panel data analysis master thesis

Graduate Thesis Or Dissertation

Panel data analysis in the demographic and spatial econometric estimation of carbon dioxide emissions sources, 1960-2010 public deposited.

This dissertation presents three interrelated investigations focusing on the human dynamics of the carbon dioxide (CO 2 ) emissions. Recent research has used the STIRPAT analytical framework for identifying anthropogenic sources of these emissions by modeling changes of population, affluence, and technology at a range of scales. Despite its wide applicability and flexibility, the STIRPAT framework as currently applied has several shortcomings with regards to modeling the spatial nature of economic production. The three investigations in this dissertation address this shortcoming by bringing space and geography in stochastic environmental modeling using concepts drawn from economic geography, quantitative spatial analysis, and economic demography.

The first investigation, in Chapter 3, addresses time and space effects in panel data. Exploring the consequences for ignoring divergence in undifferentiated time-series, cross-sectional data, this investigation illustrates potential problems for coefficients estimated using standard panel data procedures. Known as `cluster confounding,' this effect results in the tendency for income to be positive over time, but negatively correlated with carbon dioxide between places, creating significant problems for estimation and inference in STIRPAT. I present a panel data regression technique for mitigating problems stemming from cluster confounding in panel data.

Chapter 4 examines the scale sensitivity hypothesis in STIRPAT, addressing long-standing criticisms of mathematical models in local-level analyses made within the literature of human and political ecology. Juxtaposing proximate physical sources of carbon dioxide emissions with distal 'theoretical' determinants, panel data estimates in this chapter illustrate weak support for the scale sensitivity hypothesis. By estimating labor force participation, age-structure, and retail employment as distal sources of CO 2 emissions, and using industrial-economic base as proximate sources of CO 2 , this analysis challenges expected scale sensitivity hypotheses.

Last, Chapter 5 investigates the demographic dividend in national-level carbon dioxide emissions increases. Using panel data from 1960-2009, I test the temporal coincidence of industrial development and growth in labor force participation as an independent variable signaling dividend effects, and attempt to understand these interaction effects as drivers positively correlated with CO 2 emissions. This analysis finds that demographic dividend effects are not statistically significant when strictly defined, and statistically significant when industrialization is more broadly defined.

• Roberts, Tyler Douglas
• Foote, Kenneth E.
• O'Neill, Brian C.
• Root, Elisabeth D.
• Downey, Liam
• Riosmena, Fernando
• University of Colorado Boulder
• carbon dioxide
• Dissertation
• In Copyright
• English [eng]

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Panel Data Analysis: A Guide for Nonprofit Studies

• Research Papers
• Published: 19 March 2021
• Volume 34 , pages 193–208, ( 2023 )

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• Yuhao Ba   ORCID: orcid.org/0000-0002-4148-2494 1 ,
• Jessica Berrett 2 &
• Jason Coupet 1  

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The growing push in nonprofit studies toward panel data necessitates a methodological guide tailored for nonprofit scholars and practitioners. Panel data analysis can be a robust tool in advancing the understanding of causal and/or more nuanced inferences that many nonprofit scholars seek. This study provides a walk-through of the assumptions and common modeling approaches in panel data analysis, as well as an empirical illustration of the models using data from the nonprofit housing sector. In addition, the paper compiles applications of panel data analysis by scholars in leading nonprofit journals for further reference.

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Panel Data Analysis

Non-linear Panel Data Models

Regarding different types of data, in general, time-series data refers to a collection of observations made chronologically on one subject, for instance, historical stock prices of one firm. In this case, in order to have enough variation, the length of a time series is an important determinant of the usefulness of such data, panel data, as we mentioned in the paper, entail observations made on the same sample of subjects over time. The difference here is in the data structure. Compared with time-series data, panel data have more than one cross-sectional subject. Accordingly, both its length (i.e., number of time periods) and its width (i.e., number of cross-sectional subjects) jointly decide the usefulness of a panel dataset. Additionally, some studies also use the term time-series cross-sectional (TSCS) data to refer to panel data (Lewis-Beck et al., 2004), while some others suggest that TSCS data have comparatively fewer cross-sectional subjects than panel data (see Bell & Jones, 2015 ). As for the term longitudinal data, researchers often use it interchangeably with the term panel data (Frees, 2004 ).

