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Deregulation shifts the responsibility for mitigation of agency problems from the regulatory parties to the firms' shareholders. We investigate whether and how governance structure changes in response to the dynamics of the new business environment after the Regulatory Reform Act of 1994 for the US trucking industry. We show that deregulation increases market competition in the trucking industry. The deregulated trucking firms not only adjust internal governance structure but also alter antitakeover provisions to adapt themselves to the competitive status of business environment after deregulation.

This study attempts to analyze energy intensity, capacity utilization (CU), output and productivity growth of aggregate manufacturing sector in India during the period 1980–1981 to 2016–2017. A decadal analysis as well as a comparison between pre- and post-liberalization period of productivity growth is also made. Total factor productivity growth (TFPG) is also adjusted with CU to obtain adjusted TFPG. The trend in energy intensity is also analyzed to answer the question of sustainability. Results shows that TFPG declined in the post-reform period, highlighting the fact that liberalization process has its adverse impact on productivity growth. From the study it is observed that a declining trend in adjusted TFPG in the post-reform period, but the rate of decline is higher. Energy intensity and CU of the Indian manufacturing industries is found to be increasing over the study period. Increasing energy intensity, quite significantly, would increase the level of pollution generated by the manufacturing industries. So, interestingly enough, this may lead to conclude that the growth of the manufacturing industries is not in line with the basic essence of sustainable development.

Control charts have been widely used in the manufacturing industry as the most important statistical process control (SPC) technique. One assumption for successfully applying control charts is that the process is stable to begin with and adjustments are made only at a later point when the process is deemed to be out of control. A situation that often arises is when the process is subject to a slow trend caused by factors such as equipment deterioration, power decline during its consumption, or other reasons. For this type of situation, an integrated monitoring and adjustment approach can be considered and the relevant issues involved are discussed in this paper. This problem of implementing SPC is studied from the point of view of process management, together with the concept of engineering process control. A general approach is then formulated for monitoring processes with trend and subject to regular adjustments and a specific model for a general case with linear trend is studied in more detail.

A common feature of certain kinds of data is a high level of statistical dependence across space and time. This spatial and temporal dependence contains useful information that can be exploited to significantly reduce the uncertainty surrounding local distributions. This chapter develops a methodology for inferring local distributions that incorporates these dependencies. The approach accommodates active learning over space and time, and from aggregate data and distributions to disaggregate individual data and distributions. We combine data sets on Kansas winter wheat yields – annual county-level yields over the period from 1947 through 2000 for all 105 counties in the state of Kansas, and 20,720 individual farm-level sample moments, based on ten years of the reported actual production histories for the winter wheat yields of farmers participating in the United States Department of Agriculture Federal Crop Insurance Corporation Multiple Peril Crop Insurance Program in each of the years 1991–2000. We derive a learning rule that combines statewide, county, and local farm-level data using Bayes’ rule to estimate the moments of individual farm-level crop yield distributions. Information theory and the maximum entropy criterion are used to estimate farm-level crop yield densities from these moments. These posterior densities are found to substantially reduce the bias and volatility of crop insurance premium rates.

This study statistically examines the shifting distribution channels in the American wine industry based on the growth trajectory of sales, seasonality and disruption due to consumers switching to online platforms. The purpose of this paper is to design a model that will have general applicability beyond the wine industry.

The research uses regression-based additive decomposition of time series data to predict the trajectory of the market share for the digital distribution channel. The study develops a statistical prediction model using time series data between 2007 and 2020, inclusive, sourced from US Annual Wine Reports and Bureau of Alcohol, Tobacco and Firearms databases.

The results show an increasing trajectory of wine sales through the online distribution channel with predictable seasonality. The disruptive effects of consumer switching behavior point to a steady increase in sales due both to increasing demand and accelerating switching. Nevertheless, the model shows that bricks and mortar purchases will remain strong and continue to account for the bulk of wine sales. COVID-19 has caused a step function increase in online sales but this should moderate after the crisis subsides and can be tested further.

This study is original in developing a model for an industry where bricks and mortar sales are growing and are expected to remain strong while there is still identifiable switching to online sales. The wine industry presents a classic case of accelerating switching behavior where there is still a strong franchise for in-store purchases. The model should have general applicability to distribution channels beyond the wine industry where steady growth, marked seasonality and disruptive consumer switching are in evidence.

