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TerenceC.Mills, TheGlobalWarmingPolicyFoundation, 2016년 발간

대분류 | 키워드 | Time Horizon | Quality | Territorial Scope |
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Economics | STATISTICAL FORECASTING | 없음 | Recommand | Global |

The analysis and interpretation of temperature data is clearly of central importance to debates about anthropological globa lwarming(AGW) and climate change in general. For the purpose of projecting

future climate change, scientists and policymakers rely heavily on large-scale ocean–atmosphere general circulation models, which have grown in size and complexity over recent decades without necessarily becoming more reliable at forecasting. The field of economics spent the post-war decades developing computerised models of the economy that also grew to considerable size and complexity, but by the late 1970s two uncomfortable truths had been realised. First ,thesemodels produced generally poor forecasts, and adding more equations or numerical detail did not seem to fix this.

Second, relatively simple statistical models that had no obvious basis in economic theory were proving much more reliable at forecasting. It took many years fore conomists to rationalise statistical forecasting

by working out its structural connections to this theory.

But before this had happened, economic practitioners were already relying on these models simply because of their relative success.

1 Introduction

2 Basic time-series modelling

3 Fitting basic models to temperature series

4 Seasonal extensions of the basic model

5 Fitting seasonal models to temperature series

6 Forecasting from time series models

7 Forecasting temperature series

8 Discussion

9 Appendix: Technica ldetailson ARIMA analysis

10 Biblio graphy

- 원문파일 : STATISTICAL_FORECASTING.pdf

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