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\def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2019-01-15 (revised: 15 January 2019)} \def\TheID{\makeatother } \def\TheDate{2019-01-15} \title{Impact of Foreign Direct Investment on Domestic Investment in Bangladesh} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 4\p@ plus2\p@ minus2\p@ \def\@listi{\leftmargin\leftmargini \topsep 2\p@ plus1\p@ minus1\p@ \parsep 2\p@ plus\p@ minus\p@ \itemsep 1pt} } \makeatother \fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm} \fancyhf{} \setlength{\headheight}{14pt} \fancyhead[LE]{\bfseries\leftmark} \fancyhead[RO]{\bfseries\rightmark} \fancyfoot[RO]{} \fancyfoot[CO]{\thepage} \fancyfoot[LO]{\TheID} \fancyfoot[LE]{} \fancyfoot[CE]{\thepage} \fancyfoot[RE]{\TheID} \hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true} \fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}} \date{} \usepackage{authblk} \providecommand{\keywords}[1] { \footnotesize \textbf{\textit{Index terms---}} #1 } \usepackage{graphicx,xcolor} \definecolor{GJBlue}{HTML}{273B81} \definecolor{GJLightBlue}{HTML}{0A9DD9} \definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Hamida Begum} \author[2]{Nurun Nahar Akhi} \affil[1]{ National University, Asian University of Bangladesh} \renewcommand\Authands{ and } \date{\small \em Received: 10 December 2018 Accepted: 2 January 2019 Published: 15 January 2019} \maketitle \begin{abstract} This study examines the impact of foreign direct investment inflows on domestic investment in Bangladesh by using time series data for the period of 1978 to 2017. Gross Capital Formation (GCF) is used as dependent variable (which is proxy of domestic investment) and Foreign Direct Investment (FDI), broad money (M2), export (EX) are used as independent variables. ADF test is used for testing stationary of taken variables in the model. Hence, some variables are stationary at level and one is at first difference, Autoregressive Distributed Lag (ARDL) estimation technique is used to accomplish the analysis. The result shows that in the long run and short run there is a positive but insignificant relation between foreign direct investment and domestic investment and relationship between export and domestic investment also positive and significant. \end{abstract} \keywords{GCF, FDI, ARDL.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{I. Introduction}]{I. Introduction}\par angladesh is a developing country with per capita income \$1,610 in FY2017 (BER:2017). In 2015, Bangladesh graduated to the status of lower middle income country from a low income country. The average growth rate of Bangladesh during the last decade is more than 6 percent. Bangladesh has adopted the vision 2021 and the associated perspective plan 2010-2021 where Bangladesh aimed at middle income status by 2021 and targeted GDP growth rate is 8 percent by 2021. To achieve the goal of middle income status by average GDP growth rate will have to rise current 6 percent to 7.5-8.0 percent. To secure the projected GDP growth rate, the investment will need to expand around 34.4 percent by 2020. For expanding investment Foreign Direct Investment (FDI) can be one of the most important factors. It is considered as one of the vital ingredients for capital formation of a capital poor country like Bangladesh. It may allow a country to bring in technologies and knowledge that are not readily available to domestic investors, creates jobs and increases the efficiency of labor resources (De \hyperref[b13]{Gregorio, 2003} {\ref , Guoxin Wu, 2010)}. It can emerge as a significant vehicle to build up physical capital, create employment opportunities, develop productive capacity, enhance skill of local labor through transfer technology and managerial know how, and helps integrate the domestic economy with the global economy. Therefore, in Bangladesh because of insufficient domestic capital formation FDI is often welcome as a means of financing for its ongoing development process. Given the importance of FDI in country's gross capital formation, this study seeks to examine the effects of FDI on gross capital formation (GCF) in Bangladesh. If there is one dollar increase in gross capital as a result of one dollar increase in FDI, this means that domestic investment remains unchanged and FDI's influence is neutral. If there is a dollar increase in FDI increases the total capital