A Structural Change and Time-Series Analysis of the Female Labor Supply in Japan

2012 ◽  
Vol null (41) ◽  
pp. 199-220
Author(s):  
배해선
Author(s):  
Kwok Pan Pang

Most research on time series analysis and forecasting is normally based on the assumption of no structural change, which implies that the mean and the variance of the parameter in the time series model are constant over time. However, when structural change occurs in the data, the time series analysis methods based on the assumption of no structural change will no longer be appropriate; and thus there emerges another approach to solving the problem of structural change. Almost all time series analysis or forecasting methods always assume that the structure is consistent and stable over time, and all available data will be used for the time series prediction and analysis. When any structural change occurs in the middle of time series data, any analysis result and forecasting drawn from full data set will be misleading. Structural change is quite common in the real world. In the study of a very large set of macroeconomic time series that represent the ‘fundamentals’ of the US economy, Stock and Watson (1996) has found evidence of structural instability in the majority of the series. Besides, ignoring structural change reduces the prediction accuracy. Persaran and Timmermann (2003), Hansen (2001) and Clement and Hendry (1998, 1999) showed that structural change is pervasive in time series data, ignoring structural breaks which often occur in time series significantly reduces the accuracy of the forecast, and results in misleading or wrong conclusions. This chapter mainly focuses on introducing the most common time series methods. The author highlights the problems when applying to most real situations with structural changes, briefly introduce some existing structural change methods, and demonstrate how to apply structural change detection in time series decomposition.


2019 ◽  
Vol 2 (2) ◽  
pp. 107-116
Author(s):  
Sheneela Altaf ◽  
Muhammad Zahir Faridi

Human capital and Trade liberalization are very important for smooth economic growth which gives birth to new employment opportunities that increase labor force participation in all sectors. Both have positive impact on labor supply and overall economy of Pakistan. Human capital has shown positive relation to trained and efficient labor force of the country and trade liberalization strategy. Transformation of labor supply is possible with the help of trade liberalization and technically educated, skilled and well experienced labor. If a country needs to enhance exports and international trade to improve its financial health, it needs technically educated and specialized labor force participation. The key objective of this paper is to analyze the impact of trade openness and human capital on labor supply in Pakistan. The improved labor market and increase in trade liberalization increases government current expenditures and the GDP of Pakistan. The study is based on time series analysis which has shown positive results on Human Capital and trade openness, in both formal and informal sectors. Skills exchange among countries lead to transformation of ideas and helps overall economy.


2017 ◽  
Vol 19 (7) ◽  
pp. 911-933 ◽  
Author(s):  
Brian M. Mills ◽  
Rodney Fort

We extend the attendance break point literature to the team level, addressing structural change and season aggregated outcome uncertainty for franchises in three of the four North American major leagues. We compare the larger variation at the team level with past time series analysis of league-level annual aggregate attendance. We also note that there is at best mixed evidence of outcome uncertainty impacts on team-level attendance. We discuss the implications for these findings with respect to future research that attempts to comprehensively estimate the demand for attendance.


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