Here, a necessary yet challenging sampling strategy in panel data analysis is to keep track of the same sampled subjects until the completion of data collection, as attrition of subjects out of the sample could lead to incorrect inferences (Baltagi, 2008 ).

Here, we acknowledge that it is common for nonprofit scholars to choose other publication outlets such as Journal of Public Administration Research and Theory (e.g., Cheng, 2018; de Wit & Bekkers, 2017 ) and Public Performance & Management Review (e.g., Pandey & Johnson, 2019 ). Given the broad coverage of these journals, however, we decided to narrow our focus on VOLUNTAS , NML , and NVSQ , which focus solely on nonprofit studies.

In practice, the presence of different levels of multicollinearity is common given the implicit and/or explicit interconnectedness of many variables in social sciences. The disadvantage of having multicollinearity is that it could lead to relatively large estimated standard errors for the coefficient estimates of independent variables that are correlated with others, which would undermine their statistical significance (Allen, 1997 ).

Here, it is important to acknowledge that directly adding an LDV as an independent variable could violate the OLS assumption that independent variables do not correlate with the error term. This is because the error term is supposed to encapsulate all the variation that is left in the dependent variable but are not explained by the independent variable(s). An LDV is thus likely to correlate with such remaining variation as it represents the variation in the DV that is from the previous period. This issue would be further complicated if the model specification includes panel-specific error terms such as FE terms (Zhu, 2013 ). In this case, we do not recommend directly adding an LDV to account for the dynamics process. We introduce this approach because it is our intention to provide the basic assumptions and modeling approaches that are involved in panel data analysis. To deal with such potential correlations that an LDV might induce, however, researchers could rely on instrumentation techniques such as a second-order LDV (the DV that is two periods before the current one; Anderson & Hsiao, 1981 ) or the generalized method of movements (GMM) estimator proposed by Arellano & Bond ( 1991 ).

Allen, M. P. (1997). The problem of multicollinearity. In Understanding regression analysis, pp. 176–180. Springer. Doi: https://doi.org/10.1007/978-0-585-25657-3_37

Amirkhanyan, A. A., Kim, H. J., & Lambright, K. T. (2009). Faith-based assumptions about performance: Does church affiliation matter for service quality and access? Nonprofit and Voluntary Sector Quarterly, 38 (3), 490–521. https://doi.org/10.1177/0899764008320031

Article   Google Scholar  

Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76 (375), 598–606. https://doi.org/10.2307/2287517

Arellano, M. (2003). Panel data econometrics . Oxford university press. https://books.google.com/books/about/Panel_Data_Econometrics.html?id=--QBWO67VjsC

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58 (2), 277–297. https://doi.org/10.2307/2297968

Bae, K. B., & Sohn, H. (2018). Factors contributing to the size of nonprofit sector: Tests of government failure, interdependence, and social capital theory. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29 (3), 470–480. https://doi.org/10.1007/s11266-017-9888-3

Baltagi, B. H. (2008). Econometric analysis of panel data . John Wiley & Sons.

Google Scholar  

Baltagi, B. H., Bresson, G., & Pirotte, A. (2003). Fixed effects, random effects or Hausman–Taylor?: A pretest estimator. Economics Letters, 79 (3), 361–369. https://doi.org/10.1016/S0165-1765(03)00007-7

Bartels, B. (2015). Beyond ‘fixed versus random effects’: A framework for improving substantive and statistical analysis of panel, TSCS, and multilevel data. In R. Franzese (Ed.), Quantitative research in political science . SAGE Publications Ltd. https://us.sagepub.com/en-us/nam/quantitative-research-inpolitical-science/book243299

Beckman, C. M., & Burton, M. D. (2008). Founding the future: path dependence in the evolution of top management teams from founding to IPO. Organization Science, 19 (1), 3–24. https://doi.org/10.1287/orsc.1070.0311

Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3 (1), 133–153.

