In: Palgrave texts in econometrics
Intro -- Contents -- List of Figures -- 1 Introduction -- 1.1 Historical Perspective -- 1.2 Overview of the Book -- References -- 2 'Classical' Techniques of Modelling Trends and Cycles -- 2.1 The Classical Trend-Cycle Decomposition -- 2.2 Deterministic Trend Models -- 2.2.1 Linear Trends -- 2.2.2 Nonlinear Trends -- 2.2.3 Breaking and Segmented Trends -- 2.2.4 Smooth Transitions and Fourier Series Approximations -- 2.3 Estimating Trends Using Moving Averages -- 2.3.1 Simple Moving Averages -- 2.3.2 Weighted Moving Averages -- 2.4 The Cyclical Component -- 2.4.1 Autoregressive Processes for the Cyclical Component -- 2.4.2 Estimating the Cyclical Component -- 2.5 Some Problems Associated with the Classical Approach to Detrending -- 2.5.1 Further Reading and Background Material -- References -- 3 Stochastic Trends and Cycles -- 3.1 An Introduction to Stochastic Trends -- 3.2 Determining the Order of Integration of a Time Series -- 3.3 Some Examples of ARIMA Modelling -- 3.4 Trend Stationarity Versus Difference Stationarity -- 3.4.1 Distinguishing Between Trend and Difference Stationarity -- 3.4.2 Estimating Trends Robustly -- 3.4.3 Breaking Trends and Unit Root Tests -- 3.5 Unobserved Component Models and Signal Extraction -- 3.5.1 Unobserved Component Models -- 3.5.2 The Beveridge-Nelson Decomposition -- 3.5.3 Signal Extraction -- 3.5.4 Basic Structural Models -- 3.6 Further Reading and Background Material -- References -- 4 Filtering Economic Time Series -- 4.1 Detrending Using Linear Filters -- 4.1.1 Symmetric Linear Filters -- 4.1.2 Frequency-Domain Properties of Linear Filters -- 4.1.3 Designing a Low-Pass Filter -- 4.1.4 High-Pass and Band-Pass Filters -- 4.2 The Hodrick-Prescott Filter -- 4.2.1 The Hodrick-Prescott Filter in Infinite Samples -- 4.2.2 The Finite Sample H-P Filter.
In: Palgrave texts in econometrics
In: Springer eBook Collection
1 Introduction -- 2 'Classical' Techniques of Modelling Trends and Cycles -- 3 Stochastic Trends and Cycles -- 4 Filtering Economic Time Series -- 5 Nonlinear and Nonparametric Trend and Cycle Modelling -- 6 Multivariate Modelling of Trends and Cycles -- 7 Conclusions.
In: Palgrave texts in econometrics
Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In this second edition, Terence Mills expands on the research in the area of trends and cycles over the last (almost) two decades, to highlight to students and researchers the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.
In: Palgrave Texts in Econometrics Ser.
Cover -- Half-Title -- Title -- Copyright -- Contents -- List of Tables and Figures -- Series Editor's Foreword -- 1 Introduction -- 1.1 Historical perspective -- 1.2 Overview of the book -- 2 'Classical' Techniques of Modelling Trends and Cycles -- 2.1 The classical trend-cycle decomposition -- 2.2 Deterministic trend models -- 2.2.1 Linear trends -- 2.2.2 Nonlinear trends -- 2.2.3 Segmented trends and smooth transitions -- Example 2.1 A linear trend for US output per capita -- Example 2.2 Fitting deterministic trends to UK output -- Example 2.3 Fitting a smooth transition to US stock prices -- 2.3 Estimating trends using moving averages -- 2.3.1 Simple moving averages -- 2.3.2 Weighted moving averages -- Example 2.4 Weighted moving average trends for US stock prices -- 2.4 The cyclical component -- 2.4.1 Autoregressive processes for the cyclical component -- 2.4.2 Estimating the cyclical component -- Example 2.5 The cyclical component of US output per capita -- Example 2.6 The cyclical component of UK output -- Example 2.7 The cyclical component of US stock prices -- 2.5 Some problems associated with the 'classical' approach -- Example 2.8 Simulating the Slutsky-Yule effect -- Further reading and background material -- 3 Stochastic Trends and Cycles -- 3.1 An introduction to stochastic trends -- 3.2 Determining the order of integration of a time series -- 3.3 Some examples of ARIMA modelling -- Example 3.1 Modelling the dollar/sterling exchange rate -- Example 3.2 Modelling the FTA All Share index -- Example 3.3 Modelling US output -- 3.4 Trend stationarity versus difference stationarity -- Example 3.4 Testing TS versus DS processes for UK equities and US output -- 3.5 Unobserved component models and signal extraction -- 3.5.1 Unobserved component models -- 3.5.2 The Beveridge-Nelson decomposition.
In: Palgrave Texts in Econometrics
Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. Terence Mills introduces these various approaches to allow students and researchers to appreciate the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.
In: Palgrave texts in econometrics
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