formation, "crowding in" occurs through the stimulation of domestic investment. On the contrary, if a dollar increase in FDI decreases the total capital formation, "crowding out" occurs {\ref (Agosin and Machado, 2005)}. FDI could crowd in domestic investment as it provides new investment opportunities to local firms through the provision of machinery and technology, which cannot be produced domestically (J.B. \hyperref[b4]{Ang, 2009)}. \section[{II. Objective of the Study}]{II. Objective of the Study}\par (i) To evaluate the impact of foreign direct investment on domestic investment in Bangladesh. (ii) To evaluate the impact of broad money on domestic investment in Bangladesh. (iii) To evaluate the impact of export on domestic investment in Bangladesh. \section[{III. Literature Review}]{III. Literature Review}\par Yahia, Y. E., et. al, (2018) empirically examined the impact of foreign direct investment inflow on domestic investment of Sudan over the period 1976 to 2016. They used autoregressive distributed -lag bound test and the result of their study showed that FDI crowd out Sudan's domestic investment. Ali, S. {\ref A. et. al. (2015)}, studied the dynamic linkages between foreign direct investment, public investment and private domestic investment in Pakistan for the time period 1977 to 2011. They used autoregressive distributed lags (ARDL) model and the outcome of the studies was FDI had positive significant effect on private domestic investment. \hyperref[b3]{Ameer, W. et. al (2017)} examined the relationship between inward foreign direct investment, domestic investment, formal and informal institutions for China by using cointegration and Granger causality analysis (Including bivariate and multivariate Granger causality models over the time period 1990-2014. They also used autoregressive distributed lags (ARDL) econometric methodology technique. The results of multivariate model showed that there is positive unidirectional causality running from FDI to DI in the long run. In the short run, both inward FDI and domestic investment do not allow Granger causality. {\ref Ullah, I. et. al (2014)} studied dynamic interaction between domestic investment, foreign direct investment, and economic growth in Pakistan for the period 1976-2010. The empirical findings of their study revealed that the existence of long run relationship between domestic investments, foreign direct investment, and economic growth, further supported by Toda-Yamamoto causality, and bidirectional causality had been found between FDI and domestic investment implying that both domestic investment and FDI cause each other. Megbowon, E. T., et al (2016) studied the foreign direct investment inflow, capital formation and employment in South Africa: time series analysis over the period of 1980 -2014. The estimates two multivariate models and two econometric analysis, co-integration and causality. They found that while there is a long-run relationship among variables in the employment models, it was not so in the gross capital formation model. No form of causality was found between FDI inflow and gross capital formation. Amighini, A. A. et al (2017), contributed to the long debated issue of whether inward foreign direct investment (FDI) can stimulate investment in developing countries by introducing a novel measure of FDI, based on industry-level data. Their results suggested that if multinational enterprises engage in manufacturing production the impact of FDI on total investment is positive-measured as the ratio of gross fixed capital formation to GDP but the same does not hold for other business activities. Ang. J. B. (2009), studied the effects of inward FDI on domestic investment by separating the latter into two different types, namely, private domestic investment (PDI) and public domestic investment (PUB). The study used multivariate Johansen co-integration technique between the period 1960 and 2003 for Malaysia and found evidence that PUB crowds in PDI and FDI is a complement rather than competition to PDI. \hyperref[b29]{Ugwuegbe, et al (2014)}, investigated that the impact of FDI on capital accumulation in Nigeria for the period of 1986-2012. The OLS estimation indicated that FDI, TCR, and INTR positively but insignificantly effect capital formation in the short-run whit GEXP exerting negative effect on GFCF. The result also indicated that in the long-run all the variables