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economic time series
"... Abstract: The scalogram is the discrete wavelet transformation (DWT) analogue of the wellknown periodogram from the spectral analysis of time series. Just as the periodogram produces an ANOVA decomposition of the energy of a signal into different Fourier frequencies, the scalogram decomposes the en ..."
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the energy into “level components. ” In this paper we show how the DWT and the scalogram can be used to detect and separate periodic components in time series. The proposed method is then used to analyse a Spanish concrete production data set. Key words: Discrete wavelet transformations; economic time series
in economic time series
 Physica A
, 1997
"... The correlation function of a financial index of the New York stock exchange, the S&P 500, is analyzed at 1min intervals over the 13year period, Jan 84 – Dec 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two d ..."
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Cited by 12 (0 self)
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of considerable recent interest to both the economics and physics communities is whether there are correlations in economic time series and, if so, how to best quantify these correlations [1,2,3,4]. Here we study the S&P 500 index of the New York stock exchange over a 13year period (Fig. 1a). We calculate
Correlations in economic time series
, 1997
"... A financial index of the New York stock exchange, the S&P500, is analyzed at 1 min intervals over the 13yr period, January 84December 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with ..."
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interest to both the economics and physics communities is whether there are correlations in economic time series and, if so, how to best quantify these correlations [ 15]. Here we study the S&P500 index of the New York stock exchange over a 13yr period (Fig. la). We calculate the logarithmic
Variable trends in economic time series
 J. Econom. Perspectives
, 1988
"... T he two most striking historical features of aggregate output are its sustained long run growth and its recurrent fluctuations around this growth path. Real per capita GNP, consumption and investment in the United States during the postwar era are plotted in Figure 1. Both growth and deviations fro ..."
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Cited by 103 (2 self)
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from the growth trendoften referred to as "business cycles"are apparent in each series. Over horizons of a few years, these shorter cyclical swings can be pronounced; for example, the 1953, 1957 and 1974 recessions are evident as substantial temporary declines in aggregate activity
Aggregation and Unit Roots in Economic Time Series
, 1998
"... This paper analyses the effects of aggregation on testing the null hypothesis of unit root in time series. It shows that when an economic time series is derived through the aggregations of several individual time series, the usual DickeyFuller and PhillipsPerron tests can be misleading. Here an al ..."
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This paper analyses the effects of aggregation on testing the null hypothesis of unit root in time series. It shows that when an economic time series is derived through the aggregations of several individual time series, the usual DickeyFuller and PhillipsPerron tests can be misleading. Here
Modelling Highfrequency Economic Time Series
, 2000
"... The minutebyminute move of the Hang Seng Index (HSI) data over a fouryear period is analysed and shown to possess similar statistical features as those of other markets. Based on a mathematical theorem [S. B. Pope and E. S. C. Ching, Phys. Fluids A 5, 1529 (1993)], we derive an analytic form for ..."
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. The form of the Langevin equation can be determined directly from the market data. The availability of highfrequency economic time series, with a sampling rate of every few seconds, has generated a great deal of theoretical interest in the econometrics and the econophysics community[1–4]. Attempts have
Forecasting economic time series using targeted predictors
 Journal of Econometrics
, 2008
"... This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be nonlinear. Second, the factors used in the forecasting equation are estimated in a way t ..."
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Cited by 55 (1 self)
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to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to ‘targeted predictors ’ selected using hard and soft thresholding rules. Our three main findings can be summarized as follows. First, we find improvements at all
Designing a Neural Network for Forecasting Financial and Economic Time Series
, 1996
"... Artificial neural networks are universal and highly flexible function xpproximators first used in the fields of cognitive science and engineering. In recent years, neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramati ..."
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Cited by 76 (0 self)
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for forecasting economic time series data. An eightstep procedure to design a neural network forecasting model is explained including a discussion of tradeoffs in parameter selection, some common pitfalls, and points of disagreement among practitioners.
Nonlinearity, Structural Breaks Or Outliers In Economic Time Series?
 Nonlinear Econometric Modeling in Time Series Analysis
, 2000
"... This paper has its motivation from discussions at the EC ..."
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Cited by 22 (4 self)
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This paper has its motivation from discussions at the EC
Flexible Seasonal Long Memory and Economic Time Series
, 1995
"... We discuss specification, frequency domain estimation and application of a flexible seasonal long memory time series model based on fractional differencing. This type of model lends itself to seasonal unit root testing using standard distribution theory with null hypotheses of stationarity and nonst ..."
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Cited by 12 (1 self)
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We discuss specification, frequency domain estimation and application of a flexible seasonal long memory time series model based on fractional differencing. This type of model lends itself to seasonal unit root testing using standard distribution theory with null hypotheses of stationarity
Results 1  10
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