Bromley, P., Schofer, E., & Longhofer, W. (2018). Organizing for education: A cross-national, longitudinal study of civil society organizations and education outcomes. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29 (3), 526–540. https://doi.org/10.1007/s11266-018-9979-9

Brooks, A. C. (2000). Public subsidies and charitable giving: Crowding out, crowding in, or both? Journal of Policy Analysis and Management, 19 (3), 451–464. https://doi.org/10.1002/1520-6688(200022)19:3%3c451::AID-PAM5%3e3.0.CO;2-E

Bun, M. J., & Sarafidis, V. (2013). Dynamic panel data models. UvA-Econometrics Discussion Paper, 1.

Calabrese, T. D. (2011). The Accumulation of Nonprofit Profits: A Dynamic Analysis. Nonprofit and Voluntary Sector Quarterly, 41 (2), 300–324. https://doi.org/10.1177/0899764011404080

Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata, Vol. 2. Stata press College Station.

Cheng, Y. (2018). Nonprofit spending and government provision of public services: Testing theories of government-nonprofit relationships. Journal of Public Administration Research and Theory, 29 (2), 238–254. https://doi.org/10.1093/jopart/muy054

Clark, T. S., & Linzer, D. A. (2015). Should I use fixed or random effects? Political Science Research and Methods, 3 (2), 399–408.

Cooper, H. M. (1989). Integrating research: A guide for literature reviews . Sage Publications.

Coupet, J. (2018). Exploring the link between government funding and efficiency in nonprofit colleges. Nonprofit Management and Leadership, 29 (1), 65–81. https://doi.org/10.1002/nml.21309

Coupet, J., & Berrett, J. L. (2019). Toward a valid approach to nonprofit efficiency measurement. Nonprofit Management and Leadership, 29 (3), 299–320. https://doi.org/10.1002/nml.21336

David, R. J., & Han, S. (2004). A systematic assessment of the empirical support for transaction cost economics. Strategic Management Journal, 25 (1), 39–58.

De Los Mozos, I. S. L., Rodríguez Duarte, A., & Rodríguez Ruiz, Ó. (2016). Resource dependence in non-profit organizations: Is It harder to fundraise if you diversify your revenue structure? VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27 (6), 2641–2665. https://doi.org/10.1007/s11266-016-9738-8

de Wit, A., & Bekkers, R. (2017). Government support and charitable donations: A meta-analysis of the crowding-out hypothesis. Journal of Public Administration Research and Theory, 27 (2), 301–319. https://doi.org/10.1093/jopart/muw044

Diebold, J., & Coggburn, J. D. (2018). The determinants and opportunity costs of external management fees for state-administered pension plans. Public Budgeting and Finance, 38 (4), 3–31. https://doi.org/10.1111/pbaf.12207

Einolf, C. J. (2017). Parents’ charitable giving and volunteering: Are they influenced by their children’s ages and life transitions? Evidence from a longitudinal study in the United States. Nonprofit and Voluntary Sector Quarterly, 47 (2), 395–416. https://doi.org/10.1177/0899764017737870

Elhorst, J. P. (2003). Specification and estimation of spatial panel data models. International Regional Science Review, 26 (3), 244–268. https://doi.org/10.1177/0160017603253791

Faulk, L., & Derrick-Mills, T. (2015). Shaping the Future of Nonprofit Data . Shaping the Future of Nonprofit Data. https://www.american.edu/spa/news/shaping-future-of-nonprofit-data-07062015.cfm

Frees, E. W. (2004). Longitudinal and panel data: Analysis and applications in the social sciences . Cambridge University Press.

Book   Google Scholar  

Galaskiewicz, J., & Bielefeld, W. (1998). Nonprofit Organizations in an Age of Uncertainty: A Study of Organizational Change . A. de Gruyter.

Garson, G. D. (2013). Longitudinal Analysis . Statistical Associates Publishers.

