| dc.contributor.advisor | 高安邦 | zh_TW |
| dc.contributor.author (Authors) | 胡文傑 | zh_TW |
| dc.creator (作者) | 胡文傑 | zh_TW |
| dc.date (日期) | 2007 | en_US |
| dc.date.accessioned | 6-May-2016 16:55:01 (UTC+8) | - |
| dc.date.available | 6-May-2016 16:55:01 (UTC+8) | - |
| dc.date.issued (上傳時間) | 6-May-2016 16:55:01 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0094258023 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/94544 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 經濟學系 | zh_TW |
| dc.description (描述) | 94258023 | zh_TW |
| dc.description.abstract (摘要) | 本論文採用了由Box and Jenkins(1976)所提出的ARIMA模型，以及由BOX and Tiao(1975)所提出的Intervention Model，去配適台灣的失業率型態，以及比較其預測的結果。 結果顯示出台灣的失業率具有季節性的型態，亦即台灣的失業率並非僅僅受到月分之間的相關，年分之間也有所關連。是故，當本論文在預測失業率的水準時，也考慮到此一因素，加入季節性的ARIMA模型對台灣的失業率加以預測。另外，時間序列的資料常常受到外生因素的干擾。對於失業率來說，政策上的改變將會影響失業率本身的結構，因此利用介入模式預測失業率，可以得到一組較精確的預測值。介入模式的事件有以下五個，分別是解嚴、六年國建、台灣引進外勞、中共飛彈試射、新十大建設。前四個事件的確影響了失業率的結構，不過第五項，也就是新十大建設並沒有顯著影響失業率的結構。理由可能是新十大建設的內容並不能合宜的解決經濟上與社會上的問題，以及這些建設尚未完工，以致無法達到期預期的效果。 比較兩模型的預測結果時，採用了MPE、MSE、MAE、MAPE作為模型評估的準則，結果指出介入模式的預測結果比起季節性ARIMA的預測結果來的有效率。 | zh_TW |
| dc.description.abstract (摘要) | This article adopts the ARIMA model, which was first introduced by Box and Jenkins (1976), and the intervention model, which was developed by Box and Tiao (1975), to fit the time series data for the unemployment rate in Taiwan, and thus to compare the results of the forecasts. The results reveal that there is a seasonal effect in the data on the unemployment rate. This indicates that the unemployment rate figures are not only related from month to month but are also related from year to year. When forecasting the level of unemployment, we should examine not only the neighboring months but also the corresponding months in the previous year. Time series are frequently affected by certain external events. In the discussion on the unemployment rate, the policies implemented by the government as well as military threats indeed influence the structure of the series. By making a forecast using the intervention model, we can evaluate the effect of the external events which would give rise to more accurate forecasts. In this study, there were five interventions included in relation to the unemployment rate series, which were as follows. First, the lifting of Martial Law in February 1987. Second, the Six-year National Development Plan launched in June 1991. Third, the hiring of foreign labor in Taiwan, which took effect in October 1991. Fourth, the threats of missile tests from the PRC in Feb 1996. Fifth, the ten new construction programs launched in November 2003. The first four events were indeed found to give rise to a structural change in the unemployment rate series at the moment when they occurred. This result might also have implied that not all of the actual effect of expansionary policies could have exactly decreased the unemployment rate, and therefore have solved the economic and social problems simultaneously. When we refer to the comparison of the above two models, the ultimate choice of a model may depend on its goodness of fit, such as the residual mean square, AIC, or BIC. As the main purpose of this study is to forecast future values, the alternative criteria for model selection can be based on forecast errors. The comparison is based on statistics such as MPE, MSE, MAE and MAPE. The results indicate that the intervention model outperforms the seasonal ARIMA model. | en_US |
| dc.description.tableofcontents | 1. INTRODUCTION 1.1 General Background Information 1.2 Research Motivation 1.3 Research Purpose 1.4 Research Structure 2. LITERATURE REVIEW 2.1 Citation 2.2 Commentary 3. METHOD 3.1 Research Design and Empirical Model 3.1.1 What is Unemployment? 3.1.2 Forecasting 3.1.3 Unit Root Tests 3.1.4 Seasonal ARIMA Model 3.1.5 Intervention Model 3.1.6 Model Selection Based on Forecast Errors 3.2 Data Sources and the Introduction of External Interventions 3.2.1 Data Sources 3.2.2 Introduction of External Interventions 4. RESULTS 4.1 Seasonal ARIMA Model 4.1.1 Unit Root Tests 4.1.2 Model Identification, Parameter Estimation, and Diagnostic Checking 4.1.3 Forecasting 4.2 Intervention Model 4.2.1 Identification, Parameter Estimation, and Diagnostic Checking for the Noise Model 4.2.2 Identification, Parameter Estimation, and Diagnostic Checking for the Intervention model 4.2.3 Forecasting 5. CONCLUSIONS 5.1 Research Findings 5.1.1 Models 5.1.2 Forecasts 5.2 Conclusion 5.3 Limitations of the Study and Recommendations for Future Research…….70 References Acknowledgements | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0094258023 | en_US |
| dc.subject (關鍵詞) | 失業率 | zh_TW |
| dc.subject (關鍵詞) | 介入模式 | zh_TW |
| dc.subject (關鍵詞) | 季節性ARIMA模型 | zh_TW |
| dc.subject (關鍵詞) | 預測 | zh_TW |
| dc.subject (關鍵詞) | Unemployment Rate | en_US |
| dc.subject (關鍵詞) | Intervention Model | en_US |
| dc.subject (關鍵詞) | Seasonal ARIMA Model | en_US |
| dc.subject (關鍵詞) | Forecasting | en_US |
| dc.title (題名) | 台灣失業率的預測-季節性ARIMA與介入模式的比較 | zh_TW |
| dc.title (題名) | Forecasting Taiwan’s Unemployment Rate –A Comparison Between Seasonal ARIMA and the Intervention Model | en_US |
| dc.type (資料類型) | thesis | en_US |
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