A Dynamic Model for determining Inward Foreign ... - Business School

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Where and are stationary and and are uncorrelated white-noise error terms. These equations constitute a first order of VAR model as the longest lag length is unity. Hence, equations (7) and (8) are not reduced form since the gives a contemporaneous effect on and gives a contemporaneous effect on . 3.3.2.2 Lag Order Selection Criteria for Vector Autoregressive Model The selection criteria for the appropriate lag length are used to avoid over parameterising the model and produce a parsimonious model. The Bayesian Schwartz (BSC), The Hannan- Quinn Criterion (HQ) and the Akaike Information Criterion (AIC) are often used as alternative criterion. They rely on information similar to the Chi-Squared test and are derived as follows (see table 5): ( ) ( ̂) ( ) ( ̂) ( ) ( ) ( ̂) ( ( )) 3.3.2.3 Error Correction Model and Co-integration Clearly, a good time series modelling should define both short-run dynamic movements and the long-run equilibrium simultaneously. For this purpose, the error correction model is introduced in this study. The error correction model is a dynamical system with the characteristics that the deviation of the current state from its long-run relationship will be fed into its short-run dynamics. The error correction model can be used to conduct the short effect of the endogenous variables on the exogenous variables and as wall as the speed adjustment at which the exogenous variable return to equilibrium after a deviation has occurred. There are two different ways to conduct the co-integration test. Engle and Granger (1987) based on single equation and Johansen (1998) based on systems of equation. Engle and Granger test the stationary of residual based on single-equation static regression of one variable. Therefore, Johansen estimation technique is better in the sense that it uses maximum likelihood of a full system that provides test of Max-Eigen and Trace statistics (shown in equations 7 and 8 respectively) to determine the number of co-integrating vectors. Therefore, in this paper the Johansen and Jesulius estimation technique has been applied in order to determine the co-integration and the number of co-integrating vectors. 16
( ) ( ) ∑ ( ̂ ) ( ) ( ) ( ̂ ) Where is the sample size, is the number of co-integrating vectors under the null hypothesis and ̂ is the estimated value for the row of matrix ordered eigenvalue from the matrix. Thus, a significantly non-zero eigenvalue indicates a significant co-integrating vector. 4. Empirical Analyses The aims of this paper are that: to identify the major determinants of foreign direct and indirect investment, to analyse macroeconomic factors influencing inward foreign direct investment and to identify the country's risks factors that have an effect on foreign direct and indirect investment flows. 4.1 Descriptive Statistics The descriptive statistics shown in table (2, a) reveal that the average inward foreign direct investment (FDI) in Jordan is about 41with a sample range of almost 0.67 and 330.167 maximum. This implies that Jordan receives a good amount of inward FDI. According to World Investment Report in 2010 Jordan has been ranked 14th regarding inward FDI performance among Middle East countries. For instance, Saudi Arabia, Qatar and Lebanon have been levelled 17th, 13th and 6th respectively. The country risks play a major role in attracting foreign direct investment to inflow a host country. The three major variables of Jordan country risk explain also the reasonable amount of inward FDI in Jordan. The median of Jordan financial, economic and political risk are (38, 36, and 71.5) respectively, this shows that Jordan has sensible business environment to attract foreign investors. Table (3, b) presents the stock market price sectors and macroeconomic variables descriptive statistics. The standard deviations of stock market price sectors (banks, services, industries and general sectors) are more than the mean. This indicates a good variance. Table 2, b indicates the range price of the stock market price in Amman Stock Exchange 17
Page 1 and 2: A Dynamic Model for determining Inw
Page 3 and 4: 1. Introduction Foreign direct inve
Page 5 and 6: investors look for the markets whic
Page 7 and 8: macro or micro measures taken by th
Page 9 and 10: exchange rate between the Japanese
Page 11 and 12: Research Methodology and Empirical
Page 13 and 14: and error correction model (ECM) ar
Page 15 and 16: 3 Model Specifications 3.3.1 Unlagg
Page 17: uncorrelated and normally distribut
Page 21 and 22: 4.2 Unit Root Test The Augmented Di
Page 23 and 24: Table 4: Summery Statistics of the
Page 25 and 26: Table (5) presents results of VAR l
Page 27 and 28: Table (7) presents the Granger caus
Page 29 and 30: Table7: Vector Autoregressive (VAR)
Page 31 and 32: Table 8 (part: b): Estimates of the
Page 33 and 34: services locally, remove any impedi
Page 35 and 36: Guru-Gharana, Kishor K., and Deergh

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