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Full-Text Articles in Social and Behavioral Sciences
Estimation And Inference With Near Unit Roots, Peter C. B. Phillips
Estimation And Inference With Near Unit Roots, Peter C. B. Phillips
Cowles Foundation Discussion Papers
New methods are developed for identifying, estimating and performing inference with nonstationary time series that have autoregressive roots near unity. The approach subsumes unit root (UR), local unit root (LUR), mildly integrated (MI) and mildly explosive (ME) specifications in the new model formulation. It is shown how a new parameterization involving a localizing rate sequence that characterizes departures from unity can be consistently estimated in all cases. Simple pivotal limit distributions that enable valid inference about the form and degree of nonstationarity apply for MI and ME specifications and new limit theory holds in UR and LUR cases. Normalizing and …
Econometric Measurement Of Earth's Transient Climate Sensitivity, Peter C.B. Phillips, Thomas Leirvik, Trude Storelvmo
Econometric Measurement Of Earth's Transient Climate Sensitivity, Peter C.B. Phillips, Thomas Leirvik, Trude Storelvmo
Cowles Foundation Discussion Papers
How sensitive is Earth’s climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing and fundamental question in climate science was recently analyzed by dynamic panel data methods using extensive spatiotemporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). These methods revealed that atmospheric aerosol effects masked approximately one-third of the continental warming due to increasing GHG concentrations over this period, thereby implying greater climate sensitivity to GHGs than previously thought. The present study provides asymptotic theory justifying the use of these methods when …
A Conditional-Heteroskedasticity-Robust Confidence Interval For The Autoregressive Parameter, Donald W.K. Andrews, Patrik Guggenberger
A Conditional-Heteroskedasticity-Robust Confidence Interval For The Autoregressive Parameter, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva’s (2007a) modification of Stock’s (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well …
A Conditional-Heteroskedasticity-Robust Confidence Interval For The Autoregressive Parameter, Donald W.K. Andrews, Patrik Guggenberger
A Conditional-Heteroskedasticity-Robust Confidence Interval For The Autoregressive Parameter, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva’s (2007a) modification of Stock’s (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well …
Asymptotic Theory For Zero Energy Density Estimation With Nonparametric Regression Applications, Qiying Wang, Peter C.B. Phillips
Asymptotic Theory For Zero Energy Density Estimation With Nonparametric Regression Applications, Qiying Wang, Peter C.B. Phillips
Cowles Foundation Discussion Papers
A local limit theorem is given for the sample mean of a zero energy function of a nonstationary time series involving twin numerical sequences that pass to infinity. The result is applicable in certain nonparametric kernel density estimation and regression problems where the relevant quantities are functions of both sample size and bandwidth. An interesting outcome of the theory in nonparametric regression is that the linear term is eliminated from the asymptotic bias. In consequence and in contrast to the stationary case, the Nadaraya-Watson estimator has the same limit distribution (to the second order including bias) as the local linear …
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C.B. Phillips
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C.B. Phillips
Cowles Foundation Discussion Papers
We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. The concept of stationarity is refined, allowing for sample size dependence in the array and paying special attention to the rate at which the boundary unit root case is approached using a localizing coefficient around unity. The primary focus of the present paper is on estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation …
Bootstrapping I(1) Data, Peter C.B. Phillips
Bootstrapping I(1) Data, Peter C.B. Phillips
Cowles Foundation Discussion Papers
A functional law for an I(1) sample data version of the continuous-path block bootstrap of Paparoditis and Politis (2001) is given. The results provide an alternative demonstration that continuous-path block bootstrap unit root tests are consistent under the null.
Unit Root Model Selection, Peter C.B. Phillips
Unit Root Model Selection, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Some limit properties for information based model selection criteria are given in the context of unit root evaluation and various assumptions about initial conditions. Allowing for a nonparametric short memory component, standard information criteria are shown to be weakly consistent for a unit root provided the penalty coefficient C n → ∞ and C n /n → 0 as n → ∞. Strong consistency holds when C n /(log log n ) 3 → ∞ under conventional assumptions on initial conditions and under a slightly stronger condition when initial conditions are infinitely distant in the unit root model. The limit …
Structural Nonparametric Cointegrating Regression, Qiying Wang, Peter C.B. Phillips
Structural Nonparametric Cointegrating Regression, Qiying Wang, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill-posed inverse problems. In functional cointegrating regressions where the regressor is an integrated time series, it is shown here that inverse and ill-posed inverse problems do not arise. Remarkably, nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution …
Asymptotics For Stationary Very Nearly Unit Root Processes, Donald W.K. Andrews, Patrik Guggenberger
Asymptotics For Stationary Very Nearly Unit Root Processes, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
This paper considers a mean zero stationary first-order autoregressive (AR) model. It is shown that the least squares estimator and t statistic have Cauchy and standard normal asymptotic distributions, respectively, when the AR parameter ρ n is very near to one in the sense that 1 – ρ n = ( n –1 ).