included in the model has a positive impact on GFCF with only FDI and TCR exerting a significant impact on capital accumulation in Nigeria for the period under review. \hyperref[b7]{Azlina, H. et. al. (2014)}, studied the impact of inward FDI on domestic investment between 1970 and 2011. The Johansen and Juselius co-integration technique employed in their study reveals that there is a long run relationship between domestic investment, FDI and economic growth. The error correction model suggests that there is a slow correction of disequilibrium of the investment model in the short run. The findings further suggest that FDI inflows in Malaysia "crowds out" domestic investment in the short run, in which an increase in one percentage point of inward FDI merely raises capital formation by 0.56 percentage point. \hyperref[b9]{Chakraborty, D. et al (2013)} examined the nexus between the investment and economic growth in India. The finding was that there is a unidirectional causality from India's economic growth to FDI and from FDI to domestic investment. \hyperref[b31]{Wu, G. et al (2010)} revealed that FDI has a crowding-in effect on regional economic development, i.e., each unit of FDI brings 2.4241 units of domestic investment. {\ref Agosin and Machado (2005)} analyzed FDI to Asia and Africa. The result of their analysis showed that FDI increases domestic investment one -to -one in those region. \hyperref[b16]{IPEK, et al. (2015)}, studied the effects of FDI on domestic investment for Turkey, Brazil, Russia, South Africa and Mexico by using time series data. The results showed that FDI crowd out domestic investment for Turkey and South Africa, crowding in effects for Russia. And statistically insignificance coefficients for Brazil and Mexico. Prasanna, (2010) studied the impact of FDI inflows on the DI in India and found that the direct impact of FDI inflows on DI in India is positive but the indirect impact is 'neutral' on the DI in the long run. There was no evidence that the increase in DI due to FDI inflows is greater than the amount of the FDI inflows in India. \hyperref[b17]{Lipsey (2000)} showed that neither inflows nor outflows of FDI are crucial to the level of capital formation in a given country. Ashraf and Herzer(2014) explored the different impact of green field investment M \& A on domestic investment. Their results confirm that M \& A do not have a significant impact on domestic investment. \hyperref[b14]{Goh, et al. (2012)} studied the Outward FDI and Domestic Investment. They observed that there is a long run relationship between Malaysia's inward FDI, outward FDI, domestic savings and domestic investment. Using the ARDL approach, they found that outward FDI exerts a negative effect on domestic investment while inward FDI yields a positive effect on domestic investment. The positive relationship may be due to Malaysia's FDI-friendly policy to attract high participation of foreign capital. D. \hyperref[b28]{Sunny, et al (2011)} analysed the Crowding In And Crowding Out Impact Of FDI on Domestic Investment: An Indo China. They used the Johansen co-integration test among gross fixed capital formation (used as the proxy of domestic investment), inward FDI and GDP demonstrates. The result showed that there was no long run relationship amongst the variables for China but there was cointegration in the case of India. Misun, J. and V. Tomsik (2002) analyze whether FDI crowded in or crowded out domestic investment in the Czech Republic, Hungary, and Poland in the 1990s by using a model of total investment that introduced, from the point of view of the recipient country, foreign direct investment as an exogenous variable. They find that there was evidence of a crowding out effect in {\ref Poland (1990} {\ref Poland ( -2000) )} and a crowding in effect in {\ref Hungary (1990} {\ref Hungary ( -2000) )} and the Czech Republic {\ref (1993)} {\ref (1994)} {\ref (1995)} {\ref (1996)} {\ref (1997)} {\ref (1998)} {\ref (1999)} {\ref (2000)}. \hyperref[b5]{Apergis, N., et al. (2006)} analyzes the dynamic linkages between FDI and domestic investment and their study is the first that tries to explain this relationship by panel cointegration techniques. They use annual data for 30 countries from America, Asia, Europe and Africa for the years 1992-2002, and detect a two-way causality between FDI and domestic investment as a result of the bivariate