document feedback effect between trade openness and economic growth. Our study provides new insights for policymakers to articulate a comprehensive economic, trade and energy policy to sustain long‐run economic growth in Kazakhstan. [ABSTRACT FROM AUTHOR]

This paper revisits the Feldstein Horioka (FH) puzzle by estimating a time varying parameter model through Kalman filtering. The paper investigated the existence of savings and investment relationship for 22 OECD countries. We found evidence that the time varying saving retention coefficient has gradually declined since mid 70’s for most of the countries in our sample. Average time varying saving retention coefficient for the entire sample also showed steady decline as well. The degree of capital mobility is increasing as the world financial markets are becoming increasingly integrated over time. Therefore, the savings and investment relationship will go through a dynamic process. The current study tries to capture this dynamic relationship between savings and investment using a state-space model. [ABSTRACT FROM AUTHOR]

Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning. [ABSTRACT FROM AUTHOR]