Economic Phenomena, Economic Analysis, and Its Statistical Applicability: Focusing on the Developments of Econometrics and Challenging Issues

- Journal title : Korean Journal of Applied Statistics
- Volume 28, Issue 6, 2015, pp.1075-1091
- Publisher : The Korean Statistical Society
- DOI : 10.5351/KJAS.2015.28.6.1075

Title & Authors

Economic Phenomena, Economic Analysis, and Its Statistical Applicability: Focusing on the Developments of Econometrics and Challenging Issues

Kim, Chiho;

Kim, Chiho;

Abstract

This paper reviews the developments of econometric analysis and seeks a statistical applicability to current economic phenomena. During the last half century, economic analysis has progressed continuously, analyzing and predicting a broad variety of economic phenomena. In the center of this progress lies the remarkable contribution of econometrics and mathematical statistics. New economic research environment has been recently created via developments of IT and the spread of internet and SNSs. Economic phenomena has become increasingly complicated along with more volatile and sophisticated economic analysis. In that context, it can be suggested that there is a need to move beyond current economic paradigms and adapt new approaches such as complex theory and econophysics, all of which posits as a challenge for econometrics and statistics.

Keywords

economic analysis system;econometrics;mathematical statistics;complex system;econophysics;

Language

Korean

References

1.

Amemiya, T. (1981). Qualitative response models: A survey, Journal of Economic Literature, 19, 483-536.

2.

Amemiya, T. (2009). Thirty-five years of journal of econometrics, Journal of Econometrics, 148, 178-185.

3.

Arellano, M. (2001). Panel data models: Some recent developments, in Handbook of Econometrics, 5, J.J. Heckman and E. Leamer (Eds), North-Holland, Amsterdam.

4.

Bai, J. (2013). Panel data model: Factor analysis, In Advances in Economics and Econometrics, 10th World Congress, 3, Eds. by A. Acemoglu, M. Arellano, and E. Dekel, 437-484.

5.

Blanchard, O. J. and Quah, D. (1989). The dynamic effects of aggregate demand and supply distributions, American Economic Review, 79, 655-673.

6.

Bollerslev, T. (1986). Generalized autoregressive conditional Hetero-Skedasticity, Journal of Econometrics, 31, 307-327.

7.

Bollerslev, T. (2001). Financial econometrics: Past developments and future challenges, Journal of Econometrics, 100, 41-51.

8.

Box, G. E. P. and Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control, Holden Day, San Francisco.

9.

Buchanan, M. (2013). Forecast: What Extreme Weather Can Teach Us about Economics, Bloomsbury Publishing, New York.

10.

11.

Chang, C., Allen, D. and McAleer, M. (2013). Recent developments in financial economics and econometrics:An overview, North American Journal of Economics and Finance, 26, 217-226.

12.

Complex System Network, Min, B. and Kim, C. eds. (2006). Complex System Workshop, SERI, Korea. (in Korean)

13.

Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation, Econometrica, 50, 987-1007.

14.

Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error correction representation, estimation, and testing, Econometrica, 55, 251-276.

15.

Epstein, R. J. (1987). A History of Econometrics, North-Holland, Amsterdam.

17.

Fehr, E. and Rangel, A. (2011). Neuroeconomic foundations of economic choice - Recent advances, The Journal of Economic Perspectives, 25, 3-30.

19.

Friedman, M. (2011). The stability of general equilibrium - What do we know and why is it important?, In P. Bridel, ed., General Equilibrium Analysis: A Century after Walras, Rourledge, London.

20.

Fuller, W. A. (1976). Introduction to Statistical Time Series, Wiley and Sons, New York.

21.

Golan, A. (2007). Information and entropy econometrics, Journal of Econometrics, 138, 387-397.

22.

Goldberger, A. S. (1964). Econometric Theory, Wiley and Sons, New York.

23.

Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators, Econometrica, 50, 1029-1054.

24.

Hausman, J. (2001). Microeconometrics, Journal of Econometrics, 20, 33-35.

25.

Hausman, J. and Wise, D. A. (1978). A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences, Econometrica, 46, 403-426.

28.

Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-366.

29.

Kim, E. H., Morse, A. and Zingales, L. (2006). What Has mattered to economics since 1970, Journal of Economic Perspectives, 20, 189-202.

30.

Klein, L. (1950). Economic Fluctuations in the United States 1921-1941, Wiley and Sons, New York.

31.

Klein, L. and Goldberger, A. (1955). An Econometric Model of United States, 1929-1952, North-Holland, Amsterdam.

34.

Lee, G. and Hwang, S. (2014). Development of business index using big-data, The Bank of Korea Economic Analysis, 20, 1-39.

35.

Maddala, G. S. (1983). Limited-Dependent and Qualitative Variables in Econometrics, Cambridge University Press, Cambridge.

36.

Mandelbrot, B. (1963). The variance of certain speculative prices, Journal of Business, 36, 394-419.

37.

McFadden, D. (1982). Qualitative response models, In Handbook of Econometrics, 2, Z. Griliches and M. Intriligator (Eds), North-Holland, Amsterdam.

38.

Muller, U. K. (2008). The impossibility of consistent discrimination between I(0) and I(1) processes, Econometric Theory, 24, 616-630

39.

40.

Sargent, T. and Sims, C. (1977). Business cycle modelling without pretending to have too much a priori theory, in C. Sims, ed. New Methods of Business Cycle Research, FRB Minneapolis.

41.

Schorfheide, F. (2013). Estimation and evaluation of DSGE models: Progress and challenges, In Advances in Economics and Econometrics, 10th World Congress, 3, A. Acemoglu, M. Arellano, and E. Dekel (Eds), 184-230.

43.

Stigler, G. J. (1984). Economics-the imperial science?, Scandinavian Journal of Economics, 86, 301-313.

44.

Stigler, S. (2002). Statistics on the Table: The History of Statistical Concepts and Methods, Harvard University Press, Cambridge.