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Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts.

Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability.

The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.

The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions.

This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

This paper aims to examine the interrelation between two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. The authors first explore whether ex ante more productive firms are those that introduce innovations (the self-selection hypothesis) and if the introduction of innovations boosts TFP growth (the returns-to-innovation hypothesis). Second, the authors study the firm’s joint dynamic decision to implement process and/or product innovations. The authors use Colombian manufacturing data from the Annual Manufacturing and the Technological Development and Innovation Surveys.

This study uses a four-stage procedure. First, the authors estimate TFP using a modified version of Olley and Pakes (1996) and Levinsohn and Petrin (2003), proposed by De Loecker (2010), that implements an endogenous Markov process where past firm innovations are endogenized. This TFP would be estimated by GMM, Wooldridge (2009). Second, the authors use multivariate discrete choice models to test the self-selection hypothesis. Third, the authors explore, using multi-value treatment evaluation techniques, the life span of the impact of innovations on productivity growth (returns to innovation hypothesis). Fourth, the authors analyse the joint likelihood of implementing process and product innovations using dynamic panel data bivariate probit models.

The investigation reveals that the self-selection effect is notably more pronounced in the adoption of process innovations only, as opposed to the adoption of product innovations only or the simultaneous adoption of both process and product innovations. Moreover, our results uncover distinct temporal patterns concerning innovation returns. Specifically, process innovations yield immediate benefits, whereas implementing both product innovations only and jointly process and product innovations exhibit significant, albeit delayed, advantages. Finally, the analysis confirms the existence of dynamic interconnections between the adoption of process and product innovations.

The contribution of this work to the literature is manifold. First, the authors thoroughly investigate the relationship between the implementation of process and product innovations and productivity for Colombian manufacturing explicitly recognising that firms’ decisions of adopting product and process innovations are very likely interrelated. Therefore, the authors start exploring the self-selection and the returns to innovation hypotheses accounting for the fact that firms might implement process innovations only, product innovations only and both process and product innovations. In the analysis of the returns of innovation, the fact that firms may choose among a menu of three innovation strategies implies the use of evaluation methods for multi-value treatments. Second, the authors study the dynamic inter-linkages between the decisions to implement process and/or product innovations, that remains under studied, at least for emerging economies. Third, the estimation of TFP is performed using an endogenous Markov process, where past firms’ innovations are endogenized.

This paper proposes a probit regression with autocorrelated errors (PAR) to estimate the reaction function of monetary policy in Taiwan using newly constructed binary monetary indicators. We develop a practical sampling scheme via the Gibbs sampling algorithm with data augmentation to make posterior inference of the binary monetary policy reaction function. In contrast to the conventional approach, our method avoids the problem of multiple integrals by directly drawing values of latent variables from the relevant full conditional density along with all the other parameters. Empirical results show that the monetary authority responds to macroeconomic conditions asymmetrically. Specifically, in the high‐inflation regime, a contractionary monetary policy is implemented to reduce the inflation rate. Once inflation is under control, that is, in the low‐inflation regime, attention is paid to stimulating the growth of the economy.

This experimental study investigates the connotative (measured) meaning of the concept “auditor independence” within three audit engagement case contexts, including two acknowledged in the literature to represent significant potential threats to independence. The study’s research design utilises the measurement of meaning (semantic differential) framework originally proposed by Osgood et al. (1957). Findings indicate that research participants considered the concept of independence within a two factor cognitive structure comprising “emphasis” and “variability” dimensions. Participants’ connotations of independence varied along both these dimensions in response to the alternative experimental case scenarios. In addition, participants’ perceptions of the auditor’s independence in the three cases were systematically associated with the identified connotative meaning dimensions.

The purpose of this study provide answers to the following research questions: Whether and to what extent money laundering affects the achievability and the trend of Sustainable Development Goals (SDGs)?; Does the influence of money laundering on the SDGs’ achievement differ from developing to developed countries?; How does the influence of money laundering vary among the 17 SDGs?

The paper’s analysis involves two key parts. In the first part, the authors perform a multivariate analysis to examine the influence of money laundering on the achievement of SDGs, and then in the second part, the authors make use of an ordered probit regression model to investigate the impact of money laundering on the trend of attaining each SDG.

Using a sample of 98 developed and developing countries, the regression results from multivariate analysis estimates show that money laundering has a strong inhibiting effect on the achievement of almost all the SDGs in the whole sample of countries and the sub-sample of developing countries, whereas no significant effect is observed for developed countries. However, for the SDG trends, the ordered probit estimates reveal that the harmful effect of money laundering occurs for all countries regardless their development level. In addition, perhaps surprisingly, the results from both the approaches yield also evidence advocating that money laundering activities might be associated with positive externalities on production and consumption. In fact, money laundering is found to have a significant positive influence on the achievement and the trend of SDG12 (Sustainable Consumption and Production). Overall, this study’s findings do have interesting policy implications, especially for developing countries. In these countries, prioritising the formulation and implementation of sound anti-money laundering policies is a necessary requirement for their progress towards achieving the SDGs.

