Decomposition of Structural Change into Technology and Price Components: Turkey, 1973–85

The purpose of this paper is to report on an approach to the analysis of intertemporal change in input–output tables in terms of the components of this change with time. We define two alternative forms of decomposition: additive and multiplica tive. The multiplicative form is found to have advantages over the additive form: it is not affected by the choice of price-adjusting mechanism and it enables us to define summary measures in the line of, for example, direct backward linkages. We apply the methodo logy to input–output tables for Turkey for 1973 and 1985, and compute the components of total change for 1973–85. We illustrate that, in economies where price changes across industries vary to a great extent, studying structural change first requires the removal of the relative price effects. Otherwise, misleading conclusions regarding the real or tech nological change are very likely.     

Non‐Fundamentalness in Structural Econometric Models: A Review

Current economic theory typically assumes that all the macroeconomic variables belonging to a given economy are driven by a small number of structural shocks. As recently argued, apart from negligible cases, the structural shocks can be recovered if the information set contains current and past values of a large, potentially infinite, set of macroeconomic variables. However, the usual practice of estimating small size causal Vector AutoRegressions can be extremely misleading as in many cases such models could fully recover the structural shocks only if future values of the few variables considered were observable. In other words, the structural shocks may be non‐fundamental with respect to the small dimensional vector used in current macroeconomic practice. By reviewing a recent strand of econometric literature, we show that, as a solution, econometricians should enlarge the space of observations, and thus consider models able to handle very large panels of related time series. Among several alternatives, we review dynamic factor models together with their economic interpretation, and we show how non‐fundamentalness is non‐generic in this framework. Finally, using a factor model, we provide new empirical evidence on the effect of technology shocks on labour productivity and hours worked.     

Relationship between the Mean and the Constant in a Box–Jenkins Time Series Model

In some publications the mean is identified with the constant of a Box–Jenkins time series model. In this paper the relation between both terms is demonstrated. Furthermore, by means of an example, the errors which may be made if one does not use each term adequately are shown.     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

Employees' attitudes toward organizational change: A literature review

Organizations are increasingly required to improve their ability to enhance employees' support or acceptance for change initiatives. In studies that have examined the conditions in which employees support organizational change, researchers have focused on various attitudinal constructs that represent employees' attitudes toward organizational change. The constructs, which frequently serve as key variables in these studies, include readiness for change, commitment to change, openness to change, and cynicism about organizational change. These constructs have distinct meanings and emphases and therefore they can provide us with different information regarding employees' evaluation of and concerns about particular change initiatives. In this literature review, the author discusses how the constructs are defined in the organizational change literature and synthesizes the antecedents of each construct. Based on the discussion, it is proposed that the constructs are susceptible to situational variables, and may change over time as individuals' experiences change; therefore, they are better conceptualized as states than as personality traits. ©2011 Wiley Periodicals, Inc.     

Process System Modelling of Production Technology Alternatives using Input– Output Tables with Sector Specific Units

Input–output analysis is usually based on tables of accounts expressed in uniform monetary or physical units. However, from a process system modelling perspective, tables of accounts in sector specific units may be more useful for evaluating the effectiveness of new production technologies on reducing pollutant emissions. Using the sector specific unit conceptualization of an IO table, one can consider the effect of changes in direct input coefficients for a particular sector on the complete set of total input coefficients independently from the other direct input coefficients. A process system modelling based method for calculating the total industrial outputs from a new technology matrix together with the new relative prices for each sector output is presented. The method is then used to study the effect of technology changes in the steel making industry in Liaoning Province, China on prices and pollutant emissions.     

What moves housing markets: A variance decomposition of the rent–price ratio

We apply the dynamic Gordon growth model to the housing market in 23 US metropolitan areas, the four Census regions, and the nation from 1975 to 2007. The model allows the rent–price ratio at each date to be split into the expected present discounted values of rent growth, real interest rates, and a housing premium over real rates. We show that housing premia are variable and forecastable and account for a significant fraction of rent–price ratio volatility at the national and local levels, and that covariances among the three components damp fluctuations in rent–price ratios. Thus, explanations of house-price dynamics that focus only on interest rate movements and ignore these covariances can be misleading. These results are similar to those found for stocks and bonds.     

The business cycle in the G-7 economies

In this paper the Hodrick–Prescott filter is used to decompose real GDP for the G7 countries into cyclical and trend components. The resulting series of cyclical components are then examined for static relationships, using correlations and graphs; long-run relationships using autoregressive-distributed lag models; and short-run relationships, using error–correction models. The main result is that the patterns of cyclical behaviour changed following the oil price shocks in the 1970s. Since 1980, cyclical fluctuations have been smaller as a result of a decline in synchronisation of the cycles in the G7. Two separate cycles seem to be developing since 1990. One is for Germany, Italy and France, whilst the other is for the US, UK and Canada. Within each of these groups there are both long-run and short-run relationships between the cyclical components of GDP.     

A methodology for the choice of the best fitting continuous-time stochastic models of crude oil price

The crude oil price is generally considered as the fundamental factor in the valuation of undeveloped reserves but it is not the unique one. Undeveloped field value also depends on the uncertainty relating to the convenience yield and the risk-free interest rate. The purpose of this paper is to decide on the best continuous-time stochastic models for these risk factors. The Generalized Method of Moments and the Maximum Likelihood Estimation are implemented to fit the parameters of continuous-time stochastic processes. The results of unit root tests without breaks reveal a mean reversion in convenience yield series. Multiple structural change tests show that the risk-free interest rate can be considered constant. The simulation of continuous-time stochastic processes and the mean error between the simulated prices and the market ones show that the Geometric Brownian Motion with jumps is the best model for the oil price compared to the other commonly used processes.     

Spatial clustering of time series via mixture of autoregressions models and Markov random fields

Time series data arise in many medical and biological imaging scenarios. In such images, a time series is obtained at each of a large number of spatially dependent data units. It is interesting to organize these data into model‐based clusters. A two‐stage procedure is proposed. In stage 1, a mixture of autoregressions (MoAR) model is used to marginally cluster the data. The MoAR model is fitted using maximum marginal likelihood (MMaL) estimation via a minorization–maximization (MM) algorithm. In stage 2, a Markov random field (MRF) model induces a spatial structure onto the stage 1 clustering. The MRF model is fitted using maximum pseudolikelihood (MPL) estimation via an MM algorithm. Both the MMaL and MPL estimators are proved to be consistent. Numerical properties are established for both MM algorithms. A simulation study demonstrates the performance of the two‐stage procedure. An application to the segmentation of a zebrafish brain calcium image is presented.