Foreign Direct Investment Inflows in Pakistan: A Time Series Analysis with Autoregressive Distributive Lag (ARDL) Approach

Abstract

Estimating a model of foreign direct investment has been one the central elements of FDI policy makers in Pakistan. This paper attempts to model, Determinants of Foreign Direct Investment Inflow in Pakistan: A Time Series Analysis with Auto Regressive Distributive Lag (ARDL) approach using the time series data for the period 1977 to 2010. However in a specification, FDI is found important with the elasticity of GDP growth rate 10% level of significance and infrastructure at 1% level of significance. These factors show do not show better performance in short-run dynamics like only infrastructure is elastic with FDI at 1% level of significance. Stability tests of the paper do not contain any serious structural change in the model as case of and CUSUM and CUSUMSQ statistics show the critical value lines lying inside the bounds, presenting the model is stable.

The Effect of country risk on foreign direct investment: Evidence from Egypt

“…Additionally, a study conducted by Kok and Ersoy (2009) concludes that FDI flows is positively affected by gross capital formation, trade, and GDP per capita growth while it is negatively affected by total foreign debt as ratio of GDP and inflation. Mall (2013) agrees with Kok and Ersoy. Paudel (2016), on the other hand, contradicts Kok and Ersoy's (2009) and Mall's (2013) view.…”

“…Mall (2013) agrees with Kok and Ersoy. Paudel (2016), on the other hand, contradicts Kok and Ersoy's (2009) and Mall's (2013) view. Paudel (2016) states that factors negatively influencing FDI are GDP growth, labor force, education, and trade reforms.…”

Economic Globalization and Economic Performance Dynamics: Some New Empirical Evidence from Nigeria

“…ARDL approach to cointegration, commonly referred to as bounds testing approach owed its roots in the works of Pesaran and Shin ((as cited in AssenmacherWesche and Pesaran, 2008) and Pesaran et al (2001]. Some researchers (Pattichis, 1999 (as cited in Afzal et al, 2013); Ghatak and Siddiki, 2001 (as cited in Sultan, 2012); Habibi and Rahim (2009);Kargbo (2012);Mall, 2013) seem to agree that the ARDL approach to cointegration yields robust results irrespective of the sample size.…”