Abstract
In this edition the authors draw on further research on multivariate time series methods that are used and might be used in the analysis of economic and financial data. The approach is firmly multivariate and starts with linear models: time series models where the data are taken to be stationary. This implies that the series are generally differenced and that likely relationships relate to growth rates in economics and returns in finance. In this context, the model can be transformed to derive long-run relations in the rate of growth of the economy and this links with other economic factors or to long-run asset price relations. Impulse response functions are then derived and their uniqueness is considered as well as forecasting. The latter is treated in brief as these are the primary concerns of the book by Clements (2005). Forecasting is clearly an important direct and indirect outcome of time series modelling, but for the economics profession such models have meaning related to the identification of economic phenomena and the determination of policy. Forecast performance is also an important component in model selection, for economist, financial analyst and pure statistician. For the economic and financial analyst the nature of the model might be suggestive of the nature of the economy, either in a long-run or a short-run sense. Hence, models that make no economic sense or are not able to be identified might be considered to be inconsistent with theory and as a result may only have a statistical value. There is also an interest in models being well specified and stable over time. There may be reasons for instability by virtue of policy changes, but the interest is then directed to the invariant structures that have implications for exogeneity. In particular the idea that relations invariant to series that are not stable can be viewed as indicative of super exogeneity. This focuses attention on parameters, which are often linked to the long run. Having distinguished between the exogenous and endogenous series, one will then be drawn towards econometric identification. This is the capacity to detect economic structure from the long-run or short-run parameters, as compared with time series identification that relates to the dynamic process driving the short-run behaviour of the data. Causality is related to exogeneity and this considers the extent to which it is possible to detect the forcing variables or to forecast conditional on a subset of the data either in the short run or in the long run. Causality is sensitive to the nature of the system and in the short run relates to the parameters of certain dynamic processes being set to zero. This applies in the long run when some of the variables satisfy the conditions appropriate for cointegrating exogeneity (Hunter, Econ Lett 34:33-35, 1990), but with the exception of systems with only two variables or one cointegrating vector, this is also a requirement for the effect of long-run causality in the short run.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
It should be noted that the impulse response function solved from the VAR is not unique (Lippi and Reichlin 1994) and any findings on causality depend on the structure of the model estimated (Hendry and Ericsson 1990).
- 4.
Keynes discusses the latent nature of expectations, the problems with dynamic specification, measurement error, the role of forecast performance and structural breaks.
References
Bachelier, L. (1900). Théorie de la speculation. Paris: Gauthier-Villars.
Barten, A. P. (1969). Maximum likelihood estimation of an almost complete set of demand equations. European Economic Review, 1, 7–73.
Bauwens, L., & Hunter J. (2000). Identifying Long-Run Behaviour with Non-Stationary data. Discussion Paper CORE, The Catholic University, Louvain-La Nueve DP 2000/43.
Boswijk, H. P. (1996). Cointegration, identification and exogeneity: Inference in structural error correction models. Journal of Business and Economics and Statistics, 14, 153–160.
Burke, S. P., & Hunter, J. (2011). Long-run equilibrium price targeting. Quantitative and Qualitative Analysis in Social Sciences, 5, 26–36.
Clements, M. P. (2005). Evaluating econometric forecasts of economic and financial variables. Basingstoke: Palgrave-Macmillan.
Davidson, J. E. H., Hendry, D. F., Srba, F., & Yeo, S. (1978). Econometric modelling of the aggregate time series relationships between consumers expenditure and income in the United Kingdom. Economic Journal, 88, 661–692.
Davidson, R., & MacKinnon, J. G. (2004). Econometric theory methods. New York: Oxford University Press.
Deaton, A. S. (1975). Models and projections of demand in post-war Britain. London: Chapman and Hall.
Deaton, A. S., & Muellbauer, J. N. J. (1980). An almost ideal demand system. American Economic Review, 70, 312–326.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error-correction: Representation, estimation and testing. Econometrica, 55, 251–276.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25, 383–417.
Fisher, I. (1930). The theory of interest. New York: The Macmillan Company.
Friedman, M., & Schwartz A.J. (1982) Monetary Trends in the United States and the United Kingdom: Their relation to income, prices, and interest rates, pp. 1867–1975. Chicago: University of Chicago Press.
Granger, C. W. J., & Newbold, P. (1974). Spurious regression in econometrics. Journal of Econometrics, 2, 111–120.
Granger, C. W. J., & Weiss, A. A. (1983). Time series analysis of error-correcting models. In Studies in econometrics, time series, and multivariate statistics (pp. 255–278). New York: Academic.
Granger, C. W. J., & Newbold, P. (1986). Forecasting with economic time series. New York: Academic.
Hall, R. E. (1978). Stochastic implications of the life cycle-permanent income hypothesis: Theory and evidence. Journal of Political Economy, 86, 971–987.
Harvey, A. C. (1989). Forecasting structural time series models and the Kalman filter. Cambridge: Cambridge University Press.
Hendry, D. F. (1980). Econometrics – alchemy or science. Economica, 47, 387–406.
Hendry, D. F., & Ericsson, N. R. (1990). An econometric analysis of U.K. money demand in monetary trends in the United States and the United Kingdom by Milton Friedman and Anna Schwartz. American Economic Review, 81, 8–38.
