Semi-Parametric Density Estimation for Time-Series with Multiplicative Adjustment

In this article, we extend a class of semi-parametric density estimators to time-series context. The asymptotic theory and simulation study are discussed. Theoretical results and numerical comparison show that in the time-series case, the estimators in this class are better than, or at least competitive with, the traditional kernel density estimator in a broad class of densities.     

Using intervention-oriented evaluation to diagnose and correct students’ persistent climate change misconceptions: A Singapore case study

The evaluation of classroom-based educational interventions is fraught with tensions, the most critical of which is choosing between focusing the inquiry on measuring the effects of treatment or in proximately utilizing the data to improve practice. This paper attempted to achieve both goals through the use of intervention-oriented evaluation of a professional development program intended to diagnose and correct students’ misconceptions of climate change. Data was gathered, monitored and analyzed in three stages of a time-series design: the baseline, treatment and follow-up stages. The evaluation itself was the ‘intervention’ such that the data was allowed to ‘contaminate’ the treatment. This was achieved through giving the teacher unimpeded access to the collected information and to introduce midcourse corrections as she saw fit to her instruction. Results showed a significant development in students’ conceptual understanding only after the teacher's decision to use direct and explicit refutation of misconceptions. Due to the accessibility of feedback, it was possible to locate specifically at which point in the process that the intervention was most effective. The efficacy of the intervention was then measured through comparing the scores across the three research stages. The inclusion of a comparison group to the design is recommended for future studies.     

Multiple Time Series Analysis for organizational research

While multiple time-series analysis (MTSA) is a well-established method in economics, marketing, and finance, few studies have applied MTSA in organizational research. With the growing availability of data sources that contain detailed time-series data and the increasing importance of longitudinal designs, we argue that MTSA blends well with organizational research. We exemplify the possible applications of MTSA to the topics of social media, innovation, ambidexterity, and top management teams. We illustrate the state-of-the-art MTSA technique – Vector Autoregressive (VAR) model – by explaining the key methodological steps needed to estimate and interpret the results and providing a software tutorial in R and STATA. In line with the rising popularity of social media data, we employ a dataset that combines public social media data from Facebook with corporate reputation data from a private data source. We conclude with a discussion on the applicability, limitations, and extensions of MTSA for academics and practitioners.     

Detection of a changepoint,a mean-shift accompanied with a trend change,in short time-series with autocorrelation

In this study, a changepoint model, which can detect either a mean shift or a trend change when accounting for autocorrelation in short time-series, was investigated with simulations and a new method is proposed. The changepoint hypotheses were tested using a likelihood ratio test. The test statistic does not follow a known distribution and depends on the length of the time-series and the autocorrelation. The results imply that it is not possible to detect autocorrelation and that the estimate of the autocorrelation parameter is biased. It is therefore recommended to use critical values from the empirical distribution for a fixed autocorrelation.     

The link between unemployment and crime rate fluctuations: An analysis at the county, state, and national levels

Cantor and Land (1985) developed a theoretical model that proposed two pathways through which economic activity - as indexed by the aggregate unemployment rate - could affect the rate of criminal activity. The first is by increasing levels of criminal motivation within the population as deteriorating economic conditions affect social strain and social control; the second is by influencing the availability and vulnerability of criminal targets and thus the number of criminal opportunities. Although much empirical research has applied this theoretical model, few analyses have done so at disaggregated units of analysis. We present the most comprehensive analysis to date by empirically evaluating this model with data on 400 of the largest US counties - and examine the effects of aggregation on results as these county data are combined to the state and national levels - for the years 1978-2005. For seven Index crimes at each of the three levels of analysis, and with or without controls for structural covariates at each level, the directional effects hypothesized by Cantor and Land are found for 78 out of 84 estimated relationships. Even after taking into account the lack of statistical independence of these estimates by drawing on recently developed statistical theory, this is a very unlikely outcome. In accordance with expectations based on theory and prior research, (a) some of these relationships are weak and not statistically significant, and (b) the strongest and most consistent patterns of relationships for both the crime opportunity and crime motivation effects are found for three property crimes: burglary, larceny, and motor vehicle theft. Suggestions for further research on this topic are given.     

A dynamic econometric model of suicides in Turkey

This study is the first attempt to empirically examine the determinants of suicides in the case of Turkey using the time-series data for the period 1974–2007. This research proposes that the suicides in Turkey are related to some economic and social factors and they exhibit a dynamic relationship amongst them. Auto-Regressive Distributed Lag (ARDL) approach to cointegration testing procedure is employed to obtain the short-run and long-run elasticities of suicides with respect to per capita real income, divorce rates, urbanization and liquidation. The empirical results reveal that the urbanization has the highest impact on suicides, which is followed by per capita real income and liquidation. The results also provide some important policy recommendations to reduce suicides.     

Explaining Changing Suicide Rates in Norway 1948–2004: The Role of Social Integration

Using Norway 1948–2004 as a case, I test whether changes in variables related to social integration can explain changes in suicide rates. The method is the Box-Jenkins approach to time-series analysis. Different aspects of family integration contribute significantly to the explanation of Norwegian suicide rates in this period. The estimated effect of separations is stronger than the effect of divorces, both for men and women, probably because separations are closer in time to the “real” marital breakup. This difference has not been demonstrated in earlier time-series research. Marriages decrease the suicide rates for males. The unemployment estimate for men has a negative sign, contributing to fewer suicides. Both increasing alcohol (beer) consumption and fewer marriages seem to be implicated in the soaring suicide rate for young men since 1970.

A pooled time-series analysis of abortion demand

This study estimates the demand for abortion in the United States using state data pooled over years 1992 and 1982. The empirical results showed that the price elasticity of abortion demand ranged from -0.70 to -0.99 and an income elasticity between 0.27 and 0.35. The demand for abortion was found (1) not to be statistically related to a woman's educational level; (2) to be higher the greater a state's taste for abortion; (3) coincident with the business cycle; and (4) not to be related to the level of a state's welfare payment.     

Economic liberalization in Egypt: A way to reduce the shadow economy?

This study examines the relationship between economic liberalization and the size of the shadow economy in Egypt. We use annual data from 1976 to 2013 and show that economic liberalization policies in Egypt have a statistically significant decreasing effect on the size of the shadow economy as a percent of Gross Domestic Product (GDP). This effect is confirmed for both short- and long-term oriented economic liberalization policies. Based on our results, policies which promote trade and economic liberalization can reduce the extension of the shadow economy in Egypt. Therefore, we suggest conditioning international development assistance to Egypt on demonstrable policy actions with respect to economic liberalization.     

Comprehensive carrying capacity of the urban agglomeration in the Yangtze River Delta,China

Urban agglomeration (UA) is a complex artificial system. Carrying capacity reflects the environmental capability to support human activity. From the perspective of resource supply and demand, the paper selects 12 representative indicators to evaluate the carrying capacity of land, water, transportation and environment. 16 cities of the UA in the Yangtze River Delta, China, are selected as data samples. Time-series global factor analysis is employed to extract the principal factors of the index of 2000 and 2008. The results show that the comprehensive carrying capacity of the UA tends to benign development as a whole except for Shanghai. Carrying capacities of land and water have become the two critical factors to restrict economic and social development. Based on the hierarchical cluster analysis, the values differentiate the UA into significant gradients. The coefficient of variation shows that the spatial differentiation is conspicuous and expanding. The paper also proposes some policies for the government and planners to successfully design and implement the UA.