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What is Unit Root Test

Handbook of Research on Strategic Developments and Regulatory Practice in Global Finance
It is an econometric approach that tests whether the mean and variance change over time, taking into account the autoregressive structure of the time series.
Published in Chapter:
Foreign Direct Investment, Financial Development and Economic Growth: The Case of Turkey
Kadir Y. Eryigit (Uludağ University, Turkey) and Sibel Bali Eryigit (Uludağ University, Turkey)
DOI: 10.4018/978-1-4666-7288-8.ch021
Abstract
This chapter discusses the effects of financial development on relationships between FDI and economic growth within the framework of the relevant literature, and researches empirically the current situation in Turkey. To that end, analysis is conducted through the use of Johansen et al. (2000) cointegration method, based on the quarterly data for the period between 1989 and 2011. To the authors' knowledge, there are no studies assessing the complementary relationship between FDI and financial development in the relevant literature in Turkey. According to the results, financial development and FDI affect economic growth positively, and financial system makes important contributions to the positive effect of FDI on economic growth.
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More Results
Financial Development and Economic Growth: Panel Data Analysis
In statistics, a unit root test tests whether a time series variable is non-stationary using an autoregressive model. A well-known test that is valid in large samples is the augmented Dickey–Fuller test.
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Causal Relationship between Foreign Direct Investment and Economic Growth: Evidence from Turkey
In statistics, a unit root test tests whether a time series variable is non-stationary using an autoregressive model. A well-known test that is valid in large samples is the augmented Dickey–Fuller test.
Full Text Chapter Download: US $37.50 Add to Cart
A Panel Asymmetric Causality Between Health and Climate Change: Empirical Evidence From EU Regions
A test for determining whether the mean, variance and covariance of a time series are independent of time.
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