Debt stabilization games in the presence of risk premia

As a result of the recent financial crisis and the ensuing economic recession, fiscal deficits have soared in many OECD countries. As a consequence, government debt has been on the rise again after a period of stable or declining government debt. In this paper we analyze debt stabilization in a country that features endogenous risk premia, imposed by financial markets that evaluate the probability of debt default by governments. Endogenous risk premia arise by assuming, e.g., simple linear relations between risk premia and the level of debt. As a result the real interest rate on government debt can be written as a constant (measuring the risk-free real interest rate corrected for real output growth) plus an endogenous risk premium that depends on the debt level. We bring such an endogenous risk premium into Tabellini (1986) model and analyze the impact of it. This gives rise to a non-linear differential game. We solve this game for both a cooperative setting and a non-cooperative setting. The non-cooperative game is solved under an open-loop information structure. We present a bifurcation analysis w.r.t. the risk premium parameter.     

Explaining movements in UK stock prices

We examine movements in aggregate UK stock prices by decomposing the variance of unexpected real stock returns into components due to revisions in expectations of future dividends, discount rates, and the covariance between the two. The contribution of news about future discount rates is about four times that of news about future dividends, with no significant covariance between them. Our analysis of excess returns uncovers a positive covariance between news about dividends and news about real interest rates. Since these two elements have opposite effects on current stock prices, their combined effect is negligible. Persistence in expected returns, as well as predictability, are found to be important in explaining stock price movements.     

Fiscal news and macroeconomic volatility

This paper analyzes the contribution of anticipated capital and labor tax shocks to business cycle volatility in an estimated New Keynesian business cycle model. While fiscal policy accounts for about 15% of output variance at business cycle frequencies, this mostly derives from anticipated government spending shocks. Tax shocks, both anticipated and unanticipated, contribute little to the fluctuations of real variables. However, anticipated capital tax shocks do explain a sizable part of inflation fluctuations, accounting for up to 12% of its variance. In line with earlier studies, news shocks in total account for about 50% of output variance. Further decomposing this news effect, we find permanent total factor productivity news shocks to be most important. When looking at the federal level instead of total government, the importance of anticipated tax and spending shocks significantly increases, suggesting that fiscal policy at the subnational level typically counteracts the effects of federal fiscal policy shocks.     

REAL‐TIME DATA AND FISCAL POLICY ANALYSIS: A SURVEY OF THE LITERATURE

This paper surveys the empirical research on fiscal policy analysis based on real‐time data. This literature can be broadly divided into four groups that focus on: (1) the statistical properties of revisions in fiscal data; (2) the political and institutional determinants of projection errors by governments; (3) the reaction of fiscal policies to the business cycle and (4) the use of real‐time fiscal data in structural vector autoregression (VAR) models. It emerges that, first, fiscal revisions are large and initial releases are biased estimates of final values. Secondly, strong fiscal rules and institutions lead to more accurate releases of fiscal data and smaller deviations of fiscal outcomes from government plans. Thirdly, the cyclical stance of fiscal policies is estimated to be more ‘counter‐cyclical’ when real‐time data are used instead of ex post data. Fourthly, real‐time data can be useful for the identification of fiscal shocks. Finally, it is shown that existing real‐time fiscal data sets cover only a limited number of countries and variables. For example, real‐time data for developing countries are generally unavailable. In addition, real‐time data on European countries are often missing, especially with respect to government revenues and expenditures. Therefore, more work is needed in this field.     

中国投资、储蓄与消费之间关系的实证研究

中国经济要保持持续的高速发展,必须平衡好投资、储蓄与消费之间的关系。本文通过Solow黄金储蓄率的测算,发现1995年后中国的储蓄率居高不下。通过Chow Test对消费和GDP之间关系的检验,可以把1981～2008年中国经济分为两个阶段。通过对比发现1995年后,不论是消费倾向,或者是消费的GDP弹性都有较大降幅,而且消费开始不能拉动GDP增长。此外,本文分析了居民储蓄、企业储蓄和政府投资对于经济增长的作用变化,发现住房储蓄促进经济的作用有限,而通过减税提高企业储蓄能够提升经济发展潜力。     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

论政府在房地产市场诚信体系建设中的责任

目前,在我国房地产市场快速发展的同时,房地产市场诚信体系却存在严重扭曲,已经成为制约房地产市场健康发展的严重障碍。树立政府行政诚信是政府在房地产市场诚信体系建设中的首要责任。政府应履行的基本责任是:务必坚持科学的引导和规划,夯实房地产市场诚信体系建设的基础;务必坚持严格的管理和规范,强化房地产市场诚信体系建设的主体;务必坚持有效的监督与控制,保障房地产市场诚信体系建设的秩序,以期真正建立自律和他律有机结合,并能为市场各主体接受且自觉遵循的房地产市场诚信体系。     

房地产市场非理性运势形成机理及其破难建制研究

自1998年停止福利分房、推行住房商品化以来,中国房地产市场全面复苏。近10年来,在连续多年保持高速增长的发展态势下,全国各城市地价、房价均出现大幅上涨,尽管从2005年开始,政府不断进行宏观调控,但仍然难抑上涨趋势。本文以杭州为例,运用房地产市场正反馈交易理论探讨城市房地产市场非理性运势形成机理及其破难建制,为政府宏观调控房地产市场提供科学依据。     

地方政府执行房地产调控政策分析

近年来,我国政府针对房价过高问题,陆续出台了一系列调控政策,但是执行效果并不理想。从地方政府行为动机角度出发,运用博弈分析方法,认为调控政策的有效执行受到外部损失、房地产行业税收贡献率、相应调控政策执行成本及对房地产开发商超额利润的惩戒力度等四个因素制约。中央政府对地方政府的政绩考核应以经济为主要指标,以房价调控绩效为辅助指标,调动地方政府执行政策积极性。只有这样,才能从根本上解决房地产调控政策制定与执行不统一的问题,达到对房地产市场有效调控的目标。     

