| Summary: | 博士 === 國立臺灣大學 === 國際企業學研究所 === 102 === This Ph.D. dissertation comprises two essays on constructing Taiwan''s new macroeconomic indexes.The first essay is entitled New Coincident and Leading Indexes from Series with Different Frequencies, while the second estimating Taiwan''s True Economic Growth Rates.
The purpose of the first essay is to construct new coincident and leading indexes which are able to characterize the fluctuation of Taiwan''s business cycle.
Although the two new indexes portray similar message to those of the composite coincident and leading indexes compiled by the Council of Economic Planning and Development, abbreviated as CEPD,both construction methods are totally different. Specifically, this paper constructs indexes basing on econometric model, while the CEPD uses an indicator approach, by which the composite indexes are subjectively set to be equally weighted average of their component series. Another difference is that the new indexes are designed to generate data weekly, intending to tract economic movements promptly. On the contrary, the CEPD releases its indexes only once in a month. Consequently, the lag time-length owing to the new indexes can be highly shortened.
Following Stock and Watson (1989,1991),Watson (1994),
Mariano and Murasawa (2003), Aruoba et al.(2009) and Aruoba and Diebold (2009), this paper views business condition as the key unobserved variable, and adopts the dynamic factor model to extract it from a group of observed cyclical indicators. The main difference between this paper and the afore-mentioned papers is on how to handle the series of cyclical indicators with mixed frequency. Specifically,
Stock and Watson (1989, 1991) and Watson (1994) use a single frequency of data (monthly) and lose the information of other indicators measured at different frequencies.
Mariano and Murasawa (2003) bring the quarterly GDP series into Stock and Watson (1989, 1991) and Watson (1994). However, to obtain a linear relationship between quarterly and monthly rate of change of GDP, Mariano and Murasawa(2003) specify the level of quarterly GDP as the geometric average of monthly GDP. Their specification obviously violates the general recognition, ie., quarterly flow variable is the summation of its monthly counterparts.
Aruoba et al. (2009) incorporate indicators with up to four kinds of data frequencies including quarterly (e.g., GDP),
monthly (e.g., industrial production), weekly (e.g., employment), and daily (e.g., term premium). All of these indicators are removed deterministic time trend instead of being taken first order difference to attain stationary forms. However, their data transformation is unable to fulfill stationarity requirement and leads to undesirable characteristics. In this paper, we unanimously take log difference for all the data series before they enter the model. Our data treatment method results in a linear aggregation relationship between quarterly and monthly variable, ie., quarterly growth rate of variable is added up from its monthly counterparts. The biggest benefit is our model framework becomes very concise without the shortcomings of the afore-mentioned papers. Although our model contains three kinds of data frequencies including quarterly, monthly and weekly, it can be easily extended to incorporate daily frequency as long as the relevant daily-based data are available.
In this paper, we find three out of seven component series contribute very low share of weights in both of the CEPD''s composite coincident and leading indexes. Once removing those low-weight component series, the underlying business factor extracted from the remaining component series by our model performs as well as the one extracted from comprehensive component series.
The second essay concerns that quarterly GDP growth rates are typically computed using the data from the production and expenditure sides, but the results may be quite different. The Directorate-General of Budget, Accounting and Statistics (DGBAS) in Taiwan chooses the GDP growth rate based on the expenditure side, yet this choice implies that the information in the production side are completely ignored. This paper applies the Kalman filter to estimate the underlying true GDP growth rate and find that the GDP growth rate from the production side tracks the true GDP growth rate better. In order to approximate shares of the underlying true GDP growth rate contributed from the production and expenditure sides, we also apply the combinded forecast theory to obtain optimal weights.
Finally, Mincer-Zarnowitz test reveals that both the preliminary and annual revised GDP growth rates released by the DGBAS are able to rationally forecast the true GDP growth rate.
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