Abstract
‘Modern’ theories of the Phillips curve inadvertently imply that inflation is an integrated or near-integrated process, but this implication is strongly rejected using US data. Alternatively, if we assume that inflation is a stationary process around a shifting mean (due to changes in monetary policy), then any estimate of long-run relationships in the data will suffer from a ‘small-sample’ problem as there are too few stationary inflation ‘regimes’. Using the extensive literature on identification of structural breaks, we identify inflation regimes which are used in turn to estimate with panel data techniques the US long-run Phillips curve.
Similar content being viewed by others
Notes
The robustness of the outcomes from a single technique may be subjected to a small and limited number of changes to the assumptions of the estimating technique.
The type of stationarity being discussed here is covariance stationarity once shifts in means have been accounted for. See Russell (2014) who finds that once the shifts in mean are accounted for in US inflation the data are homoscedastic.
If the data are integrated, then the analysis proceeds within a cointegration framework. Within this framework, Banerjee et al. (2001) and Banerjee and Russell (2001, 2005) argue that while the ‘true’ statistical process for inflation is most likely stationary around a frequently shifting mean they proceed under the maintained assumption that this process can be approximated by an integrated process.
We thank a referee for drawing our attention to the break-point estimation techniques of Frick et al. (2014), Fryzlewicz (2014) and James and Matteson (2015) from the statistics literature. These techniques generate similar break dates to those reported in this paper below. Further information and results from these techniques can be found at www.billrussell.info.
An alternative approach to the one employed here is the unobserved components model of Stock and Watson (2007) that assumes the change in the mean rate of inflation evolves smoothly rather than abruptly across inflation regimes. However, the focus of this paper is to estimate the long-run relationship between the means of variables across inflation regimes rather than the transition between the means in successive inflation regimes.
For a recent survey on the wide range of theoretical and empirical approaches to modelling inflation, see Mavroeidis and Stock, (2014).
The SPC Phillips curve is ‘postmodern’ in the sense that it eschews the empirically invalid assumptions of full information and no missing markets as well as the logical implications of models incorporating identical representative agents. Instead, the knowledge set of agents contains elements that they can be reasonably expected to know and agents behave in ways consistent with the knowledge that agents are not identical.
This argument is considered in more detail in Russell (2015).
See Perron (1989).
Changes in monetary policy is used in the sense that the monetary policy instrument has either changed, or not changed, in such a way so that the mean rate of inflation does not remain constant.
Inflation is measured as the quarterly change in the natural logarithm of the seasonally adjusted US gross domestic product implicit price deflator at factor cost. See “Data Appendix” for further details concerning the data.
If the Volker deflation is a downward shift in the mean rate of inflation and the mean rates of inflation in the early 1960s and after 1990 are similar, then there must have been a similar upward shift in the mean either prior to or after the ‘Volker’ break in the early 1980s. Consequently, the ‘Volker’ break cannot exist on its own and there must be a minimum of two breaks in the data.
If the long-run Phillips curve is not vertical and has a slope, then the curve must be nonlinear. If this is not the case, then as inflation increases to an infinite rate the forcing variable will exceed its conceptual boundaries.
Personal correspondence between Bill Russell and Pierre Perron between March and June 2015 considers the issue of minimum trim size and the number of breaks when applying the Bai–Perron technique to inflation data. In this correspondence, Pierre Perron indicates that in his simulation work (based on 1 or 2 breaks in the data) he finds that BIC outperforms the other information criteria if persistence is low. He also acknowledges the importance of a practical approach to modelling breaks based on an understanding of the data when the number of breaks in the data may be large.
The statistics literature refers to change-points and segmentation in the same manner as the econometrics literature refers to breaks in time series data.
See also Killick et al. (2010).
For sake of space, the estimation of the short-run Phillips curves assuming inflation is a stationary process around a shifting mean is not pursued here and readers are directed to Russell (2011).
This proof by contradiction is another component of ‘postmodern’ theories of inflation in that agents cannot logically predict future breaks in mean inflation. Therefore, this information must logically be ‘missing’ and all optimisation behaviour of agents based on agents holding unbiased predictions of future relative prices is logically moot. See Russell and Chowdhury (2013).
