Monetary policy shocks: We got news!

We assess the role of monetary policy news shocks in the context of a medium scale DSGE model estimated on US data. We estimate several versions of the model and find decisive evidence in favour of the inclusion of monetary policy news shocks over a two-quarter horizon. According to our results, monetary policy news shocks account for a non-negligible fraction of the variance of real variables, especially at shorter forecast horizons. Further, we document that the importance of monetary policy news shocks goes beyond what was observed in recent years. The historical importance of monetary policy news shocks dates back to the 1999–2006 period when the official FOMC statements provided information about both the current policy setting and the expected future policy path. We also show that adding monetary policy news shocks to the model does not lead to identification problems.     

The interplay between the Bayesian and frequentist approaches: a general nesting spatial panel data model

The interplay between the Bayesian and Frequentist approaches: a general nesting spatial panel-data model. Spatial Economic Analysis. An econometric framework mixing the Frequentist and Bayesian approaches is proposed in order to estimate a general nesting spatial model. First, it avoids specific dependency structures between unobserved heterogeneity and regressors, which improves mixing properties of Markov chain Monte Carlo (MCMC) procedures in the presence of unobserved heterogeneity. Second, it allows model selection based on a strong statistical framework, characteristics that are not easily introduced using a Frequentist approach. We perform some simulation exercises, finding good performance of the properties of our approach, and apply the methodology to analyse the relation between productivity and public investment in the United States.     

Vacant technology forecasting using new Bayesian patent clustering

Vacant technology forecasting (VTF) is a technology forecasting approach to find technological needs for given industrial field in the future. It is important to know the future trend of developing technology for the R&D planning of a company and a country. In this paper, we propose a new Bayesian model for patent clustering. This is a VTF methodology based on patent data analysis. Our method is composed of Bayesian learning and ensemble method to construct the VTF model. To illustrate the practical way of the proposed methodology, we perform a case study of given technology domain using retrieved patent documents from patent databases in the world.     

中国通货膨胀率与其不确定性之间的非线性动态关系

通货膨胀率和通货膨胀不确定性之间相互影响,二者之间的内在关系对政府运用货币政策具有重要的现实意义。本文采用1990年1月以来居民消费价格指数(CPI)的月度数据,结合随机域回归模型、系列随机域的非线性检验方法、最大似然估计及贝叶斯估计方法,对我国通货膨胀率与通货膨胀不确定性的关系进行了实证分析。实证研究发现,通货膨胀率引起了通货膨胀不确定性,两者呈现非线性的U型关系,支持了Friedman假说;大的通货膨胀不确性引起通货膨胀率先升后降,呈现非线性倒U曲线关系,Cukierman-Meltzer假说在U型左侧范围内成立,于U型右侧Holland结论成立。     

The political market for immigration restrictions: Model and test

This paper addresses the important question of whether public investment spending on economic infrastructure enhances economic growth in Mexico. It estimates a Cobb-Douglas production function that includes public infrastructure capital. Using cointegration analysis, the paper estimates a vector error correction model (VECM) for the 1995?–?99 period. The results suggest that there is a long-term stable relationship among the variables included in the VECM. The evidence also indicates that both public infrastructure spending and private capital formation have a positive and highly significant effect on the rate of output growth. Finally, the impulse response functions (IRF) and the variance decompositions (VDC) of the endogenous variables in the VECM suggest that the response of private capital to public infrastructure is positive while the reverse causation is not affirmed. From a policy standpoint, the findings call into question stabilization policies that disproportionately reduce public infrastructure to meet targeted reductions in the fiscal deficit (JEL, O1, O47, O54).     

Diversification by entry into a new submarket?

Using a dataset on the subsector and geographical segmentation of 208 Pharmaceutical companies, a Bayesian panel probit is used to analyze the role of state dependency, size and achieved diversification in affecting entry decision. We properly account for unobservable heterogeneity in a context with nonstrictly exogenous regressors. We find that achieved diversification, measured by the number of submarkets already entered affects negatively the probability of entry. Beside some country-specific exceptions, size and the lagged dependent variable do not seem to be relevant.     

Bayesian clustering with priors on partitions

Traditional clustering algorithms are deterministic in the sense that a given dataset always leads to the same output partition. This article modifies traditional clustering algorithms whereby data are associated with a probability model, and clustering is carried out on the stochastic model parameters rather than the data. This is done in a principled way using a Bayesian approach which allows the assignment of posterior probabilities to output partitions. In addition, the approach incorporates prior knowledge of the output partitions using Bayesian melding. The methodology is applied to two substantive problems: (i) a question of stylometry involving a simulated dataset and (ii) the assessment of potential champions of the 2010 FIFA World Cup.     

Microenvironment-specific effects in the application credit scoring model

This paper introduces the improved version of a credit scoring model which assesses credit worthiness of applicants for a loan. The scorecard has a two-level multilevel structure which nests applicants for a loan within microenvironments. In addition, the paper proposes a new type of clustering for a hierarchical two-level structure which is more intuitive and efficient in the application to credit scoring. This structure explores living area-specific effects which are viewed as unobserved determinants of default. The primary benefit of the multilevel scorecard compared to a conventional scoring model is higher accuracy of the model predictions.     

Financial Convergence in Transition Economies: EU Enlargement

This paper analyzes the financial effect of the enlargement of the European Union (EU) to include ten new Central and East European countries (CEECs) on firms' business and financial structures. We employ quantitative analytic techniques and financial ratios to discover whether firms in the new EU member states tend to converge with businesses in the EU-15 in terms of the structure of their financial statements. We examine the extent to which the increasing integration of the former may foster the convergence of productive structures. We analyze the evolution of twelve financial ratios in a sample of firms obtained from the AMADEUS database, performing a dynamic factor analysis that identifies the determining factors of the joint evolution of deviations in financial ratios from the average values of EU-15 firms. This allows us to analyze the convergence in each of the CEECs toward the EU-15.     

The Economics of American Higher Education,ed., William E. Becker and Darrell R. Lewis

The author describes a protocol for classroom experiments for courses that introduce undergraduates to signaling games. Signaling games are conceptually difficult because, when analyzing the game, students are not naturally inclined to think in probabilistic, Bayesian terms. The experimental design explicitly presents the posterior frequencies of the unobserved events. The protocol's emphasis on the posterior enhances convergence to the equilibrium prediction, relative to a treatment in which posterior frequencies are not explicitly computed. This convergence reinforces the development of the theory in subsequent lecture periods.