Asymptotic Theory For Local Time Density Estimation And Nonparametric Cointegrating Regression, Qiying Wang, Peter C.B. Phillips
Asymptotic Theory For Local Time Density Estimation And Nonparametric Cointegrating Regression, Qiying Wang, Peter C.B. Phillips
Cowles Foundation Discussion Papers
We provide a new asymptotic theory for local time density estimation for a general class of functionals of integrated time series. This result provides a convenient basis for developing an asymptotic theory for nonparametric cointegrating regression and autoregression. Our treatment directly involves the density function of the processes under consideration and avoids Fourier integral representations and Markov process theory which have been used in earlier research on this type of problem. The approach provides results of wide applicability to important practical cases and involves rather simple derivations that should make the limit theory more accessible and useable in econometric applications. …
Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root, Peter C.B. Phillips, Chirok Han
Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root, Peter C.B. Phillips, Chirok Han
Cowles Foundation Discussion Papers
This note introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite sample bias, are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform / n rate of convergence. En route, a useful CLT for sample covariances of linear processes is given, following Phillips and Solo (1992). The approach also has useful extensions to dynamic panels.
Regression Asymptotics Using Martingale Convergence Methods, Rustam Ibragimov, Peter C.B. Phillips
Regression Asymptotics Using Martingale Convergence Methods, Rustam Ibragimov, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Weak convergence of partial sums and multilinear forms in independent random variables and linear processes to stochastic integrals now plays a major role in nonstationary time series and has been central to the development of unit root econometrics. The present paper develops a new and conceptually simple method for obtaining such forms of convergence. The method relies on the fact that the econometric quantities of interest involve discrete time martingales or semimartingales and shows how in the limit these quantities become continuous martingales and semimartingales. The limit theory itself uses very general convergence results for semimartingales that were obtained in …
The Kpss Test With Seasonal Dummies, Sainan Jin, Peter C.B. Phillips
The Kpss Test With Seasonal Dummies, Sainan Jin, Peter C.B. Phillips
Cowles Foundation Discussion Papers
It is shown that the KPSS test for stationarity may be applied without change to regressions with seasonal dummies. In particular, the limit distribution of the KPSS statistic is the same under both the null and alternative hypotheses whether or not seasonal dummies are used.
Partially Linear Models With Unit Roots, Ted Juhl, Zhijie Xiao
Partially Linear Models With Unit Roots, Ted Juhl, Zhijie Xiao
Cowles Foundation Discussion Papers
This paper studies the asymptotic properties of a nonstationary partially linear regression model. In particular, we allow for covariates to enter the unit root (or near unit root) model in a nonparametric fashion, so that our model is an extension of the semiparametric model analyzed in Robinson (1988). It is proven that the autoregressive parameter can be estimated at rate N even though part of the model is estimated nonparametrically. Unit root tests based on the semiparametric estimate of the autoregressive parameter have a limiting distribution which is a mixture of a standard normal and the Dickey-Fuller distribution. A Monte …
Bootstrapping Spurious Regression, Peter C.B. Phillips
Bootstrapping Spurious Regression, Peter C.B. Phillips
Cowles Foundation Discussion Papers
The bootstrap is shown to be inconsistent in spurious regression. The failure of the bootstrap is spectacular in that the bootstrap effectively turns a spurious regression into a cointegrating regression. In particular, the serial correlation coefficient of the residuals in the bootstrap regression does not converge to unity, so the bootstrap is not even first order consistent. The block bootstrap serial correlation coefficient does converge to unity and is therefore first order consistent, but has a slower rate of convergence and a different limit distribution from that of the sample data serial correlation coefficient. The analysis covers spurious regressions involving …
Nonlinear Instrumental Variable Estimation Of Autoregression, Peter C.B. Phillips, Joon Y. Park, Yoosoon Chang
Nonlinear Instrumental Variable Estimation Of Autoregression, Peter C.B. Phillips, Joon Y. Park, Yoosoon Chang
Cowles Foundation Discussion Papers
Instrumental variable (IV) estimation methods that allow for certain nonlinear functions of the data as instruments are studied. The context of the discussion is the simple unit root model where certain advantages to the use of nonlinear instruments are revealed. In particular, certain classes of IV estimators and associated t -tests are shown to have simpler (standard) limit theory in contrast to the least squares estimator, providing an opportunity for the study of optimal estimation in certain IV classes and furnishing tests and confidence intervals that allow for unit root and stationary alternatives. The Cauchy estimator studied in recent work …
Local Whittle Estimation In Nonstationary And Unit Root Cases, Katsumi Shimotsu, Peter C.B. Phillips
Local Whittle Estimation In Nonstationary And Unit Root Cases, Katsumi Shimotsu, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Asymptotic properties of the local Whittle estimator in the nonstationary case (d > 1/2) are explored. For 1/2 < d < 1, the estimator is shown to be consistent, and its limit distribution and the rate of convergence depend on the value of d . For d = 1, the limit distribution is mixed normal. For d > 1 and when the process has a linear trend, the estimator is shown to be inconsistent and to converge in probability to unity.