References
Algama M, Keith JM (2014) Investigating genomic structure using changept: a Bayesian segmentation model. Comput Struct Biotechnol J 10:107–15
Almon S (1965) The distributed lag between capital appropriations and net expenditures. Econometrica 33:178–196
Andrews D (1993) Tests for parameter instability and structural change with unknown change point. Econometrica 61:821–856
Andrews D (2003) Tests for parameter instability and structural change with unknown change point: a corrigendum. Econometrica 71:395–397
Bai J (1994) Least squares estimation of a shift in linear processes. J Time Ser Anal 15:453–72
Bai J (1997) Estimation of a change point in multiple regression models. Rev Econ Stat 79:551–63
Bai J, Perron P (1998) Estimating and testing linear models with multiple structural changes. Econometrica 66:47–78
Bai J, Perron P (2003) Computation and analysis of multiple structural change models. J Appl Econ 18:1–22
Banerjee A, Lumsdaine R, Stock J (1992) Recursive and sequential tests of the unit-root and trend-break hypotheses: theory and international evidence. J Bus Econ Stat 10:271–87
Banerjee A, Cockerell L, Russell B (2001) An I(2) analysis of inflation and the markup. J Appl Econ, Sargan Special Issue 16:221–240
Banerjee A, Russell B (2001) The relationship between the markup and inflation in the G7 economies and Australia. Rev Econ Stat 83(2):377–87
Banerjee A, Russell B (2005) Inflation and measures of the markup. J Macroecon 27:289–306
Braun JV, Muller H-G (1998) Statistical methods for DNA sequence segmentation. Stat Sci 13:142–62
Cagan P (1956) The monetary dynamics of hyperinflation. In: Friedman M (ed) Studies in the quantity theory of money. Chicago University Press, Chicago, p 25.117
Chen Y, Russell B (2002) An optimising model of price adjustment with missing information. European University Institute Working Papers, Eco No. 2002/3
Chow GC (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28:591–605
Clarida R, Galí J, Gertler M (1999) The science of monetary policy: a new keynesian perspective. J Econ Lit 37:1661–1707
Enders W, Lee J (2012a) A unit root test using a Fourier series to approximate smooth breaks. Oxf Bull Econ Stat 74(4):574–99
Enders W, Lee J (2012b) The flexible Fourier form and Dickey–Fuller type unit root tests. Econ Lett 117(1):196–9
Friedman M (1968) The role of monetary policy. Am Econ Rev 58(1):1–17
Frick K, Munk A, Sieling H (2014) Multiscale change point inference. J R Stat Soc Ser B (Stat Methodol) 76:495–580
Fryzlewicz P (2014) Wild binary segmentation for multiple change-point detection. Ann Stat 42:2243–2281
Fryzlewicz P, Sapatinas T, Subba Rao S (2006) A Haar–Fisz technique for locally stationary volatility estimation. Biometrika 93:687–704
Galí J, Gertler M (1999) Inflation dynamics: a structural econometric analysis. J Monet Econ 44:195–222
Galí J, Gertler M, Lopez-Salido JD (2001) European inflation dynamics. Eur Econ Rev 45:1237–1270
Gardner LA (1969) On detecting changes in the mean of normal variates. Ann Math Stat 40:116–26
Giraitis L, Kokoszka P, Leipus R (2003) Rescaled variance and related tests for long memory in volatility and levels. J Econ 112:265–94
Griliches Z (1967) Distributed lags: a survey. Econometrica 35(1):16–49
Harris D, McCabe B, Leybourne S (2008) Testing for long memory. Econ Theory 24:143–75
Hawkins DM (2001) Fitting multiple change-point models to data. Comput Stat Data Anal 37:323–41
Hendry D, Johansen S, Santos C (2008) Automatic selection of indicators in a fully saturated regression. Comput Stat 23:317–39
Jackson B, Sargle JD, Barnes D, Arabhi S, Alt A, Gioumousis P, Gwin E, Sangtrakulcharoen P, Tan L, Tsai TT (2005) An algorithm for optimal partitioning of data on an interval. IEEE Signal Process Lett 12:105–8
James B, James KL, Siegmund D (1987) Tests for a change-point. Biometrika 74:71–84
James NA, Matteson DS (2015) ecp: An R package for nonparametric multiple change point analysis of multivariate data. J Stat Softw 62:1–25
Killick R, Eckleya IA, Ewans K, Jonathan P (2010) Detection of changes in variance of oceanographic time-series using change point analysis. Ocean Eng 37:1120–6
Killick R, Fearnhead P, Eckleya IA (2012) Optimal detection of changepoints with a linear computational cost. J Am Stat Assoc 107:1590–8
Koyck LM (1954) Distributed lags and investment analysis. North-Holland Publishing Co., Amsterdam
Lee J, Strazicich MC (2003) Minimum lagrange multiplier unit root test with two structural breaks. Rev Econ Stat 85(4):1082–9
Lucas RE Jr, Rapping LA (1969) Price expectations and the Phillips curve. Am Econ Rev 59(3):342–350
Lumsdaine RL, Papell DH (1997) Multiple trend breaks and the unit root hypothesis. Rev Econ Stat 79:212–8
MacNeill IB (1978) Properties of sequences of partial sums of polynomial regression residuals with applications to tests for change of regression at unknown times. Ann Stat 6:422–33
Mavroeidis S, Plagborg-Moller M, Stock JH (2014) Empirical evidence on inflation expectations in the new Keynesian Phillips curve. J Econ Lit 52:124–88