This chapter covers the conceptual framework and presents practical guidelines for using single-case research (SCR) methods to determine evidence-based treatments. We posit that SCR designs contribute compelling evidence to the knowledge base that is distinct from group design research. When effect sizes are calculated SCR can serve as a reliable indicator of how much behavior change occurs with an intervention in applied settings. Strong SCR design can determine functional relationships and effect sizes with confidence intervals can represent the size and the certainty of the results in a standardized manner. Thus, SCR is unique in retaining data about the individual and individual effects, while also providing data that can be aggregated to identify evidence-based treatments and examine moderator variables.

The authors in this paper aim to investigate the performance of different portfolios of REITs which specialise by property type compared to the performance of a diversified free-float market capitalisation-weighted benchmark index to determine whether superior risk-adjusted returns can be achieved.

First, the authors examine the performance of portfolios constructed using the criteria of equal weight, minimum variance, maximum Sharpe and risk parity rather than free-float market capitalisation. Second, the authors apply an automated trading strategy of trend following to see if this filter will improve risk-adjusted returns.

The two-step process of forming combinations of REIT sectors with the subsequent addition of a trend following overlay can offer clear benefits relative to a passive benchmark investment.

Although three of the four strategies were shown to outperform the benchmark index on a risk-adjusted basis, one issue was that the efficient portfolios tended to have large weightings to relatively few sectors. The authors also found that maximum drawdowns (losses) of the strategies tended to be rather high, as was the benchmark.

The methods outlined in this paper can be applied to construct superior risk-adjusted REIT portfolios globally.

Although studies have been undertaken separately on REIT specialisation and trend following in equity and commodity markets, this paper is the first to combine the two topics, and therefore has particular value for real estate fund managers globally.

This study is to examine factors contributing to the variability in chronic obstructive pulmonary disorder (COPD) and asthma hospitalization rates when the influence of patient characteristics is being simultaneously considered by applying a risk adjustment method.

A longitudinal analysis of COPD and asthma hospitalization of rural Medicare beneficiaries in 427 rural health clinics (RHCs) was conducted utilizing administrative data and inpatient and outpatient claims from Region 4. The repeated measures of risk-adjusted COPD and asthma admission rate were analyzed by growth curve modeling. A generalized estimating equation (GEE) method was used to identify the relevance of selected predictors in accounting for the variability in risk-adjusted admission rates for COPD and asthma.

Both adjusted and unadjusted rates of COPD admission showed a slight decline from 2010 to 2013. The growth curve modeling showed the annual rates of change were gradually accentuated through time. GEE revealed that a moderate amount of variance (marginal R ² = 0.66) in the risk-adjusted hospital admission rates for COPD and asthma was accounted for by contextual, ecological, and organizational variables.

The contextual, ecological, and organizational factors are those associated with RHCs, not hospitals. We cannot infer how the variability in hospital practices in RHC service areas may have contributed to the disparities in admissions. Identification of RHCs with substantially higher rates than an average rate can portray the need for further enhancement of needed ambulatory or primary care services for the specific groups of RHCs. Because the risk-adjusted rates of hospitalization do not vary by classification of rural area, future research should address the variation in a specific COPD and asthma condition of RHC patients.

Risk-adjusted admission rates for COPD and asthma are influenced by the synergism of multiple contextual, ecological, and organizational factors instead of a single factor.

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.

Recently, USDA-RMA introduced a Trend Adjusted-Actual Production History (TA-APH) program, which increases APH by a trend factor to cover yield changes over time. The purpose of this paper is to examine the effects of the TA-APH program on farmer participation, coverage election, and risk by analyzing data before and after the program.

Since the program was carried out in selected counties, the authors employ a difference in differences approach doing comparisons of insurance participation and coverage levels between eligible and ineligible counties.

The authors find that farmers within the counties where the TA-APH program was available experienced an increase in insured acres of 3 percent for corn and 5 percent for soybeans. The authors also find the farmers eligible for the program purchased lower coverage levels relative to those not eligible. However, the magnitude of that negative effect is relatively small, −0.9 percent in corn and −1.3 percent in soybeans. Collectively the evidence shows the TA-APH program does increase the guaranteed yield level mitigating farmer risk.

The data set used only permitted analysis at the county level, thus the authors could not look at the individual farmer choices.

The results suggest that if a greater level of farmer risk protection is desired from crop insurance, the authors find that the trend adjustment as implemented was a successful way to achieve this.

This paper contributes to the literature on the crop insurance by evaluating the program controlling for a non-participating groups, farming experience, liability rates, and subsidy rates. In doing this, it fulfills an identified need to study the actual impact on participation rates and coverage levels elected.