Glanville, J. L., Paxton, P., & Wang, Y. (2015). Social capital and generosity: A multilevel analysis. Nonprofit and Voluntary Sector Quarterly, 45 (3), 526–547. https://doi.org/10.1177/0899764015591366

Greene, W. H. (2003). Econometric Analysis (5th, illustrated ed.). Prentice Hall. https://books.google.com/books?id=JJkWAQAAMAAJ

Grossman, A., & Rangan, V. K. (2001). Managing multisite nonprofits. Nonprofit Management and Leadership, 11 (3), 321–337. https://doi.org/10.1002/nml.11306

Habitat for Humanity. (2014, November 11). HUD awards $4.2 million capacity building grant to Habitat for Humanity . https://www.habitat.org/newsroom/11-11-2014-HUD-Capacity-Building

Habitat for Humanity. (2020). Our mission, vision, and principles. https://www.habitat.org/about/mission-and-vision

Halaby, C. N. (2004). Panel models in sociological research: Theory into practice. Annual Review of Sociology, 30 (1), 507–544. https://doi.org/10.1146/annurev.soc.30.012703.110629

Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49 (2), 149–164. https://doi.org/10.2307/2095567

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46 (6), 1251–1271. https://doi.org/10.2307/1913827

Heist, H. D., & Vance-McMullen, D. (2019). Understanding donor-advised funds: how grants flow during recessions. Nonprofit and Voluntary Sector Quarterly , 48 (5), 1066–1093. https://doi.org/10.1177/0899764019856118 .

Heutel, G. (2014). Crowding out and crowding in of private donations and government grants. Public Finance Review, 42 (2), 143–175. https://doi.org/10.1177/1091142112447525

Hsiao, C. (2007). Panel data analysis—Advantages and challenges. TEST, 16 (1), 1–22. https://doi.org/10.1007/s11749-007-0046-x

Hsiao, C. (2014). Analysis of panel data . Cambridge University Press.

Hutcheson, G. D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models . SAGE Publications, Ltd. http://methods.sagepub.com/book/the-multivariate-social-scientist

James, R. N., III. (2018). Cash is not king for fund-raising: Gifts of noncash assets predict current and future contributions growth. Nonprofit Management and Leadership, 29 (2), 159–179. https://doi.org/10.1002/nml.21334

Jaskyte, K. (2012). Boards of directors and innovation in nonprofit organizations. Nonprofit Management and Leadership, 22 (4), 439–459. https://doi.org/10.1002/nml.21039

Jensen, U. T., & Vestergaard, C. F. (2016). Public service motivation and public service behaviors: Testing the moderating effect of tenure. Journal of Public Administration Research and Theory, 27 (1), 52–67. https://doi.org/10.1093/jopart/muw045

Keele, L., & Kelly, N. J. (2006). Dynamic models for dynamic theories: The ins and outs of lagged dependent variables. Political Analysis, 14 (2), 186–205. https://doi.org/10.1093/pan/mpj006

Kim, M. (2015). Socioeconomic diversity, political engagement, and the density of nonprofit organizations in US counties. The American Review of Public Administration, 45 (4), 402–416.

Kim, Y. H., & Kim, S. E. (2018). What accounts for the variations in nonprofit growth? a cross-national panel study. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , 29 (3), 481–495. https://doi.org/10.1007/s11266-016-9752-x .

Light, R. J., & Pillemer, D. B. (1984). Summing Up: The Science of Reviewing Research . Harvard University Press. https://books.google.com/books?id=5Y14EDR7UicC

Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61 (S1), 631–652. https://doi.org/10.1111/1468-0084.0610s1631

Maier, F., Meyer, M., & Steinbereithner, M. (2016). Nonprofit organizations becoming business-like: A systematic review. Nonprofit and Voluntary Sector Quarterly, 45 (1), 64–86.

Matsunaga, Y., Yamauchi, N., & Okuyama, N. (2010). What determines the size of the nonprofit sector?: a cross-country analysis of the government failure theory. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , 21 (2), 180–201. https://doi.org/10.1007/s11266-010-9125-9 .

Mayer, W. J., Wang, H., Egginton, J. F., & Flint, H. S. (2014). The impact of revenue diversification on expected revenue and volatility for nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 43 (2), 374–392.

Mosley, J. E., & Galaskiewicz, J. (2015). The relationship between philanthropic foundation funding and state-level policy in the era of welfare reform. Nonprofit and Voluntary Sector Quarterly , 44 (6), 1225–1254. https://doi.org/10.1177/0899764014558932 .

Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica: Journal of the Econometric Society , pp. 1417–1426.

