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This study evaluates the Federal Reserve (Fed) initial and final forecasts of the unemployment rate for 1983Q1-2018Q4. The Fed initial forecasts in a typical quarter are made in the first month (or immediately after), and the final forecasts are made in the third month of the quarter. The analysis also includes the private forecasts, which are made close to the end of the second month of the quarter.

In evaluating the multi-period forecasts, the study tests for systematic bias, directional accuracy, symmetric loss, equal forecast accuracy, encompassing and orthogonality. For every test equation, it employs the Newey–West procedure in order to obtain the standard errors corrected for both heteroscedasticity and inherent serial correlation.

Both Fed and private forecasts beat the naïve benchmark and predict directional change under symmetric loss. Fed final forecasts are more accurate than initial forecasts, meaning that predictive accuracy improves as more information becomes available. The private and Fed final forecasts contain distinct predictive information, but the latter produces significantly lower mean squared errors. The results are mixed when the study compares the private with the Fed initial forecasts. Additional results indicate that Fed (private) forecast errors are (are not) orthogonal to changes in consumer expectations about future unemployment. As such, consumer expectations can potentially help improve the accuracy of private forecasts.

Unlike many other studies, this study focuses on the unemployment rate, since it is an important indicator of the social cost of business cycles, and thus its forecasts are of special interest to policymakers, politicians and social scientists. Accurate unemployment rate forecasts, in particular, are essential for policymakers to design an optimal macroeconomic policy.

This article analyzes the determinants of credit constraints and their effects on the productivity of micro-firms in Colombia.

An Endogenous Switching Regression Model (ESRM) is estimated to analyze credit constraint impact on economic performance.

The results show that owner characteristics such as age and gender decrease the likelihood of being constrained. Firms' characteristics, such as legal status, the formality of the employees, commercial property and savings, are important for reducing credit constraints.

This article discusses how formal credit restrictions harm the economic performance of Colombia's micro-firms. The results show that the productivity of the micro firms in Colombia could increase, on average, by U$ 825 USD when all types of restrictions are eliminated.

This paper examines whether farmers' knowledge of the minimum support prices (MSPs) affects farm-gate prices. MSP is the minimum guaranteed price for agricultural commodities announced by the Government of India for 24 commodities. Most farmers in India prefer to sell their produce at the farm-gate due to a small marketable surplus and hence do not directly benefit from MSP. The authors test the common argument in the political discourse that if farmers have knowledge of MSP, then they can bargain with traders during the farm-gate transaction and demand a better price close to MSP.

The authors use matching methods to examine the impact of knowledge of MSP on farm-gate prices.

Using nationally representative data, the authors show that there is no empirical evidence that the knowledge of MSP of the crops leads to higher bargaining power and better farm-gate prices.

Price information (MSP in this case) alone cannot improve the bargaining power of farmers and result in a better price realization. As a safety net, MSP fails in the absence of procurement of products by the government. This also raises the question of the equitability of the price support system in India and calls for a rethink of the MSP policy.

This study is the first of its kind to examine the anchoring effect of knowledge of MSP on farm-gate prices using a nationally representative dataset.

This empirical study uses herd behavior model to explore the role of anchor investors in ensuring fundraising success and overfunding of crowdfunded ventures.

Qualitative comparative analysis (QCA) is applied to find the configurational patterns describing how anchor investors' information disclosure leads to successful financing and overfunding.

Even when the anchor investor's resume is not detailed or the anchor investor has little experience in entrepreneurial investment, success or overfunding can be achieved, provided the anchor investor is a corporation rather than an individual. For individual anchor investors, a detailed resume matters. Overfunding can be achieved even when an individual anchor investor makes a small relative investment, if this small relative investment is compensated for by a detailed resume. Experience in entrepreneurial investment is crucial when individual anchor investors have few previous investments. Regardless of the anchor investor's identity, investment in absolute terms is crucial for crowdfunding success when experience in entrepreneurial investment is low. Such experience must be extensive if the anchor investor's resume is not detailed.

Both entrepreneurs and crowdfunding platforms can benefit from the findings in relation to the design of campaigns that use anchor investors' informational cues to achieve success and overfunding.

The study examines the importance of anchor investors' information disclosure in digital crowdfunding environments, differentiating between individual and corporate anchor investors.

本實證研究使用羊群行為模型, 去探究錨定投資者在確保眾籌活動可達成功籌資以及過多籌資方面所扮演的角色。

研究人員以定性比較分析法、去找出描述錨定投資者的資訊公佈如何帶來成功融資和過多籌資的配置模式。

研究結果顯示、只要錨定投資者不是個人、而是一間公司, 則即使他們的履歷不詳盡, 又或他們對企業投資的經驗淺薄, 也無礙籌資或過多籌資的成功完成。如錨定投資者為個人, 則詳盡的履歷會影響甚鉅。即使個人錨定投資者相對而言參與少量的投資, 但若這少量的投資給他們詳盡的履歷所彌補的話, 則過多籌資仍可成功達到。若個別錨定投資者原有的投資量不多的話, 則企業投資的經驗至為重要。不管錨定投資者的身份是什麼, 若他們對企業投資所持的經驗淺薄, 則按絕對值計算的投資額對眾籌能否成功至為重要。若錨定投資者的履歷不詳盡, 則這種經驗必須是豐富廣泛的。

本研究區分了個人錨定投資者與公司錨定投資者兩者對眾籌的影響, 就此而研究在數碼的眾籌環境裡, 錨定投資者信息公佈的重要性。

研究結果可幫助企業家和群眾募資平台去設計可使用錨定投資者的資訊提示來達至成功眾籌和過多籌資的活動。

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.

This study aims to investigate a startup accelerator’s decisions toward exerting effort in an information acquisition process and selecting an information disclosure strategy. In particular, the authors are interested in examining which factors may cause the accelerator to report more or less accurate information, which will subsequently affect the investment decision and the outcome of the ventures. This study examines the impact of the equity share taken by the accelerator on the effort level being exerted in the information acquisition process, as well as the accelerator’s decision on the information disclosure regime.

The authors use mathematical models built upon well-established theoretical and practical concepts to analyze the research problems and derive the findings.

The authors show that when the accelerator takes a sufficiently large equity share from the entrepreneur in exchange for admitting the entrepreneur’s venture into the acceleration program, the accelerator is motivated to exert a significant level of effort to observe an accurate signal for the quality of the venture, and then disclose the information about the venture’s quality consistently with the observed signal (informative disclosure regime). On the other hand, if the accelerator takes a small equity share, it is optimal for her to exert no effort in the information acquisition process and simply adopt the basic disclosure regime, where the accelerator reports the quality of the venture based solely on the ex ante expected payoff of the venture, regardless of the observed signal.

The results indicate that an equity sharing scheme, which awards a sufficient amount of equity to the accelerator, can be an effective tool to help obtain accurate information about the quality of a startup venture and make a well-informed investment decision.

This research illustrates that the ownership stake of the accelerator can potentially indicate the accuracy of the information about the venture provided by the accelerator to outside investors. That is, when the stake held by the accelerator is large, the investors can conjecture that the information about the venture reported by the accelerator may be highly accurate and reliable. In contrast, if the accelerator holds a small stake, then it is likely that the information provided by the accelerator may not add any value to the publicly available information. These insights can guide investors (e.g. angle investors, venture capitalists, etc.) in making well-informed startup investment decisions.