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One component of revenue forecast error has been attributed to the phenomena of consistent underestimation bias due asymmetrical loss. Because underestimation of revenue forecast results in less loss to forecasters than overestimations, there appears to be a bias for forecasters to underestimate revenue forecasts. This paper confirms this hypothesis. Additionally, with the greater usage of national forecasting organizations that provide economic forecasts on which revenue forecasts are based, a secondary source of forecaster bias may be present in many state level forecasts. This hypothesis is supported by the increase in number of states using such organizations and a decrease in the standard deviation of the annual mean percentage state forecast error.

We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.

Studies on consumer knowledge calibration have used different measures of calibration. The purpose of this paper is to undertake a comparative assessment of important measures. In addition, it seeks to identify the best performing measure.

The paper reports on three studies. The first study uses eight survey data sets. The second and third studies use experiments.

The study found that the Brier score component measure is most responsive to feedback and is the most suitable measure of knowledge calibration. The results also indicate that researchers should use measures that use item-level confidence judgements, as against an overall confidence judgement.

By documenting the relationship between the different measures of knowledge calibration, the study enables proper interpretation and accumulation of results of various studies that have used different measures. The study also provides guidance to researchers in psychology and education where this issue has been noted.

The study provides guidance to managers in knowledge intensive industries, such as finance and insurance, interested in understanding their consumers’ knowledge calibration.

This is the first study in consumer research that examines this issue.

We consider conditional distribution and conditional density functionals in the space of generalized functions. The approach follows Phillips (1985, 1991, 1995) who employed generalized functions to overcome non-differentiability in order to develop expansions. We obtain the limit of the kernel estimators for weakly dependent data, even under non-differentiability of the distribution function; the limit Gaussian process is characterized as a stochastic random functional (random generalized function) on the suitable function space. An alternative simple to compute estimator based on the empirical distribution function is proposed for the generalized random functional. For test statistics based on this estimator, limit properties are established. A Monte Carlo experiment demonstrates good finite sample performance of the statistics for testing logit and probit specification in binary choice models.

Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

This paper aims to presents one of the first direct micro-econometric impact of competitive industries (based on revealed comparative advantage [RCA] between Pakistan and China) on household welfare in Pakistan using semi-parametric matching technique.

The study has also measured and identified the industrial competitiveness in both agricultural and non-agricultural (manufacturing) industries using RCA approach. RCA at the four-digit ISIC level are matched to household survey data (Pakistan Social and Living Standard Measurement) for 2013-2014 to represent the competitive industries in which the household’s higher earner is employed.

The findings of the study reveal that the China–Pakistan ex-post treatment effect (industrial competitiveness) provides welfare-improving effects. Furthermore, on this behalf, this study further assesses ex-ante treatment effects of recently signed China–Pakistan Economic Corridor (CPEC) on household welfare and suggests that CPEC would boost further trade liberalization and, therefore, would lead to industrial competitiveness and hence economic growth.

Paper contributes to two streams of literature. First, it measures and identifies the industrial competitiveness in both agricultural and non-agricultural industries using RCA approach; and second, it assesses the welfare of those households associated with these industries using semi-parametric propensity score matching technique.

This study examines the adoption drivers of scale-appropriate mechanization in Nepal's maize-based farming systems. The authors also assess the contribution of scale-appropriate mechanization to the United Nations Sustainable Development Goals (SDGs) of zero hunger (SDG2) and no poverty (SDG1).

Propensity score matching and doubly robust inverse probability-weighted regression adjusted methods were applied to estimate the effects of mini-tiller adoption. These methods control the biases that arise from observed heterogeneities between mini-tillers users and nonusers.

The study findings show that farm size, labor shortages, draft animal scarcity, market proximity, household assets and household heads' educational level influence the adoption of mechanization in Nepal. Mechanized farms exhibited enhanced maize productivity, profits and household food self-sufficiency. Reduced depth and severity of poverty were also observed. Nevertheless, these effects were not uniform; very small farms (≤0.41 ha) facing acute labor shortages benefited the most.

The study results suggest that policymakers in developing nations like Nepal may wish to expand their emphasis on scale-appropriate mechanization to improve farm productivity and household food security, reduce poverty and contribute to the SDGs.

This first-of-its-kind study establishes the causal effects between scale-appropriate farm mechanization and SDG1 (no poverty) and SDG2 (zero hunger) in a developing nation.

Establishing the strength of a novel variable–mortgage debt as a fraction of US gross domestic product (GDP)–on forecasting capitalisation rates in both the US office and multifamily sectors.

The authors specify a vector error correction model (VECM) to the data. VECM are used to address the nonstationarity issues of financial variables while maintaining the information embedded in the levels of the data, as opposed to their differences. The cap rate series used are from Green Street Advisors and represent transaction cap rates which avoids the problem of artificial smoothness found in appraisal-based cap rates.

Using a VECM specified with the novel variable, unemployment and past cap rates contains enough information to produce more robust forecasts than the traditional variables (return expectations and risk premiums). The method is robust both in and out of sample.

This has direct implications for governmental policy, offering a path to real estate price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable future.

This study offers a new and highly explanatory variable to the literature while being among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return expectations and risk premiums).

Cooperatives are used as one of the strategic tools to reach smallholder farmers and reduce household poverty through augmenting access to inputs, technologies, farm productivity and markets. Our study aims to investigate the impacts of the Meki Batu Cooperative Union on reducing household poverty.

This study employed a cross-sectional survey of households in the Dugda district of the East Shewa zone in the Oromia region. A two-stage sampling design was applied, where four rural Kebeles were first randomly picked, followed by stratified random samples of 217 producers comprising 100 members and 117 non-members of cooperatives. The standard probit model was estimated with a set of observable factors. Propensity score matching (PSM), doubly robust inverse probability weighting and treatment effect estimation were performed along with matching techniques.

The results reveal that education, livestock assets, access to irrigation and extension contact positively determine participation in cooperatives. As the income-based poverty attests, the poverty gap was reduced by 5.9–6.3% and the severity of poverty by 3.7–3.8% due to the cooperative membership.

The investigation suggests the need for continued and comprehensive social services to address development challenges through the facilitation of producers’ engagement in collective actions and agribusinesses.

Existing research evidence is inconclusive with the view of impacts of collective actions on housed welfare in Ethiopia. This study empirically tested the impacts in connection to the production and marketing of high-value crops.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2024-0231