causality tests and cointegration between FDI and domestic investment for all the chosen country groups as a result of the multivariate cointegration tests. The bivariate model reveals evidence in favor of a positive long-run relationship, whereas long-run relationship is evident for Asian and African countries and not evident for American and European countries in the multivariate model. This shows that crowding out effect becomes dominant when American and European countries are considered. Omri and Kahouli (2014) found a statistically significant and positive effect of FDI on the domestic capital. Furthermore, the study concluded that there is a uni-directional causal relationship from foreign direct investment to domestic capital for the Middle East and North Africa regions.\par The above literature shows that there is a negative and positive effect of FDI on domestic investment. \section[{IV. Data}]{IV. Data}\par In this study we used annual time series data from 1978 to 2017. Data of GCF, FDI, M2 and EX collected from the world development indicators published by World Bank. \section[{V. Model Specefication}]{V. Model Specefication}\par The respective model of the study on the impact of FDI on Gross Capital Formation (used as the proxy of domestic investment) in Bangladesh can be written as below:GCF t = ? 0 + ? 1 FDI t + ? 2 M2 t ++ ? 3 EX t +? t ?????... (1)\par Here, ? t is error term which means there could be some other factors that can affect GCF and ? 0 is a scalar parameter, ? 1, ? 2 , and ? 3 are the slope coefficient parameters. All variables are transformed into log-linear form (LN). As a result the estimated results from these models represent elasticities. According to \hyperref[b27]{Shahbaz et al. (2013)}, modeling the log-log model specification will provide efficient results by mitigating the sharpness in time series data compared with the simple linear-linear specification.LNGCF t = ? 0 + ? 1 LNFDI t +? 2 LNM2 t + ? 3 LNEX t + ? t ?. (2)\par Here, LNGCF= log of Gross capital formation that measured in percentage of GDP. We employ Autoregressive Distributed Lag (ARDL) bound test to estimate the short run and long run dynamic relationship among the selected variables for the study. This test initially introduced by Pesaran and Shin (1999). One advantages of this test is that it is not necessary to be all variables I(1). It is applicable if some variables are I(0) and some are I(1). The another advantages of this approach is in the small sample size (30 to 80 observations) ARDL provides robust result. To employ this test firstly we test the stationarity of the considered variables by using Augment Dicked Fuller test (ADF) by {\ref Fuller (1979, 1981)} to see the order of integration. The ARDL is based on the assumption that the order of integrations of the variables are I(0)or I(1) \hyperref[b22]{(Ouattara, 2004)}. If any variables are integrated of I (2), the results can be spurious and the ADRL bound test is not suitable \hyperref[b24]{(Pesaran \& Shin, 1998)}. \section[{LNFDI= log of Foreign}]{LNFDI= log of Foreign}\par The equation for ARDL test is as below:Î?"LNGCF t =? 0 + ? ? i Î?"(LNGFCF) t-i + ? µ i Î?"(LNFDI) t-i + ?? i Î?"(LNM2) t-i + ?? i Î?"(LNEX) t-i + ? 1 LNGFCF t-1 +? 2 LNFDI t- 1 + ? 3 LNM2 t-1 + ? 4 LNEX t-1 +? t ??????????..(3)\par Where Î?" shows the first differences of the variables. The term ?'s represents the error correction dynamic and ?'s shows the long run relationship, ? 0 is the drift component and ? t is the white noise residuals. We analyzed the ARDL directly by using e-views 10.The null hypothesis of there is no co-integration among the variables against the alternative hypothesis of the existence of co-integration among the variables are given below:H0: ? 1 = ? 2 = ? 3 = ? 4 =0\par The F-statistics value is compared with the tabulated values of \hyperref[b20]{Narayan (2004)} for the small sample size (30 to 80 observations). If the F-statistics value is greater than the upper critical value, reject null hypothesis that means there exists a co-integration and H1:? 1 ?0, ? 2 ? 0, ? 3 ? 0, ? 4 ?0\par relationship or long run relationship among the variables. If the F-statistics value is less than the lower critical value, the null hypothesis cannot be rejected which means there is no co-integration among the variables. If, however, the F-statistics value lies within the upper and lower bound, the results are inconclusive. We employ the Akaike Information Criteria (AIC) to determine the optimal lag length for the study.