A comparison of alternative models for estimating firm’s growth rate

The growth rate plays an important role in determining a firm’s asset and equity values, nevertheless the basic assumptions of the growth rate estimation model are less well understood. In this paper, we demonstrate that the model makes strong assumptions regarding the financing mix of the firm. In addition, we discuss various methods to estimate firms’ growth rate, including arithmetic average method, geometric average method, compound-sum method, continuous regression method, discrete regression method, and inferred method. We demonstrate that the arithmetic average method is very sensitive to extreme observations, and the regression methods yield similar but somewhat smaller estimates of the growth rate compared to the compound-sum method. Interestingly, the ex-post forecast shows that arithmetic average method (compound-sum method) yields the best (worst) performance with respect to estimating firm’s future dividend growth rate. Firm characteristics, like size, book-to-market ratio, and systematic risk, have significant influence on the forecast errors of dividend and sales growth rate estimation.     

The process followed by PPP data. On the properties of linearity tests

Recent research has reported the lack of correct size in stationarity test for PPP deviations within a linear framework. However, theoretically well motivated non-linear models, such as the ESTAR, appear to parsimoniously fit the PPP data and provide an explanation for the PPP ‘puzzle’. Employing Monte Carlo experiments the size and power of the non-linear tests are analysed against a variety of nonstationary hypotheses. Aslo the ESTAR model is fitted to data from high inflation economies. The results provide further support for ESTAR specification.     

Policy evaluation as science: a critical assessment

During theh last few years policy evaluation has become an important area of research. Urban and regional policies have been subjected to more detailed scrutiny than ever before. Yet the gowing body of evaluative research remains isolated from many wider debates in the social and economic sciences. This can be attributed partly to the predilection for scientific methods, caused by the apparent certainty and objectivity they confer. This orientatioin is reflected in the tendency to separate evaluation from the policy process; the narrow perspective of many evaluative studies; the emphasis on quantification; the mechanical approach to analysis; and the inattention paid to undestanding causal processes.     

Sequential estimate for linear regression models with uncertain number of effective variables

As a result of novel data collection technologies, it is now common to encounter data in which the number of explanatory variables collected is large, while the number of variables that actually contribute to the model remains small. Thus, a method that can identify those variables with impact on the model without inferring other noneffective ones will make analysis much more efficient. Many methods are proposed to resolve the model selection problems under such circumstances, however, it is still unknown how large a sample size is sufficient to identify those “effective” variables. In this paper, we apply sequential sampling method so that the effective variables can be identified efficiently, and the sampling is stopped as soon as the “effective” variables are identified and their corresponding regression coefficients are estimated with satisfactory accuracy, which is new to sequential estimation. Both fixed and adaptive designs are considered. The asymptotic properties of estimates of the number of effective variables and their coefficients are established, and the proposed sequential estimation procedure is shown to be asymptotically optimal. Simulation studies are conducted to illustrate the performance of the proposed estimation method, and a diabetes data set is used as an example.     

Trade integration and within-plant productivity evolution in Chile

We analyze the impact of trade integration on plant TFP using Chilean plant-level data (1982–1999) and 3-digit bilateral trade flows. Our contribution is to disentangle the impact of export and import barriers, estimated as border effects within a multilateral context. A fall in export barriers is positively correlated with plant productivity in traded sectors. The reduction of import barriers, however, can only be associated to productivity improvements in export-oriented sectors. In import-competing sectors a robust positive correlation shows up between plant productivity and protection. We then test several channels linking trade integration and firm productivity.     

Understanding China's demand for resource imports

Is China's demand for resources driven predominantly by domestic factors or by global demand for its exports? The answer to this question is of interest given the highly resource-intensive nature of China's growth, and is important for many resource-exporting countries, such as Australia, Brazil, Canada and India. This paper provides evidence that China's (mainly manufacturing) exports have been a significant driver of its demand for resource commodities over recent decades. First, it employs input–output tables to demonstrate that, historically, manufacturing has been at least as important as construction as a driver of China's demand for resource-intensive metal products. Second, it shows that global trade in non-oil resource commodities can be described by the gravity model of trade. Using this model it is found that, controlling for other determinants of resource trade, exports (and the manufacturing sector more generally) are a sizeable and significant determinant of a country's resource imports, and that this has been true for China as well as for other countries.     

Preparing Russian Small Innovative Enterprises for International Competitiveness: A Scoping Study

Russian small innovative enterprises (SIEs) are emerging as an important force behind the restructuring of the Russian economy and its industrial and commercial infrastructure. The research presented in this paper suggests that there are at least three categories of factors that impact the move of Russian SIEs into international markets: (i) macro-economic obstacles (ii) lacking managerial and business competencies and (iii) differences in culture and business practices. The first factor cannot be directly influenced by the individual SIE, while the latter two represent opportunities of a developmental nature. The work of this paper purports to lay the groundwork for more theoretical follow-up analyses.     

Examining the success of women of color-owned small and medium-sized enterprises in the United States: A system dynamics perspective

The objective of this study was to unravel the challenges confronting women of color (WoC)-owned small and medium-sized enterprises (SMEs) in the United States. This is based on findings that most WoC-owned SMEs fail within the first few years of establishment. The impact of the global financial crisis resulting from the COVID-19 pandemic on WoC-owned SMEs was also explored. System Dynamics (SD) is a computational modeling approach useful for understanding changes in a system over time and is applied in this study to illustrate WoC entrepreneurs' navigation through the startup and maturation of SMEs. The authors calibrated and validated the model with publicly available data. Findings revealed that more emphasis should be placed on failure reduction in the early years of establishment of these businesses. Also, there is the need for early intervention rather than focusing on the improvement of the successful business exit from the system. Results indicated that the creation of new businesses by WoC after the failure of existing businesses produced an increase in the number of failed enterprises. The authors assert that attention must be paid at the individual level through support to the entrepreneur. This study contributes to the extant literature by providing the first known SD model useful in depicting the SME system for WoC entrepreneurs in the US. The model serves as a potentially useful tool for informing effective policy making, education, and programmatic approaches to support the success of WoC entrepreneurs in the US.