These lectures describe some of the authors recent research
in areas that have potential for empirical applications. The lectures emphasize the
motivating ideas, provide a conceptual development of the econometric methods and discuss
and illustrate some practical implementations. Technical details will be covered at a
level that should be accessible to persons with a graduate economics background. The theme
of the lectures is econometric methodology for describing and analyzing nonstationary
economic data and the course has several distinct but related components.
The first part of the course discusses some new methods of econometric model
determination and shows how they can be used to construct vector time series models in an
automated way, to generate economic forecasts and to perform policy analysis. The
judgmental elements in using these methods for forecasting are minimal and are confined to
the choice of variables, the selection of the model classes to be used, and the setting of
certain maximal parameters like the maximal lag order in a vector autoregression. The
methods provide a simple procedure for finding consistent estimates of cointegrating rank
in vector autoregressions and can be used to determine jointly the cointegrating rank and
the lag order, thereby facilitating the use of reduced rank regression methods in practice
without the use of complicated testing procedures. Some experience that the author has had
in using the techniques in practical ex ante macroeconometric forecasting over the last
four years for a group of Asia-Pacific countries are reported.
The second component of the course discusses some new methods of description that can
be applied to both stationary and nonstationary data. These descriptive tools help to
characterize the properties of observed series and to assist in a novel way in making
agnostic assessments of such matters as level shifts and trend breaks. The methods also
provide a new way of measuring quantities of empirical interest like inflation hazards.
Some empirical illustrations to interest rates and inflation are provided. The third part
of the course addresses the longstanding issue of trends and spurious regressions from a
new perspective. It is argued that, in spite of serious statistical analysis in recent
years, the phenomenon is still imperfectly understood. While the commonality of trending
mechanisms in economic data is well known to make econometric regressions vulnerable to
spurious empirical relationships, such relationships will be shown to frequently have
their own mathematical justification. In consequence, some new interpretations of the
phenomena are put forward and the implications for empirical research are discussed.
The final component of the course is concerned with the econometric analysis of panel
data where the time series have nonstationary characteristics, as in most multi-country
macroeconomic panels. Some new methods are described that should be useful in various
empirical applications like testing growth convergence theories in macroeconomics,
estimating long-run relations between international financial series such as relative
prices and exchange rates, and testing hypotheses about international capital mobility.
Reference and Reading1
Econometric Model Determination: Automated Time Series Modeling and Forecasting
- Phillips, Peter C.B. (1995). "Bayesian Model Selection and Prediction with
Empirical Applications," Journal of Econometrics, 69, 289-332.
- Phillips, Peter C.B. (1995). "Automated forecasts of Asia-Pacific Economic
Activity," Asia Pacific Economic Review, 1, 92-102.
- Phillips, Peter C.B. (1996). "Econometric Model Determination," Econometrica,
- Phillips, P.C.B. (1998). "Impulse Response and Forecast Error Variance Asymptotics
in Nonstationary VAR's," Journal of Econometrics, 83, 21-56.
- Ploberger, W. and P.C.B. Phillips (1999). "Rissanen's Theorem and Econometric Time
Series" in Hugo A. Keuzenkamp, Michael McAleer and Arnold Zeller Simplicity,
Inference and Econometric Modelling, Cambridge: Cambridge University Press,
Spatial Analysis for Nonstationary Time Series Data: Densities and Hazard Functions
- Phillips, P.C.B. (1998). ""Econometric Analysis of Fisher's Equation,"
mimeographed, Yale University.
- Phillips, P.C.B. and J.Y. Park (1998). "Nonstationary Density Estimation and Kernel
Autoregression," mimeographed, Yale University.
- Park, J.Y. and P.C.B. Phillips (1999). "Asymptotics for Nonlinear Transformations
of Integrated Time Series," Econometric Theory (forthcoming).
Trends, Trend Elimination and Spurious Regressions
- Phillips, P.C.B. (1986). "Understanding Spurious Regressions in Econometrics,"
Journal of Econometrics, 33, 311-340.
- Durlauf, S.N. and P. C. P. Phillips (1988). "Trends versus Random Walks in Time
Series Analysis," Econometrica, 56, 1333-1354.
- Phillips, P.C.B. (1995). "Nonstationarity and Cointegration: Recent Books and
Themes for the Future," Journal of Applied Econometrics, 10, 87-94.
- Phillips, P.C.B. (1998). "New Tools for Understanding Spurious Regression," Econometrica
- Phillips, P.C.B. (1998). "New Unit Root Asymptotics in the Presence of
Deterministic Trends," mimeographed, Yale University.
Nonstationary Panel Data and Cointegration
- Phillips, P.C.B. and H.R. Moon (1998) "Nonstationary Panel Data Analysis: An
Overview of Some Recent Developments" mimeographed, Yale University.
- Phillips, P.C.B. and H.R. Moon (1999) "Linear Regression Limit Theory for
Nonstationary Panel Data", Econometrica (forthcoming)
- Moon, H.R. and P.C.B. Phillips (1998) "A Reinterpretation of the Feldstein-Horioka
Regressions from a Nonstationary Panel Viewpoint," mimeographed, Yale University.
1 Copies of forthcoming/unpublished papers can be downloaded from the author's web