\par The ARDL restricted ECM models is defined as:\par Where ? shows the speed of adjustment. At last conduct the stability and diagnostic test to ensure the goodness of fit of the chosen model. \section[{VI. EMPIRICAL FINDINGS a) Unit Root Test}]{VI. EMPIRICAL FINDINGS a) Unit Root Test}\par In order to check the stationary of the variables researchers used Augmented -Dickey Fuller (ADF) test. The result of the ADF test is given in table \hyperref[tab_0]{1}. \section[{b) Optimal Lag}]{b) Optimal Lag} \begin{quote} Figure 1\end{quote} \par According to the akaike information criteria the optimal lag of ARDL model is 3,1,3,0. Global Journal of Human Social Science Table \hyperref[tab_2]{2} shows the result of Bound F-test. The calculated F value for LNGCF is 9.825255 which is higher than all the lower and upper bound limits at 1\%, 2.25\%, 5\% and 10\%. So we can reject the null hypothesis "no relationship" that there exists a long run relationship between LNGCF and all other dependent variables used in this study. Table \hyperref[tab_3]{3} shows the long run coefficient of ARDL model. From the table we can see that the variable LNM2 bears the significant (at 10 percent) negative impact on LNGCF. That is if 1 percent increase in broad money gross capital formation will be decrease in 0.015 percent. The result also indicates that the impact of lagged LNFDI is negative and significant at 10 percent and that of LNEX is positive significant at 1 percent level of significance on LNGCF. \hyperref[tab_4]{4} shows that in the short run impact of D (LNFDI) on GCF is positive but insignificant. If current year FDI increases 1\% then GCF (domestic investment) increases 0.002 percent. Also, impact of exports on domestic investment is positive and significant at 5 percent level. If exports increase 1 percent, domestic investment will increase 0.05 percent.- LNGCF t = ? 0 + ? ? i Î?"(LNGFCF) t-i + ? µ i Î?"(LNFDI) t-i + ? ? i Î?"(LNM2) t-i + ?? i Î?"(LNEX) t-i + ?ECM t-I +?t ??? ??...(4) c) Bound Test \section[{d) Long -Run Estimates of Ardl Approach}]{d) Long -Run Estimates of Ardl Approach} \section[{e) Short Run Analysis of Ardl}]{e) Short Run Analysis of Ardl}\par Estimated results also indicate that the sign of lagged error correction representations (ECMt-1) is negative and statistically significant. The ECMt-1 shows the speed of adjustment toward equilibrium. Approximately, 24\% disequilibria from the previous year's shock converge on the long run equilibrium in the current year. From the result it can be seen that the R 2 value is 0.865698, which reflects that 86.56 percent differences of the dependent variable explained by the independent variables. The adjusted R 2 is 0.838838 or 83.88 percent. The Durbin-Watson (D-W) value is 2.123644, which confirms that there is no autocorrelation among the variables. The statistics' (R 2 , Adj. R 2 , D -W,) results show that our model is robust and well fitted. \section[{f) Stability Test}]{f) Stability Test}\par To check the stability of the model researchers used cusum and cusum square test. The result of the tests is given following figure 2 and figure 3: Global Journal of Human Social Science \section[{-}]{-}\par The result of Jarque Bera test shows that the value of the test is 0.522013 and p-value is 0.770276 which is greater than 0.05. that means we cannnot reject the null hypothesis that sates: the model is normally distributed. Hence the estimated model is normally distributed.\par ii Test for Serial Correlation\par The existence of serial correlation is tested by Breusch-Godfrey Serial Correlation LM Test. \section[{VII. Conclusion}]{VII. Conclusion}\par This study reveals the impact of FDI inflows on domestic investment of Bangladesh. To summarize, the outcome of the analysis has confirmed that FDI could 'crowd in' domestic investment and in the long run broad money has a negative and significant impact on domestic investment. On the other hand, exports positively influence domestic investment in Bangladesh both in short run and in long run. Bangladesh is now a lower middle income country and for achieving higher middle income status it needs to increase its domestic investment. In this research it has been proved that foreign direct investment positively affects domestic