Robust growth-equity decomposition of change in poverty: The case of Iran (2000–2009)

This paper examines a robust nonparametric methodology for decomposition of change in poverty into growth and redistribution components. The decomposition is exact, symmetric and free of residual terms. It is equivalent to the Shapley value decomposition in this two-component case. We avoid parametric assumptions about the underlying distributions and Lorenz functions. All of the currently popular poverty measures may be decomposed as suggested in this paper. We identify the issues that arise with parametric approaches to decomposition. An empirical application is given based on recent data on real consumption in rural and urban areas of Iran in 2000, 2004 and 2009 (covering the country's third and fourth five-year development plans). We find that both ‘pure growth’ and ‘redistribution’ components are present in a striking change in poverty, especially among rural households. It would appear that stochastic dominance rankings of the consumption distributions make poverty analyses and decompositions robust to the choice of a poverty line, or poverty measure.     

政府债务对经济增长的影响及作用渠道

基于1960～2010年113个国家的面板数据集,运用系统广义矩动态面板方法和稳健性分析,比较发达国家和发展中国家政府债务经济增长效应的差异,并尝试分析政府债务影响经济增长的渠道。研究结果显示,政府债务对经济增长有非线性影响;发达国家政府债务对经济增长、投资以及全要素生产率均无显著影响;发展中国家对政府债务的直接承受力更弱,但在一个宽松的临界点内,政府债务的增加可以提高投资率。     

Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors

Although globalization has shaped the world economy in recent decades, emerging economies have experienced impressive growth compared to developed economies, suggesting specific comovements within developed and emerging business cycles. Using observed developed and emerging real economy activity variables, we investigate whether the latter assertion can be supported by observed data. Based on a two-level factor model, we assume these activity variables can be decomposed into global components, emerging or developed common components, and idiosyncratic national shocks. We propose a statistical test for the null hypothesis of a one-level specification, where it is irrelevant to distinguish between emerging and developed latent factors against the two-level alternative. This paper provides a theoretical justification and Monte Carlo simulations that document the testing procedure. An application of the test to various data sets of developed and emerging countries leads to strong statistical evidence of specific comovements within these two groups.     

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989-2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.     

Success factors of university-spin-offs: Regional government support programs versus regional environment

In recent years entrepreneurship research has increasingly interpreted new firm emergence in the light of the context the potential or real founder is living and working in. This is especially true for university spin-offs, a type of new firms that gives rise to great hopes for policymakers and technology transfer institutions. The aim of this paper is to analyze what is more influential: specific characteristics of the regional environment of the spin-off founder or public programs to support university spin-offs. Based upon a unique data set covering 11 years of data collection we were able to apply a control group approach with two different government support programs in two regional contexts. The results based upon ordinal regressions suggest that the regional context in which an individual starts a firm, has an impact on start-up success, but the fact that he/she had received government support has a lesser impact. To summarize: site specific factors matter, government support programs per se do not.     

Sources of real exchange rate movements in Saudi Arabia

The paper investigates the sources of real exchange rate movements in Saudi Arabia by decomposing real exchange rate movements into those attributable to real and nominal shocks. Using a popular structural VAR model and assuming long-run neutrality of nominal shocks, we find that real shocks play a significant role in explaining real exchange rate movements in Saudi Arabia. Using a more disaggregated model, we also find that oil production shocks rather than real oil price shocks are responsible for real exchange rate movements. In order to stabilize the real exchange rate, Saudi Arabia should focus on stabilizing oil production.(JEL F3, C5)     

POLITICAL PROCESS AND THE FORMATION OF ECONOMIC POLICY

Institutional economics has some useful things to say about government policy-forming processes. It focuses attention on the structural components of government institutions which shape the direction of policy formation and its delivery. In this article, the author discusses how institutional models of government decision-making may be adapted to parliamentary systems of government. This approach has some merit because it rescues analysis of government processes from models based on the US constitutional system.     

论政府在房地产市场诚信体系建设中的责任

目前，在我国房地产市场快速发展的同时，房地产市场诚信体系却存在严重扭曲，已经成为制约房地产市场健康发展的严重障碍。树立政府行政诚信是政府在房地产市场诚信体系建设中的首要责任。政府应履行的基本责任是：务必坚持科学的引导和规划，夯实房地产市场诚信体系建设的基础；务必坚持严格的管理和规范，强化房地产市场诚信体系建设的主体；务必坚持有效的监督与控制，保障房地产市场诚信体系建设的秩序，以期真正建立“自律”和“他律”有机结合，并能为市场各主体接受且自觉遵循的房地产市场诚信体系。     

Idiot's Bayes—Not So Stupid After All?

Folklore has it that a very simple supervised classification rule, based on the typically false assumption that the predictor variables are independent, can be highly effective, and often more effective than sophisticated rules. We examine the evidence for this, both empirical, as observed in real data applications, and theoretical, summarising explanations for why this simple rule might be effective.     

环境的外在性和保护策略

一个简单的环境污染模型发展后,环境污染具有外部性,在这个内生增长模型中,考虑了通过税收提供的资本来保护环境的两条途径。在政府不参与环境控制时,经济增长率最高,但污染也最严重,社会福利要比政府采取补贴情形时底。当环境外部性较少时,环境保护采取政府补贴社会福利要比完全由政府控制时的福利要大,而当外部性较大时,则环境保护应当采取完全由政府控制为妙。     

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.