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Full-Text Articles in Social and Behavioral Sciences
Unit Roots, Peter C.B. Phillips
Unit Roots, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Nonstationarity is certainly one of the most dominant and enduring characteristics of macroeconomic and financial time series. It therefore seems appropriate that this feature of the data be seriously addressed both in econometric methodology and in empirical practice. However, until recently this has not been the case. Before 1980, it was standard empirical practice in econometrics to treat observed trends as simple deterministic functions of time. Nelson-Plosser (1982) challenged this practice and showed that observed trends are better modeled if one allows for stochastic trends. Since their work there has been a continuing reappraisal of trend behavior in economic methods …
Comment On ‘To Criticize The Critics’, By Peter C.B. Phillips, Christopher A. Sims
Comment On ‘To Criticize The Critics’, By Peter C.B. Phillips, Christopher A. Sims
Cowles Foundation Discussion Papers
In his paper “To Criticize the Critics” (1991), Peter Phillips discusses Bayesian methodology for time series models. The main point that Uhlig and I set out to make, however, was that careful consideration of the implications of the likelihood principle suggests that much of the recent work under the “unit root” label in the econometrics literature is being incorrectly interpreted in practice. We pointed out that time series models with possible unit roots are one of the few domains within which the implications of a likelihood principle approach to inference are difference, even in the large samples, from those of …
Bayesian Routes And Unit Roots: De Rebus Prioribus Semper Est Disputandum, Peter C.B. Phillips
Bayesian Routes And Unit Roots: De Rebus Prioribus Semper Est Disputandum, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper provides detailed responses to the following 8 discussants of my paper “To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends”: Gary Koop and Mark Steel; Edward Leamer; In-Moo Kim and G. S. Maddala Dale J. Poirier; Peter C. Schotman and Herman K. van Dijk; James H. Stock; David Dejong and Charles H. Whiteman; and Christopher Sims. This reply puts new emphasis on the call made in the earlier paper for objective Bayesian analysis in time series; it underlines the need for a new approach, especially with regard to posterior odds testing; and it draws attention to …
Testing The Null Hypothesis Of Stationarity Against The Alternative Of A Unit Root: How Sure Are We That Economic Time Series Have A Unit Root?, Denis Kwiatkowski, Peter C.B. Phillips, Peter Schmidt
Testing The Null Hypothesis Of Stationarity Against The Alternative Of A Unit Root: How Sure Are We That Economic Time Series Have A Unit Root?, Denis Kwiatkowski, Peter C.B. Phillips, Peter Schmidt
Cowles Foundation Discussion Papers
The standard conclusion that is drawn from this empirical evidence is that many or most aggregate economic time series contain a unit root. However, it is important to note that in this empirical work the unit root is set up as the null hypothesis testing is carried out ensures that the null hypothesis is accepted unless there is strong evidence against it. Therefore, an alternative explanation for the common failure to reject a unit root is simply that most economic time series are not very informative about whether or not there is a unit root; or, equivalently, that standard unit …
Time Series Modelling With A Bayesian Frame Of Reference: I. Concepts And Illustrations, Peter C.B. Phillips, Werner Ploberger
Time Series Modelling With A Bayesian Frame Of Reference: I. Concepts And Illustrations, Peter C.B. Phillips, Werner Ploberger
Cowles Foundation Discussion Papers
This paper offers a general approach to time series modeling that attempts to reconcile classical and methods. The central idea put forward to achieve reconciliation is that the Bayesian approach relies implicitly a frame of reference for the data generating mechanism that is quite different from the one that is employed in the classical approach. Differences in inferences from the two approaches are therefore to be expected unless the altered frame reference is taken into account. We show that the new frame of reference in Bayesian inference is a consequence of a change of measure that arises naturally in the …