investment. Based on the above empirical findings we can suggest that Bangladesh should take foreign direct investment favorable policies which will help to ameliorate domestic investment. Both investments will increase productivity as well as create new employment opportunities to achieve targeted GDP growth rate to attain sustainable development goals of Bangladesh. \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.06185983827493262\textwidth}P{0.068733153638814\textwidth}P{0.21765498652291107\textwidth}P{0.12142857142857141\textwidth}P{0.08477088948787062\textwidth}P{0.14892183288409702\textwidth}P{0.14663072776280323\textwidth}} Variables\tabcellsep \tabcellsep \tabcellsep ADF Test\tabcellsep p-value\tabcellsep Decision\tabcellsep Conclusion\\ LNGCF\tabcellsep Level\tabcellsep Intercept and Trend\tabcellsep -5.421816\tabcellsep 0.0004\tabcellsep No Unit Root\tabcellsep Stationary\\ LNFDI\tabcellsep Level\tabcellsep Intercept and Trend\tabcellsep -4.219390\tabcellsep 0.0098\tabcellsep No Unit Root\tabcellsep Stationary\\ LNM2\tabcellsep Level\tabcellsep Intercept and Trend\tabcellsep -6.334252\tabcellsep 0.0000\tabcellsep No Unit Root\tabcellsep Stationary\\ LNEX\tabcellsep Level\tabcellsep Intercept and Trend\tabcellsep -2.530876\tabcellsep 0.3125\tabcellsep Unit Root\tabcellsep Non-stationary\\ \tabcellsep 1 st diff.\tabcellsep Intercept and Trend\tabcellsep -8.316245\tabcellsep 0.0000\tabcellsep No Unit Root\tabcellsep stationary\end{longtable} \par \caption{\label{tab_0}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_1}Table 1}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.1389937106918239\textwidth}P{0.07484276729559748\textwidth}P{0.1389937106918239\textwidth}P{0.32610062893081765\textwidth}P{0.17106918238993712\textwidth}} Test statistic\tabcellsep Value\tabcellsep Significant\tabcellsep Lower bound[I(0)]\tabcellsep Upper bound[I(1)]\\ F-statistic\tabcellsep 9.825255\tabcellsep 10\%\tabcellsep 2.37\tabcellsep 3.2\\ K\tabcellsep 3\tabcellsep 5\%\tabcellsep 2.79\tabcellsep 3.67\\ \tabcellsep \tabcellsep 2.5\%\tabcellsep 3.15\tabcellsep 4.08\\ \tabcellsep \tabcellsep 1\%\tabcellsep 3.65\tabcellsep 4.66\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: calculated by author}\end{longtable} \par \caption{\label{tab_2}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.16046728971962615\textwidth}P{0.20654205607476636\textwidth}P{0.15570093457943926\textwidth}P{0.21130841121495328\textwidth}P{0.11598130841121494\textwidth}} \tabcellsep \multicolumn{2}{l}{Dependent Variable: LNGCF}\tabcellsep \tabcellsep \\ Variables\tabcellsep Coefficient\tabcellsep Std. error\tabcellsep t-statistic\tabcellsep p-value\\ C\tabcellsep 0.505071\tabcellsep 0.137777\tabcellsep 3.665858\tabcellsep 0.0011\\ LNGCF(-1)\tabcellsep -0.246482\tabcellsep 0.063854\tabcellsep -3.860054\tabcellsep 0.0007\\ LNFDI(-1)\tabcellsep -0.003566\tabcellsep 0.001894\tabcellsep -1.883466\tabcellsep 0.0709\\ LNEX(-1)\tabcellsep 0.137226\tabcellsep 0.027468\tabcellsep 4.995949\tabcellsep 0.0000\\ LNM2\tabcellsep -0.015114\tabcellsep 0.007741\tabcellsep -1.952440\tabcellsep 0.0617\\ D(LNGCF(-1))\tabcellsep 0.445185\tabcellsep 0.060986\tabcellsep 7.299750\tabcellsep 0.0000\\ D(LNGCF(-2))\tabcellsep -0.315336\tabcellsep 0.062796\tabcellsep -5.021605\tabcellsep 0.0000\\ D(LNFDI)\tabcellsep 0.001877\tabcellsep 0.001513\tabcellsep 1.240316\tabcellsep 0.2259\\ D(LNEX)\tabcellsep 0.045288\tabcellsep 0.023603\tabcellsep 1.918777\tabcellsep 0.0661\\ D(LNEX(-1))\tabcellsep -0.073490\tabcellsep 0.031252\tabcellsep -2.351541\tabcellsep 0.0266\\ D(LNEX(-2))\tabcellsep -0.161359\tabcellsep 0.026985\tabcellsep -5.979603\tabcellsep 0.0000\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: author's calculation}\end{longtable} \par \caption{\label{tab_3}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.22137592137592135\textwidth}P{0.1816953316953317\textwidth}P{0.1963144963144963\textwidth}P{0.14828009828009828\textwidth}P{0.10233415233415233\textwidth}} Variables\tabcellsep Coefficient\tabcellsep Std. Error\tabcellsep t-statistic\tabcellsep p-value\\ D(LNGCF-1)\tabcellsep 0.445185\tabcellsep 0.054714\tabcellsep 8.136530\tabcellsep 0.0000\\ D(LNGCF-2)\tabcellsep -0.315336\tabcellsep 0.047061\tabcellsep -6.700626\tabcellsep 0.0000\\ D(LNFDI)\tabcellsep 0.001877\tabcellsep 0.001224\tabcellsep 1.533833\tabcellsep 0.1372\\ D(LNEX)\tabcellsep 0.045288\tabcellsep 0.020881\tabcellsep 