Nikolova, M. (2015). Government funding of private voluntary organizations: Is there a crowding-out effect? Nonprofit and Voluntary Sector Quarterly, 44 (3), 487–509. https://doi.org/10.1177/0899764013520572

Paarlberg, L. E., & Hwang, H. (2017). The heterogeneity of competitive forces: the impact of competition for resources on united way fundraising. Nonprofit and Voluntary Sector Quarterly , 46 (5), 897–921. https://doi.org/10.1177/0899764017713874 .

Pandey, S. K., & Johnson, J. M. (2019). Nonprofit management, public administration, and public policy: Separate, subset, or intersectional domains of inquiry? Public Performance and Management Review, 42 (1), 1–10. https://doi.org/10.1080/15309576.2018.1557382

Pedrini, M., & Ferri, L. M. (2016). Doing well by returning to the origin. Mission drift, outreach and financial performance of microfinance institutions. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , 27 (6), 2576–2594. https://doi.org/10.1007/s11266-016-9707-2 .

Plümper, T., Troeger, V. E., & Manow, P. (2005). Panel data analysis in comparative politics: Linking method to theory. European Journal of Political Research, 44 (2), 327–354. https://doi.org/10.1111/j.1475-6765.2005.00230.x

Ranucci, R., & Lee, H. (2019). Donor influence on long-term innovation within nonprofit organizations. Nonprofit and Voluntary Sector Quarterly, 48 (5), 1045–1065. https://doi.org/10.1177/0899764019843346

Rao, C. R., & Toutenburg, H. (1995). Linear Models: Least Squares and Alternatives (1st ed.). Springer. https://doi.org/10.1007/978-1-4899-0024-1

Rosenbaum, P. R. (2005). Heterogeneity and Causality. The American Statistician, 59 (2), 147–152. https://doi.org/10.1198/000313005X42831

Rotolo, T., Wilson, J., & Dietz, N. (2014). Volunteering in the United States in the aftermath of the foreclosure crisis. Nonprofit and Voluntary Sector Quarterly, 44 (5), 924–944. https://doi.org/10.1177/0899764014546669

Shearer, R., & Clark, T. (2016). Are we drawing the correct conclusions? Regression analysis in the nonprofit literature. Canadian Journal of Nonprofit and Social Economy Research , 7(1). https://doi.org/10.22230/cjnser.2016v7n1a213

Sydow, J., Schreyögg, G., & Koch, J. (2009). Organizational path dependence: Opening the black box. The Academy of Management Review, 34 (4), 689–709.

Szper, R. (2013). Playing to the test: Organizational responses to third party ratings. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 24 (4), 935–952. https://doi.org/10.1007/s11266-012-9290-0

Tsay, W.-J., & Chung, C.-F. (2000). The spurious regression of fractionally integrated processes. Journal of Econometrics, 96 (1), 155–182. https://doi.org/10.1016/S0304-4076(99)00056-

Wawro, G. (2002). Estimating dynamic panel data models in political science. Political Analysis, 10 (1), 25–48. https://doi.org/10.1093/pan/10.1.25

Weimar, D., Wicker, P., & Prinz, J. (2015). Membership in nonprofit sport clubs: A dynamic panel analysis of external organizational factors. Nonprofit and Voluntary Sector Quarterly, 44 (3), 417–436.

Wicker, P., & Frick, B. (2016). Recruitment and retention of referees in nonprofit sport organizations: the trickle-down effect of role models. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , 27 (3), 1304–1322. https://doi.org/10.1007/s11266-016-9705-4 .

Winters, M. S., & Rundlett, A. (2015). The challenges of untangling the relationship between participation and happiness. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 26 (1), 5–23. https://doi.org/10.1007/s11266-014-9500-z

Witt, A., Kurths, J., & Pikovsky, A. (1998). Testing stationarity in time series. Physical Review E, 58 (2), 1800–1810. https://doi.org/10.1103/PhysRevE.58.1800

Wooldridge, J. M. (2005). Fixed-effects and related estimators for correlated random-coefficient and treatment-effect panel data models. Review of Economics and Statistics, 87 (2), 385–390.

