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by Chris Chatfield
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slides of lecture notes:
Table of contents
I. Introduction to Stochastic Processes
II. Basic Concepts
II.1 Mathematical Techniques of Time Series Analysis
II.2 (Stochastic) Di
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by Francesco Ravazzolo
Introduction 11
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Predictive gains from forecast combinations using time varying model
weights 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Forecast combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.1 Simple combination schemes . . . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Estimated weight combination schemes . . . . . . . . . . . . . . . . 24
2.2.3 Bayesian model averaging . . . . . . . . . . . . . . . . . . . . . . . 25
2.3 Simulation exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.1 Varying correlations between predictors . . . . . . . . . . . . . . . . 29
2.3.2 Misspeci
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by Helgi Tomasson

The aim of this review is to give a brief review of the statistical tools, models and fundamental concepts that are available for financial data analysis. The approach is set up as an index of basic concepts for the quantiatively minded. This review is inevitably very brief as both finance and statistics are large subjects. Finance is:
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By Ching-Zong Wei, Hwai-Chung Ho, Ching-Kang Ing, Tze Leung Lai
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I like the examples/ exercises mentioned in this paper, nice for beginners..
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John H. Cochrane
1 Preface 7
2 What is a time series? 8
3 ARMAmodels 10
3.1 White noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Basic ARMAmodels . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Lag operators and polynomials . . . . . . . . . . . . . . . . . 11
3.3.1 Manipulating ARMAs with lag operators. . . . . . . . 12
3.3.2 AR(1) to MA(∞) by recursive substitution . . . . . . . 13
3.3.3 AR(1) to MA(∞) with lag operators. . . . . . . . . . . 13
3.3.4 AR(p) to MA(∞), MA(q) to AR(∞), factoring lag
polynomials, and partial fractions . . . . . . . . . . . . 14
3.3.5 Summary of allowed lag polynomial manipulations . . 16
3.4 Multivariate ARMAmodels. . . . . . . . . . . . . . . . . . . . 17
3.5 Problems and Tricks . . . . . . . . . . . . . . . . . . . . . . . 19
4 The autocorrelation and autocovariance functions. 21
4.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Autocovariance and autocorrelation of ARMA processes. . . . 22
4.2.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 25
1
4.3 A fundamental representation . . . . . . . . . . . . . . . . . . 26
4.4 Admissible autocorrelation functions . . . . . . . . . . . . . . 27
4.5 Multivariate auto- and cross correlations. . . . . . . . . . . . . 30
5 Prediction and Impulse-Response Functions 31
5.1 Predicting ARMAmodels . . . . . . . . . . . . . . . . . . . . 32
5.2 State space representation . . . . . . . . . . . . . . . . . . . . 34
5.2.1 ARMAs in vector AR(1) representation . . . . . . . . 35
5.2.2 Forecasts fromvector AR(1) representation. . . . . . . 35
5.2.3 VARs in vector AR(1) representation. . . . . . . . . . . 36
5.3 Impulse-response function . . . . . . . . . . . . . . . . . . . . 37
5.3.1 Facts about impulse-responses . . . . . . . . . . . . . . 38
6 Stationarity and Wold representation 40
6.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2 Conditions for stationary ARMA
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TIME SERIES ANALYSIS: FORECASTING PRODUCT DEMAND AND REVENUE

COPYRIGHT 1997 JAMES L. POWELL

OBJECTIVES OF THIS COURSE
- Learn concepts (and technical jargon) for forecasting problems.
- Get hands-on experience with leading time series methods, especially
Box-Jenkins ARIMA methods and vector autoregressions (VARs).
- See how alternative methods differ in their out-of-sample
predictions.
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by D.S.G. Pollock

These two booklets contain some of the material of the courses titled "Methods of Time-Series Analysis" and "Economic Forecasting" which have been taught in the Department of Economics of Queen Mary College in recent years. The material is presented in the form of a series of ten lectures for a course given at the Institute for Advanced Studies in Vienna titled A Short Course in Time-Series Analysis.

The main link is D.S.G. POLLOCK: An Archive of Selected Works
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