The long-standing tradition of previous empirical studies examining the nexus between money laundering and sustainable development concentrates mainly on the economic dimension of sustainability (i.e. economic growth). However, little is known about the consequences of money laundering activities on the environment and the societies. Consequently, this study seeks to fill this gap by assessing the influence of money laundering on the achievement of the economic, environmental and social goals of sustainable development. To the best of the authors’ knowledge, this is the first integrated study to analyse the potential repercussions of money laundering on the SDGs’ achievement.

This study aims to examine determinants of farmers’ use of climate-smart agricultural practices, specifically improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting in Wadla district, northeast Ethiopia.

A cross-sectional household survey was used. A structured interview schedule for respondent households and checklists for key informants and focus group discussants were used. This study used both descriptive statistics and a multivariate probit econometric model to analyze the collected data. The model was used to compute factors influencing the use of climate-smart agricultural practices in the study area.

The results revealed that households adopted selected practices. The likelihood of farmers’ decisions to use improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting was 85%, 52%, 69% and 59%, respectively. The joint probability of using these climate-smart agricultural practices was 23.7%. The model results confirmed that sex, level of education, livestock holding, access to credit, farm distance, market distance and training were significant factors that affected the use of climate-smart agricultural practices in the study area.

The present study used the most selected locally practiced interventions for climate-smart agriculture.

A growing number of econometric examinations show that works councils substantially shape the personnel policy of firms in Germany. Firms with works councils make greater use of various human resource management (HRM) practices. This gives rise to the question of whether employers view the shaping of personnel policy positively or negatively. Against this background, the purpose of this paper is to examine the influence of works councils on employer attitudes toward HRM practices.

Using data from manufacturing establishments, multivariate and recursive multivariate models are applied to estimate the determinants of employer attitudes toward HRM practices.

The incidence of a works council increases the probability of positive employer attitudes toward the incentive effects of performance pay, profit sharing, promotions, further training and worker involvement in decision making. However, it decreases the probability of positive employer attitudes toward high wages. The results suggest that works councils play a redistribution role in wages and a collective voice role in the other HRM practices.

The study complements examinations focusing on the influence of works councils on the formal presence of HRM practices. There are two potential limitations of focusing solely on formal HRM practices. First, the formal presence of a practice does not necessarily mean that the practice is effectively used. Second, a firm may informally use HRM practices even though the practices have not been formally adopted. The study provides insights into the question of whether or not works councils influence employers’ support for the various practices. This support can be important for the effective use of the practices, regardless of whether they are of formal or informal nature.

The purpose of this paper is to analyze the determinants of agricultural insurance adoption in Bulgaria, using a purpose-built survey of 224 farmers interviewed in 2011. The insurance decision is analyzed conjointly with other risk management decisions on the farm such as having contracts with retailers or processors, diversifying farm activities and using irrigation.

The agricultural insurance sector in Bulgaria is presented in the broader context of the transition to a market-oriented economy and integration of Bulgarian agriculture into the EU Common Agricultural Policy. The recent developments on the determinants of farm insurance adoption in the agricultural economics and finance literature are discussed. A multivariate probit model is used in order to determine the factors explaining the adoption or non-adoption of various risk management tools by the surveyed farmers, including farm insurance.

The authors find that farmers with diversified activities, using irrigation or having contracts with retailers or processors, are more likely to adopt insurance, after controlling for farms and farmers’ structural characteristics. Additionally, the authors find that the main characteristics distinguishing farmers who purchase agricultural insurance from non-users are farm size and farm location. The existence of strong regional effect suggests the importance of adapting the insurance products to the different regional contexts in Bulgaria.

This paper contributes to the (limited) literature on agricultural insurance adoption in transition countries, currently shifting from a system where compensation against natural hazards tended to come from a State damage mitigation fund, inherited from the centrally planned governments to private and voluntary agricultural insurance. This research provides a unique data source on the Bulgarian case study.

The objectives of this study are to offer cash based funds flow components as an alternative to financial ratios for classifying the financial performance of companies; to test empirically the ability of funds flow components to distinguish between failed and nonfailed companies with special emphasis on working capital components; to analyse the empirical results and make recommendations for future study.

The purpose of this paper is to draw on recent developments in the open innovation literature to explore whether the openness of SMEs to the four categories of external sources of information (ESI) is complementary, substitute or independent, while assessing the determinants of SMEs’ openness to these ESI.

This research is based on data from a survey of 451 manufacturing SMEs in the province of Québec, Canada. Data have been elaborated through a multivariate probit model to empirically show that SMEs are considered to be simultaneously open to different ESI. The results of this study show significant heterogeneity in the determinants of SMEs’ openness to these ESI.

The study found that the SMEs’ openness to different ESI seems to be complementary rather than substitute; and not all variables included in the model explain the SMEs’ openness to the different ESI.

The paper provides practical implications for managers and policy makers including the SMEs’ managers’ role to recognize the consolidation of different ESI jointly instead of separately. Furthermore, managers and policy makers should attempt to provide a fair context to SMEs to manage their openness ecosystem.

This study is virtually the first to investigate both the complementarity and the determinants of SMEs’ openness to different ESI using a sophisticated econometric model.