We revisit the relationship between financial development and economic growth in a panel of 52 middle income countries over the 1980-2008 period, using pooled mean group estimator in a dynamic heterogeneous panel setting. We show that financial development does not have a linear positive long-run impact on economic growth in this sample. When we consider a non-linear relationship between financial development and growth, we find an inverted U-shaped relationship between finance and growth in the long run. In the short-run, the relationship is insignificant. This finding suggests that middle income countries face a threshold point after which financial development no longer contributes to economic growth.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2434445_code459177.pdf?abstractid=2434445&mirid=5&type=2

Is the Relationship between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle Income Countries

Bitcoin’s emergence has the potential to pave the way for a technological revolution in financial markets. What determines its valuation is an important open question with far-reaching business and...

How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis

In this paper we use multivariate cointegration analysis to estimate electricity demand elasticities at the subsectoral industry level. This enables us to reap the benefits of lower heterogeneity within the electricity-consuming sectors investigated and of retaining additional information otherwise blurred by aggregation. The annual data set used covers eight subsectors of the German economy for the period 1970-2007. By employing a cointegrated VAR model specification and accounting for structural breaks we find cointegration relationships for five of the eight subsectors studied. The long-run elasticities range between 0.70 and 1.90 for economic activity and between –0.52 and zero for the price of electricity. The short-run elasticities are estimated by single-equation error-correction modeling and found to be between 0.17 to 1.02 for economic activity and –0.57 to zero for electricity price. Granger-causality tests indicate that in the long term causality runs from both economic activity and electricity price to electricity consumption, while Granger-causality from electricity price and electricity consumption to economic activity is detected in only two subsectors. Electricity price is found to be Granger-caused neither in the long nor the short run. Finally, an impulse response analysis yields plausible results confirming the usefulness of the approach adopted.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1729732_code1494485.pdf?abstractid=1729732&mirid=4

Short- and Long-Run Electricity Demand Elasticities at the Subsectoral Level: A Cointegration Analysis for German Manufacturing Industries

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

https://www.mdpi.com/2076-3417/7/5/510/pdf

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Stock market development and economic growth: Empirical evidence from China

Thank you very much for downloading likelihood based inference in cointegrated vector autoregressive models. Maybe you have knowledge that, people have look hundreds times for their chosen books like this likelihood based inference in cointegrated vector autoregressive models, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious bugs inside their desktop computer.

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Papers

Likelihood Based Inference In Cointegrated Vector Autoregressive Models