Wooldridge, J.M. (2013). Introductory Econometrics: A Modern Approach (5th ed.). Cengage Learning. https://books.google.com/books?id=C0KHwUKxys0C

Woronkowicz, J., & Nicholson-Crotty, J. (2017). The effects of capital campaigns on other nonprofits’ fundraising. Nonprofit Management and Leadership, 27 (3), 371–387. https://doi.org/10.1002/nml.21255

Yi, H., & Chen, W. (2019). Portable innovation, policy wormholes, and innovation diffusion. Public Administration Review, 79 (5), 737–748. https://doi.org/10.1111/puar.13090

Yu, Z. (2016). The effects of resources, political opportunities and organisational ecology on the growth trajectories of AIDS NGOs in China. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27 (5), 2252–2273. https://doi.org/10.1007/s11266-016-9686-3

Zhu, L. (2013). Panel data analysis in public administration: Substantive and statistical considerations. Journal of Public Administration Research and Theory, 23 (2), 395–428. https://doi.org/10.1093/jopart/mus064

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Ba, Y., Berrett, J. & Coupet, J. Panel Data Analysis: A Guide for Nonprofit Studies. Voluntas 34 , 193–208 (2023). https://doi.org/10.1007/s11266-021-00342-w

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Some Estimation Methods for Dynamic Panel Data Models

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Economic Growth in ASEAN-4 Countries: A Panel Data Analysis

Nooraini, Saidin (2012) Economic Growth in ASEAN-4 Countries: A Panel Data Analysis. Masters thesis, Universiti Utara Malaysia.

This research studies the impact of economic variables which are foreign direct investment (FDI), openness and gross fixed capital formation to economic growth which indicates using gross domestic product (GDP). Data is collected from 1981 until 2008 using World Development Indicator CD-ROM. This research estimates using panel data estimation. In order to test the significance of the variable, this research uses panel unit root test. Result of panel data unit root test shows that all variables in panel unit root test are significant and stationary at first difference 5 percent level of significant. In addition, the impact of variables to GDP is estimated using three panel estimation models which are called pooled model (pooled), fixed effects model (FEM) and random effects model (REM). Result of three particular models in panel estimation give the stationary at 5 percent level of significant for all variables involved. Variable of openness for pooled and random effects model indicate negative relation with GDP. Meanwhile, other variables in all models show positive relation with GDP. Goodness to fit in this research for all models demonstrate high value which 0.74 (pooled), 0.87 (FEM) and 0.73 (REM). Furthermore, Hausman test is employed to this research in order to choose the best model. Result for this test suggests rejecting null hypothesis because of the value of p is 0.00 (p<.05). On other words, rejecting of null hypothesis may conclude that the FEM will apply. Thus, this research describes that all variables are correlated with each other and also have the positive relationship to GDP. Hence, all variables may lead economic growth boost when they are increase whereas FDI becomes the most efficient variable in order to assist economic growth and followed by openness and gross fixed capital formation. Otherwise, the result in Ordinary Least Squares (OLS) which implies in this study as well test all variables stationary at 5 percent level of significant. These shows only gross fixed capital formation is significant to growth and contributes the positive effect to GDP in each ASEAN-4 countries. However, OLS estimation result for Indonesia shows the other variable has significant to growth which is openness; while it gives the negative affect the GDP. Instead of Indonesia, openness is not significant at other ASEAN-4 countries such as Malaysia, Thailand and Philippines. Besides, other variable is FDI also not significant in the case of all ASEAN-4 countries. It means that, openness does not correlated to growth for Malaysia, Thailand and Philippines countries; while FDI is not correlated to growth for all ASEAN-4 countries in this study.

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• Economic growth and Inflation: A panel data analysis
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Abstract [en].

One of the most important objectives for any countries is to sustain high economic growth. Even though there are main factors that affect economic growth, the concern of this paper is only about inflation. The relationship between economic growth and inflation is debatable. The first objective of this study is to investigate the relationship between inflation and economic growth. This study uses panel data which includes 13 SSA countries from 1969 to 2009. To analyze the data the model is formed by taking economic growth as dependent variable and four variables (i.e. inflation, investment, population and initial GDP) as independent variables. The result indicates that there is a negative relationship between economic growth and inflation. This study is also examined the causality relationship between economic growth and inflation by using panel Granger causality test. Panel granger causality test shows that inflation can be used in order to predict growth for all countries in the sample, while the opposite it is only true for Congo, Dep. Rep and Zimbabwe.

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