Unit Root Log Periodogram Regression, Peter C.B. Phillips
Unit Root Log Periodogram Regression, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Log periodogram (LP) regression is shown to be consistent and to have a mixed normal limit distribution when the memory parameter d = 1. Gaussian errors are not required. Tests of d = 1 based on LP regression are consistent against d < 1 alternatives but inconsistent against d > 1 alternatives. A test based on a modified LP regression that is consistent in both directions is provided.
Automated Forecasts Of Asia-Pacific Economic Activity, Peter C.B. Phillips
Automated Forecasts Of Asia-Pacific Economic Activity, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper reports quarterly ex ante forecasts of macroeconomic activity for the U.S.A., Japan and Australia for the period 1995-1997. The forecasts are based on automated time series models of vector autoregressions (VAR’s), reduced rank regressions (RRR’s), error correction models (ECM’s) and Bayesian vector autoregressions (BVAR’s). The models are automated by using an asymptotic predictive form of the model selection criterion PIC to determine autoregressive lag order, cointegrating rank and trend degree in the VAR’s, RRR’s, and ECM’s. The same criterion is used to find optimal values of the hyperparameters in the BVAR’s. The forecasts are graphed and tabulated. In …
A Bayesian Analysis Of Trend Determination In Economic Time Series, Eric Zivot, Peter C.B. Phillips
A Bayesian Analysis Of Trend Determination In Economic Time Series, Eric Zivot, Peter C.B. Phillips
Cowles Foundation Discussion Papers
In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of …
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 …
Testing For A Unit Root In The Presence Of Deterministic Trends, Peter C.B. Phillips, Peter Schmidt
Testing For A Unit Root In The Presence Of Deterministic Trends, Peter C.B. Phillips, Peter Schmidt
Cowles Foundation Discussion Papers
This paper provides a new unit root test based on an alternative parameterization which has previously been considered by Bhargava (1986). This parameterization allows for trend under both the null and the alternative, without introducing any parameters that are irrelevant under either. This is not so in the Dickey-Fuller parameterizations. The new test is extracted from the score or LM principle under the assumption that the errors are iid N(0, sigma squared (epsilon)), but our asymptotics hold under more general assumptions about the errors. Two forms of the test (a coefficient test and at t-test) are derived.
Testing For A Unit Root By Generalized Least Squares Methods In The Time And Frequency Domains, In Choi, Peter C.B. Phillips
Testing For A Unit Root By Generalized Least Squares Methods In The Time And Frequency Domains, In Choi, Peter C.B. Phillips
Cowles Foundation Discussion Papers
New time and frequency domain tests for the presence of a unit root are developed. The tests are based on generalized least squares (GLS) methods in both the time and the frequency domains. For the time domain tests, moving average processes are assumed for the error terms on the autoregression. For the frequency domain tests, general assumptions are made which allow for stationary and weakly dependent error processes. The limiting distributions of feasible GLS tests are derived under MA(1) errors in the time domain. This theory is extended to higher order moving average processes under an invertibility condition. The limiting …
Towards A Unified Asymptotic Theory For Autoregression, Peter C.B. Phillips
Towards A Unified Asymptotic Theory For Autoregression, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper develops an asymptotic theory for a first order autoregression with a root near unity. Deviations from the unit root theory are measured through a noncentrality parameter. When this parameter is negative we have a local alternative that is stationary; when it is positive, the local alternative is explosive; and when it is zero we have the standard unit root theory. Our asymptotic theory accommodates these alternatives and helps to unify earlier theory in which the unit root case appears as a singularity of the asymptotics. The general theory is expressed in terms of functionals of a simple diffusion …