Nerlove M (1956) Estimates of the elasticities of supply of selected agricultural commodities. J Farm Econ 38(2):496–509
Page ES (1955) A test for a change in a parameter occurring at an unknown point. Biometrika 42:523–27
Page ES (1957) On problems in which a change in a parameter occurs at an unknown point. Biometrika 44:248–52
Perron P (1989) The great crash, the oil price shock, and the unit root hypothesis. Econometrica 57(6):1361–1401
Perron P (1990) Testing for a unit root in a time series with a changing mean. J Bus Econ Stat 8:153–62
Perron P (2006) Dealing with structural breaks. In: Patterson K, Mills TC (eds) Palgrave handbook of econometrics: econometric theory, vol 1. Palgrave Macmillan, Basingstoke, pp 278–352
Phelps ES (1967) Phillips curves, expectations of inflation, and optimal unemployment over time. Economica 34(3):254–81
Priyadarshana WJRM, Sofronov G (2015) Multiple break-point detection in array CGH data via the cross-entropy method. Trans Comput Biol Bioinform 12:487–98
Quant RE (1958) The estimation of the parameters of a linear regression system obeying two separate regimes. J Stat Assoc 53:873–80
Quant RE (1960) Tests of the hypothesis that a linear regression system obeys two separate regimes. J Stat Assoc 55:324–30
Rappoport P, Reichlin L (1989) Segmented trends and non-stationary time series. Econ J 99:168–77
Robinson P, Labato I (1998) A nonparametric test for I(0). Rev Econ Stud 65(3):475–495
Russell B (1998) A rules based model of disequilibrium price adjustment with missing information, Dundee discussion papers, Department of Economic Studies, University of Dundee, November, No. 91
Russell B (2011) Non-stationary inflation and panel estimates of United States short and long-run Phillips curves. J Macroecon 33:406–19
Russell B (2014) ARCH and structural breaks in United States inflation. Appl Econ Lett 21(14):973–978
Russell B (2015) Modern’ Phillips curves and the implications for the statistical process of inflation, Dundee discussion papers, economic studies, University of Dundee, September, No. 289, forthcoming in Applied Economic Letters
Russell B, Chowdhury RA (2013) Estimating United States Phillips curves with expectations consistent with the statistical process of inflation. J Macroecon 35:24–38
Russell B, Evans J, Preston B (2002) The impact of inflation and uncertainty on the optimum markup set by firms. European University Institute Working Papers, Eco No. 2002/2
Scott AJ, Knott M (1974) A cluster analysis method for grouping means in the analysis of variance. Biometrics 30:507–12
Sen A, Srivastava MS (1975) On tests for detecting change in mean. Ann Stat 3:98–108
Starica C, Granger C (2005) Nonstationarities in stock returns. Rev Econ Stat 87:503–22
Stock JH, Watson MW (2007) Why has US inflation become harder to forecast? J Money Credit banking 39(s1):3–33
Sullivan JH (2002) Estimating the locations of multiple change points in the mean. Comput Stat 17:289–96
Svennson LEO (2000) Open economy inflation targeting. J Int Econ 50:155–83
Author information
Authors and Affiliations
Corresponding author
Additional information
We thank Tom Doan for generously making available the Bai–Perron programmes on the Estima website. All data are available at http://billrussell.info.
Appendix 1: Data appendix
Appendix 1: Data appendix
The US data are seasonally adjusted and quarterly for the period March 1960 to June 2015. The US national accounts data are from the National Income and Product Account (NIPA) tables from the USA, Bureau of Economic Analysis (BEA) and downloaded on 2 and 3 September 2015 except for Table 1.1.6 which was downloaded on 21 November. The database is available at www.BillRussell.info.
US data | |
---|---|
Variable | Details |
The price level | Defined as the gross domestic product implicit price deflator at factor cost (ipdfc) calculated from NIPA Table 1.10 as gross domestic income (line 1) less taxes on production and imports (line 7) plus subsidies (line 8) divided by real gross domestic product at constant prices (Billions of Chained 2009 Dollars) (NIPA Table 1.1.6 line 1). The price level is the natural logarithm of ipdfc (Database: lipdfc) |
Inflation | Defined as the log change in the price level (Database dlipfc) |
The markup (National Accounts Basis) | Defined as gross domestic income at factor cost divided by total compensation paid to employees (Database: mufc). Calculated from NIPA Table 1.10 as gross domestic income (line 1) less taxes on production and imports (line 7) plus subsidies (line 8) divided by compensations of employees paid (line 2). The markup is the natural logarithm of the markup (mufc) (Database: lmufc) |
Rights and permissions
About this article
Cite this article
Russell, B., Rambaccussing, D. Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve. Empir Econ 56, 1455–1475 (2019). https://doi.org/10.1007/s00181-017-1404-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00181-017-1404-5