베이지안 다계층모형을 이용한 가격인상에 따른 판매량의 동적변화 추정 및 예측

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expect it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. Above factors make the sales dynamic and unstable. We develop a time series model to evaluate the sales patterns with stockpiling and short term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods

단위근과 수준변화에 대한 동시가설 검정

시계열의 구조를 정확히 파악하기 위해서는 구조변화와 단위근의 존재여부를 가설 검정하는 것이 필요하다. 하지만, 이 두 가지의 가설들은 각각 검정되어 왔고, 이에 따라 단위근도 존재하고 구조변화도 존재하거나, 어느 하나만 존재하는 시계열들을 구분하여 파악하는 데에 있어서는 한계점이 있었다. 이 논문에서는 상태공간모형과 시뮬레이션 방법에 근거하여 두 가지 가설을 동시에 검정하는 절차를 제시하여 논란이 되는 시계열들에 대해 합리적인 해결책을 제시하고자 한다

STRUCTURAL CHANGES IN DYNAMIC LINEAR MODEL

The author is currently assistant professor of Management Science at Korea Advanced Institute of Science and Technology, following a few years as assistant professor of Industrial Engineering at Kyung Hee University, Korea. He received his doctorate from the department of Industrial Engineering and Operations Research, University of California, Berkeley. His research interests are time series and forecasting modelling, Bayesian forecasting and the related software development. He is now teaching time series analysis and econometrics at the graduate level

Exponential Smoothing with an Adaptive Response to Random Level Changes

Exponential smoothing methods have enjoyed a long history of successful applications and have been used in forecasting for many years. However, it has been long known that one of the deficiencies of the method is an inability to respond quickly to interventions to interruptions, or to large changes in level of the underlying process. An exponential smoothing method adaptive to repeated random level changes is proposed using a change-detection statistic derived from a simple dynamic linear model. The results are compared with Trigg and Leachs and the exponential smoothing methods

경쟁적 가격 행동과 시장구조분석: 한국 이동통신 시장에의 응용

After the launch of PCS in 1997, price competition between five mobile carriers was so severe that the Korean mobile telephony market achieved a remarkable subscriber base growth. But in that optimal pricing behavior depends on how each firm is likely to react to other firms’ choice of price, it is very interesting to analyze competitive pricing behavior and understand market structure in terms of pricing competitiveness in the Korean mobile telecommunications market. In this paper, we use structural econometric models in New Empirical Industrial Organization (NEIO) framework. But previously used models in this framework generally assume that market size is fixed and that all firms maximize their profits. To fit in with the Korean mobile telephony market, we derive various models in using MNL market share model under the assumptions that market size varies with industry’s total attractions and that firms maximize their market share. In this paper, we find that the model under market share maximization with the assumption that market size varies with total attraction shows the best fitting results

State Space Model & Structural Changes in Business Cycle

일반적으로 경영경제시계열에 내재된 구서요소들에 대한 분석을 위해 상태공간모형을 널리 사용하고 있다. 상태공간모형으로 표현된 경영경제시계열은 내재된 구성요소에 대한 추가적인 해석이 가능한 장점이 있으나, 상태공간모형을 적용함에 있어 모형에 대한 충분한 이해가 부족할 경우 왜곡된 해석을 보일 수 있는 위험이 존재하기도 한다. 본 연구에서는 상태공간모형에 의한 경영경제시계열의 장기추세와 단기변동의 분해 과정 및 이를 바탕으로 한 경영경제시계열의 구조변화에 대한 분석결과를 경기변동의 전환점 분석에 대한 실증연구와 함께 제시하고 있다

An Adaptive Structural Model When There is a Major Level Change

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes