How Can We Extract a Fundamental Trend from an Economic Time- Series?

This paper attempts to extract a fundamental trend, which we call a " trend-cycle component," from an economic time-series. The "trend-cycle component" consists of a medium-term business cycle component and a long- term trend component. The objective is to eliminate the short-term irregular and seasonal variations that hide a fundamental trend in an economic time-series. We test five different time-series methods. Among them, the Henderson moving average (which is incorporated in an X-12- ARIMA seasonal adjustment program), the Band-Pass filter (which utilizes a Fourier transformation), and the DECOMP are found to be effective in extracting a "trend-cycle component" with a cyclical period longer than 1 .5 years. However, no method is found to be effective in extracting a " long-term trend component" with a cyclical period longer than that of a medium-term business cycle. Although the HP filter is somewhat successful , it still contains a component with a cyclical period of about three years that corresponds to a business cycle. These methods are useful for forecasting a wide variety of economic variables because they reveal a fundamental trend in the time series. In addition, statistical programs are available for easy application. They have, however, a few shortcomings. First, it is often difficult to provide a meaningful economic interpretation of the revealed characteristics of the "trend- cycle component." Second, the addition of new data can change the estimation results. In particular, an extracted component around the end of a sample period is likely to be revised with new data. Special caution is in order, therefore, in interpreting the estimation results and forecasting the time series when the data exhibit large variations. In this case, comparing the results of different methods provides a useful way to assess the reliability of an extracted "trend-cycle component."

What Determines the Relation between the Output Gap and Inflation ? An International Comparison of Inflation Expectations and Staggered Wage Adjustment

This paper undertakes a cross-country study on the price- output gap relationship for selected industrialized countries (Japan, the U.S., Germany, the U.K., and Canada). The estimation results show that the price-output gap relationship in these countries can be classified into two categories: (1) a Phillips Curve type (in which the output gap fluctuation affects the inflation rate); and (2) a NAIRU type (in which fluctuations in the output gap affect changes in the inflation rate). In addition, such classifications may vary according to the sample period chosen. During the first half of the observation period (1978-86), NAIRU- type relations existed in all countries except Japan. During the second half (1987-97), NAIRU-type relations were observed in the U.S., the U.K., and Canada, while Phillips Curve-type relations were indicated in Japan and Germany. These results lead to the presumption that the price-output gap relationship is influenced by the recent inflation record, which is one of the most important factors that determine the formation mechanism of inflation expectations and the speed of price adjustment.

Fast Computation of the EM Algorithm for Mixture Models

Mixture models become increasingly popular due to their modeling flexibility and are applied to the clustering and classification of heterogeneous data. The EM algorithm is largely used for the maximum likelihood estimation of mixture models because the algorithm is stable in convergence and simple in implementation. Despite such advantages, it is pointed out that the EM algorithm is local and has slow convergence as the main drawback. To avoid the local convergence of the EM algorithm, multiple runs from several different initial values are usually used. Then the algorithm may take a large number of iterations and long computation time to find the maximum likelihood estimates. The speedup of computation of the EM algorithm is available for these problems. We give the algorithms to accelerate the convergence of the EM algorithm and apply them to mixture model estimation. Numerical experiments examine the performance of the acceleration algorithms in terms of the number of iterations and computation time

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of local binary pattern was used for calculating feature vectors. Support vector machine (SVM) or XGBoost was trained using the feature vectors and their labels. TPE or random search was used for parameter optimization of SVM and XGBoost. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. In conclusion, XGBoost was better than SVM for classifying lung nodules. TPE was more efficient than random search for parameter optimization.Comment: 29 pages, 4 figure

Excessive gingival display treated with two-piece segmental Le Fort I osteotomy : A Case Report

Background. The demand for orthognathic surgery has increased worldwide. Women with jaw deformity tend to have a worse quality of life than men owing to the deformity’s negative effects on body image, low self-esteem, lack of self-confidence, and dissatisfaction with life. Therefore, they wish for more reliable treatment options. Case Description. A woman aged 25 years and 9 months sought treatment for a convex profile and excessive gingival display caused by a skeletal Class II jaw-base relationship. Gingival exposure was up to 6.5 millimeters at full smile. She chose orthognathic surgery, and the authors performed a 2-piece segmental Le Fort I osteotomy and bilateral sagittal split ramus osteotomy. After active orthodontic treatment, the protrusive profile was improved, and an acceptable occlusion and an attractive smile were achieved. Practical Implications. It is hoped that 2-piece segmental Le Fort I osteotomy becomes a common treatment option for patients with protrusive profiles and excessive gingival displays

Objective evaluation method using multiple image analyses for panoramic radiography improvement

Introduction: In the standardization of panoramic radiography quality, the education and training of beginners on panoramic radiographic imaging are important. We evaluated the relationship between positioning error factors and multiple image analysis results for reproducible panoramic radiography. Material and methods: Using a panoramic radiography system and a dental phantom, reference images were acquired on the Frankfurt plane along the horizontal direction, midsagittal plane along the left-right direction, and for the canine on the forward-backward plane. Images with positioning errors were acquired with 1-5 mm shifts along the forward-backward direction and 2-10 degrees rotations along the horizontal (chin tipped high/low) and vertical (left-right side tilt) directions on the Frankfurt plane. The cross-correlation coefficient and angle difference of the occlusion congruent plane profile between the reference and positioning error images, peak signal-to-noise ratio (PSNR), and deformation vector value by deformable image registration were compared and evaluated. Results: The cross-correlation coefficients of the occlusal plane profiles showed the greatest change in the chin tipped high images and became negatively correlated from 6 degrees image rotation (r = -0.29). The angle difference tended to shift substantially with increasing positioning error, with an angle difference of 8.9 degrees for the 10 degrees chin tipped low image. The PSNR was above 30 dB only for images with a 1-mm backward shift. The positioning error owing to the vertical rotation was the largest for the deformation vector value. Conclusions: Multiple image analyses allow to determine factors contributing to positioning errors in panoramic radiography and may enable error correction. This study based on phantom imaging can support the education of beginners regarding panoramic radiography