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Articles 1 - 13 of 13
Full-Text Articles in Social and Behavioral Sciences
Business Cycles, Trend Elimination, And The Hp Filter, Peter C.B. Phillips, Sainan Jin
Business Cycles, Trend Elimination, And The Hp Filter, Peter C.B. Phillips, Sainan Jin
Cowles Foundation Discussion Papers
We analyze trend elimination methods and business cycle estimation by data filtering of the type introduced by Whittaker (1923) and popularized in economics in a particular form by Hodrick and Prescott (1980/1997; HP). A limit theory is developed for the HP filter for various classes of stochastic trend, trend break, and trend stationary data. Properties of the filtered series are shown to depend closely on the choice of the smoothing parameter (λ). For instance, when λ = O ( n 4 ) where n is the sample size, and the HP filter is applied to an I(1) process, the filter …
Non-Linearity Induced Weak Instrumentation, Ioannis Kasparis, Peter C.B. Phillips, Tassos Magdalinos
Non-Linearity Induced Weak Instrumentation, Ioannis Kasparis, Peter C.B. Phillips, Tassos Magdalinos
Cowles Foundation Discussion Papers
In regressions involving integrable functions we examine the limit properties of IV estimators that utilise integrable transformations of lagged regressors as instruments. The regressors can be either I(0) or nearly integrated (NI) processes. We show that this kind of nonlinearity in the regression function can significantly affect the relevance of the instruments. In particular, such instruments become weak when the signal of the regressor is strong, as it is in the NI case. Instruments based on integrable functions of lagged NI regressors display long range dependence and so remain relevant even at long lags, continuing to contribute to variance reduction …
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C.B. Phillips
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C.B. Phillips
Cowles Foundation Discussion Papers
An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the finite sample distributions of nonlinear least squares estimators, resulting in potentially misleading inference. A new local limit theory is developed that approximates the finite sample distributions of the estimators uniformly well irrespective of the strength of the identification. An important technical …
Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C.B. Phillips
Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration. The requisite limit theory involves sample covariances of integrable transformations of non-stationary sequences and time translated sequences, allowing for the presence of a bandwidth parameter so as to accommodate kernel regression. The theory is an extension of Wang and Phillips (2008) and is useful for the analysis of nonparametric regression models with a misspecified lag structure and in situations where temporal aggregation issues arise. The limit properties of the Nadaraya-Watson (NW) estimator for cointegrating regression …
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 …
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 …
Local Limit Theory And Spurious Nonparametric Regression, Peter C.B. Phillips
Local Limit Theory And Spurious Nonparametric Regression, Peter C.B. Phillips
Cowles Foundation Discussion Papers
A local limit theorem is proved for sample covariances of nonstationary time series and integrable functions of such time series that involve a bandwidth sequence. The resulting theory enables an asymptotic development of nonparametric regression with integrated or fractionally integrated processes that includes the important practical case of spurious regressions. Some local regression diagnostics are suggested for forensic analysis of such regresssions, including a local R² and a local Durbin Watson (DW) ratio, and their asymptotic behavior is investigated. The most immediate findings extend the earlier work on linear spurious regression (Phillips, 1986), showing that the key behavioral characteristics of …
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. …
Nonlinear Regressions With Integrated Time Series, Joon Y. Park, Peter C.B. Phillips
Nonlinear Regressions With Integrated Time Series, Joon Y. Park, Peter C.B. Phillips
Cowles Foundation Discussion Papers
An asymptotic theory is developed for nonlinear regression with integrated processes. The models allow for nonlinear effects from unit root time series and therefore deal with the case of parametric nonlinear cointegration. The theory covers integrable, asymptotically homogeneous and explosive functions. Sufficient conditions for weak consistency are given and a limit distribution theory is provided. In general, the limit theory is mixed normal with mixing variates that depend on the sojourn time of the limiting Brownian motion of the integrated process. The rates of convergence depend on the properties of the nonlinear regression function, and are shown to be as …
Asymptotics For Nonlinear Transformations Of Integrated Time Series, Joon Y. Park, Peter C.B. Phillips
Asymptotics For Nonlinear Transformations Of Integrated Time Series, Joon Y. Park, Peter C.B. Phillips
Cowles Foundation Discussion Papers
An asymptotic theory for stochastic processes generated from nonlinear transformations of nonstationary integrated time series is developed. Various nonlinear functions of integrated series such as ARIMA time series are studied, and the asymptotic distributions of sample moments of such functions are obtained and analyzed. The transformations considered in the paper include a variety of functions that are used in practical nonlinear statistical analysis. It is shown that their asymptotic theory is quite different from that of integrated processes and stationary time series. When the transformation function is exponentially explosive, for instance, the convergence rate of sample functions is path-dependent. In …
Time Series Regression With A Unit Root And Infinite Variance Errors, Peter C.B. Phillips
Time Series Regression With A Unit Root And Infinite Variance Errors, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Chan and Tran give the limit theory for the least squares coefficient in a random walk with the iid errors that are in the domain of attraction of a stable law. This note discusses their results and provides generalizations to the case of I(q) processes with weakly dependent errors whose distributions are in the domain of attraction of a stable law. General unit root tests are also studied. It is shown that the semiparametric corrections suggested by the author for the finite variance case continue to work when the errors have infinite variance. The limit laws are expressed in terms …
Weak Convergence To The Matrix Stochastic Integral Bdb, Peter C.B. Phillips
Weak Convergence To The Matrix Stochastic Integral Bdb, Peter C.B. Phillips
Cowles Foundation Discussion Papers
The asymptotic theory of regression with integrated processes of the ARIMA type frequently involves weak convergence to stochastic integrals of the form ∫ 0 1 WdW , where W ( r ) is standard Brownian motion. In multiple regressions and vector autoregressions with vector ARIMA processes the theory involves weak convergence to matrix stochastic integrals of the form ∫ 0 1 BdB ’, where B ( r ) is vector Brownian motion with non scalar covariance matrix. This paper studies the weak convergence of sample covariance matrices to ∫ 0 1 BdB ’ under quite general conditions. The theory is …
Multiple Time Series Regression With Integrated Processes, Peter C.B. Phillips, Steven N. Durlauf
Multiple Time Series Regression With Integrated Processes, Peter C.B. Phillips, Steven N. Durlauf
Cowles Foundation Discussion Papers
This paper develops a general asymptotic theory of regression for processes which are integrated of order one. The theory includes vector autoregressions and multivariate regressions amongst integrated processes that are driven by innovation sequences which allow for a wide class of weak dependence and heterogeneity. The models studied cover cointegrated systems and quite general linear simultaneous equations systems with contemporaneous regressor-error correlation and serially correlated errors. Problems of statistical testing in vector autoregressions and multivariate regressions with integrated processes are also studied. It is shown that the asymptotic theory for conventional tests involves major departures from classical theory and raises …