Hendry, D. F., & Mizon, G. E. (1978). Serial correlation as a convenient simplification not a nuisance: A comment on a study of the demand for money by the Bank of England. Economic Journal, 88, 549–563.
Hendry, D. F., & Richard, J. F. (1982). On the formulation of empirical models in dynamic econometrics. Journal of Econometrics, 20, 3–33.
Hendry, D. F., & Richard, J. F. (1983). The econometric analysis of economic time series. International Statistical Review, 51, 111–163.
Hull, J. (2015). Options, futures and other derivatives. London: Prentice Hall.
Hunter, J. (1989). Dynamic modelling of expectations: With particular reference to the labour market. Unpublished Ph.D. manuscript, London School of Economics.
Hunter, J. (1990). Cointegrating exogeneity. Economics Letters, 34, 33–35.
Hunter, J. (1992). Tests of cointegrating exogeneity for PPP and uncovered interest rate parity for the UK. Journal of Policy Modelling,Special Issue: Cointegration, Exogeneity and Policy Analysis, 14, 453–463.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231–254.
Johansen, S. (1992). Testing weak exogeneity and the order of cointegration in UK money demand data. Journal of Policy Modelling, Special Issue: Cointegration, Exogeneity and Policy Analysis, 14, 313–334.
Johansen, S. (1995). Likelihood-inference in cointegrated vector auto-regressive models. Oxford: Oxford University Press.
Johansen, S., & Juselius, K. (1992). Some structural hypotheses in a multivariate cointegration analysis of the purchasing power parity and the uncovered interest parity for UK. Journal of Econometrics, 53, 211–244.
Keynes, J. M. (1939). Professor Tinbergen’s method. Reprinted in the collected writings of John Maynard Keynes (Vol. XIV, pp. 306–318).
Leijonhufvud, A. (1968). On Keynesian economics and the economics of Keynes. Oxford: Oxford University Press.
Lin, J.-L., & Tsay, R. S. (1996). Co-integration constraint and forecasting: An empirical examination. Journal of Applied Econometrics, 11, 519–538.
Lothian, J. R., & Taylor, M. P. (1996). Real exchange rate behaviour: The recent float from the perspective of the past two centuries. Journal of Political Economy, 104, 488–509.
Lütkepohl, H. (2006). New introduction to multiple time series analysis. Berlin: Springer-Verlag.
Muellbauer, J. (1983). Surprises in the consumption function. Economic Journal, 93, Supplement March 1983, 34–50.
Paruolo, P. (1996). On the determination of integration indices in I(2) systems. Journal of Econometrics, 72, 313–356.
Patterson, K. (2000). An introduction to applied econometrics: A time series approach. Basingstoke: Palgrave-Macmillan.
Patterson, K. (2010). A primer for unit root testing. Basingstoke: Palgrave-Macmillan.
Patterson, K. (2011). Unit root tests in time series (Vol. 1). Basingstoke: Palgrave-Macmillan.
Robinson, P. M., & Marinucci, D. (1998). Semiparametric Frequency Domain Analysis of Fractional Cointegration. STICERD Discussion Paper EM/98/348, London School of Economics.
Robinson, P. M., & Yajima, Y. (2002). Determination of cointegrating rank in fractional systems. Journal of Econometrics, 106, 217–241.
Sargan, J. D. (1964). Wages and prices in the UK: A study in econometric methodology. In P. E. Hart, G. Mills, & J. K. Whitaker (Eds.), Econometric analysis for national economic planning. London: Butterworth.
Sims, C. (1980). Macroeconomics and reality. Econometrica, 48, 11–48.
Spliid, H. (1983). A fast estimation method for the vector auto-regressive moving average model with exogenous variables. Journal of the American Statistical Association, 78, 843–849.
Theil, H. (1965). The information approach to demand analysis. Econometrica, 33, 67–87.
Wallis, K. F., Andrews, M. J., Bell, D. N. F., Fisher, P. G., & Whitley, J. D. (1984). Models of the UK economy. Oxford: Oxford University Press.
Wickens, M. R. (1982). The efficient estimation of econometric models with rational expectations. Review of Economic Studies, 49, 55–67.
Wold, H., & Jureen, L. (1953). Demand Analysis. New York: Wiley.
Yoo, S. (1986). Multi-cointegrated Time Series and Generalised Error-Correction Models. University of San Diego Working Paper.
Yule, G. U. (1926). Why do we sometimes get non-sense correlation between time-series? A study of sampling and the nature of time-series. Journal of the Royal Statistical Society, 89, 1–64.
Yule, G. U. (1927). On a method of investigating periodicities in disturbed series with special reference to Wolfer’s sunspot numbers. Philisophical Transactions (A), 226, 267–298.
Author information
Authors and Affiliations
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Hunter, J., Burke, S.P., Canepa, A. (2017). Introduction. In: Multivariate Modelling of Non-Stationary Economic Time Series. Palgrave Texts in Econometrics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-31303-4_1
Download citation
DOI: https://doi.org/10.1057/978-1-137-31303-4_1
Published:
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-0-230-24330-9
Online ISBN: 978-1-137-31303-4
eBook Packages: Economics and FinanceEconomics and Finance (R0)