Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time series modeling and seasonal adjustment, as is reflected both in the contents of the chapters and in their authorship, with contributors coming from academia and government statistical agencies. For easier perusal and absorption, the contents have been grouped into seven topical sections: Section I deals with periodic modeling of time series, introducing, applying, and comparing various seasonally periodic models Section II examines the estimation of time series components when models for series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation Section III examines the quantification of error in X-11 seasonal adjustments, with comparisons to error in model-based seasonal adjustments Section IV discusses some practical problems that arise in seasonal adjustment: developing asymmetric trend-cycle filters, dealing with both temporal and contemporaneous benchmark constraints, detecting trading-day effects in monthly and quarterly time series, and using diagnostics in conjunction with model-based seasonal adjustment Section V explores outlier detection and the modeling of time series containing extreme values, developing new procedures and extending previous work Section VI examines some alternative models and inference procedures for analysis of seasonal economic time series Section VII deals with aspects of modeling, estimation, and forecasting for nonseasonal economic time series By presenting new methodological developments as well as pertinent empirical analyses and reviews of established methods, the book provides much that is stimulating and practically useful for the serious researcher and analyst of economic time series.
Some recent suggestions forestimation in the frequency domain (Hannan, 1969; Hannan and Nicholls, 1972; Nicholls, 1973; Nicholls et al., 1975, pp. 123–125; and Pukkila, 1977), however, lead to much simpler procedures for estimation of ...
The results of this section can be generalized, as discussed by Engel [1984], where conditions are provided to assure that the sum of two dependent Gaussian ARMA processes is ARMA. Engel further shows that if X, is Gaussian ARMA with ...
Introduction to the theory of time series; Spectral analysis; Building linear series models; The theory of forecasting; Practical methods for univariate time series forecasting; Forecasting from regression models; Multiple modeling...
Davies , N. and Newbold , P. ( 1979 ) , ' Some Power Studies of a Portmanteau Test of Time Series Model Specification ' , Biometrika , 66 , 153-5 . Davies , N. , Triggs , C. M. and Newbold , P. ( 1977 ) , ' Significance Levels of the ...
Durland , J. M. and McCurdy , T. H. ( 1994 ) . Duration dependent transitions in a Markov model of US GNP growth . Journal of Business and Economic Statistics , 12 , 279–288 . Emerson , R. A. ( 1994 ) . Two essays on Investment Trusts ...
... A.W. , 168 Griliches , Z. , 140 , 223 Chipman , J.S. , 193 Chow , G.C. , 352 , 356 , 357 Collier , P. , 158 , 189 Cox ... 138 , 339 Hendry , D.F. , 189 , 216 , 283 , 286 , 287 , 291 , 292 , 294 , 300 , 311 , 330 , 339 , 357 Hinkley ...
Comprising a selection of expository and research papers, Harmonic Analysis and Integral Geometry grew from presentations offered at the July 1998 Summer University of Safi, Morocco-an annual, advanced research school...
In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years.
For many time series data , we can say that the observations at any time t and t - 1 , that is , y , and y , -1 , are ... -1 , and may be with y , - ; for some i , with i = 2,3,4 , ... , we can exploit this correlation for forecasting .
The authors believe this is the first published study to really deal with this issue of context.