2.168913\tabcellsep 0.0394\\ D(LNEX(-1))\tabcellsep -0.073490\tabcellsep 0.028032\tabcellsep -2.621693\tabcellsep 0.0144\\ D(LNEX(-2))\tabcellsep -0.161369\tabcellsep 0.023343\tabcellsep -6.912674\tabcellsep 0.0000\\ ECM(t-1)\tabcellsep -0.246482\tabcellsep 0.032738\tabcellsep -7.528889\tabcellsep 0.0000\\ R-squared\tabcellsep 0.865698\tabcellsep \multicolumn{3}{l}{Durbin -Watson stat 2.123644}\\ Adjusted R-squared\tabcellsep 0.838838\tabcellsep \tabcellsep \tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_4}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.05557692307692308\textwidth}P{0.013076923076923078\textwidth}P{0.19288461538461538\textwidth}P{0.008173076923076924\textwidth}P{0.032692307692307694\textwidth}P{0.03596153846153846\textwidth}P{0.05884615384615385\textwidth}P{0.006538461538461539\textwidth}P{0.23375\textwidth}P{0.1389423076923077\textwidth}P{0.07355769230769231\textwidth}} \tabcellsep \tabcellsep \multicolumn{7}{l}{Impact of Foreign Direct Investment on Domestic Investment in Bangladesh}\tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{g) Diagnostic Test}\tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep i Normality test\tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep 10\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Series: Residuals}\\ \tabcellsep 8\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Sample 1981 2017 Observations 37}\\ \tabcellsep 6\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Mean\tabcellsep -4.84e-16\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Median\tabcellsep -0.000123\\ \tabcellsep 2 4\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Maximum Minimum Std. Dev. Skewness Kurtosis\tabcellsep 0.028737 -0.024572 0.013061 0.056066 2.429010\tabcellsep 7 Year 2019\\ Volume XIX Issue VI Version I\tabcellsep 0\tabcellsep -0.02\tabcellsep -0.01\tabcellsep 0.00\tabcellsep 0.01\tabcellsep 0.02\tabcellsep 0.03\tabcellsep \multicolumn{2}{l}{Jarque-Bera 0.522013 Probability 0.770276}\tabcellsep Volume XIX Issue VI Version I\\ ( E )\tabcellsep \tabcellsep 1.4\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( E )\\ \tabcellsep \tabcellsep \multicolumn{8}{l}{92 94 96 98 00 02 04 06 08 10 12 14 16}\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{CUSUM of Squares}\tabcellsep \multicolumn{2}{l}{5\% Significance}\tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep © 2019 Global Journals\end{longtable} \par \caption{\label{tab_5}Figure 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.24285714285714283\textwidth}P{0.16190476190476188\textwidth}P{0.32380952380952377\textwidth}P{0.12142857142857141\textwidth}} F-statistic\tabcellsep 0.149707\tabcellsep Prob. F(2,24)\tabcellsep 0.8618\\ Obs*R-squared\tabcellsep 0.155910\tabcellsep Prob. Chi-Square(2)\tabcellsep 0.7962\end{longtable} \par \caption{\label{tab_6}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.85\textwidth}} iii Test of Homoscedasticity\end{longtable} \par \caption{\label{tab_7}Table 5}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.12386934673366833\textwidth}P{0.3331658291457286\textwidth}P{0.034170854271356785\textwidth}P{0.025628140703517585\textwidth}P{0.3331658291457286\textwidth}} \tabcellsep \multicolumn{2}{l}{F-statistic 0.224567}\tabcellsep \tabcellsep Prob. F(10,26) 0.9913\\ \tabcellsep \multicolumn{3}{l}{Obs* R-squared 2.941678}\tabcellsep Prob. Chi -square(10) 0.9828\\ \tabcellsep \multicolumn{3}{l}{Scaled explained SS 1.037872}\tabcellsep Prob. Chi-square(10) 0.9998\\ Table\tabcellsep 6 shows\tabcellsep that the\tabcellsep result\tabcellsep of\\ \multicolumn{2}{l}{heteroskedasticity test.}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_8}Table 6 :}\end{figure} \footnote{Year 2019 © 2019 Global Journals Impact of Foreign Direct Investment on Domestic Investment in Bangladesh} \footnote{Year 2019 © 2019 Global Journals Impact of Foreign Direct Investment on Domestic Investment in Bangladesh} \backmatter \begin{bibitemlist}{1} \bibitem[Ashraf and Herzer]{b6}\label{b6} ‘2014) the effect of greenfield investment and M \& A on domestic investment in developing countries’. A Ashraf , D Herzer . \textit{Applied Economics Letters} 21 (24) p. . \bibitem[Agosin and Mayer ()]{b1}\label{b1} M Agosin , R Mayer . \textit{Foreign Direct Investment in Developing Countries: Does It Crowd in Domestic Investment? 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