Forecasting economic and financial variables with global VARs

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.     

Investment incentives and the demand for labor in U.S. regions

Most studies of federal investment incentives are limited to those policies' effects on capital formation. This paper departs from this tradition by examining the employment consequences, in general, and by industry and metropolitan area. In particular, it asks whether the accelerated depreciation allowance and investment tax credit: (1) generally displace more jobs (via the substitution effect) than they create (via the scale effect), (2) affect the overall employment situation in some metropolitan areas more than in others, and (3) help reallocate jobs across industries and metropolitan areas. Using data from Los Angeles and Philadelphia in two production models, the paper concludes that each of these outcomes is likely, though (2) depends on metropolitan areas' particular mixes of industries.     

美国调节区域经济差异的财政措施及启示

区域经济发展不平衡是当今世界普遍存在的问题,也是各国政府宏观调控的一个重要目标.美国是当代经济最发达的资本主义国家,曾经也是一个地区经济发展很不平衡的国家.区域经济发展的不平衡,使美国的商品市场和资本市场一度步入沉闷的境地,影响了美国经济的整体发展和社会的全面进步.     

DEA of financial statements data: The U.S. computer industry

DEA (data envelopment analysis) is a technique for determining the efficiencyfrontier (the envelope) to the inputs and outputs of a collection of individual corporations or other productive units. DEA is here employed to estimate the intertemporal productive efficiency of U.S. computer manufactures, using financial data brought from earnings statements and balance sheets. The results indicate that a few corporations, including Apple Computer Inc., Compaq Computer Corp., and Seagate Technology were able to stay at the productivity efficiency frontier throughout the time period investigated. But not all successful corporations did; sometimes subefficiency (=disequilibrium) actually goes together with very rapid growth. A new Malmquist type productivity index is calculated for each corporation, measuring shifts of the estimated intertemporal efficiency frontier.     

A revisit to economic exposure of U.S. multinational corporations

To shed light on the influence of U.S. major trade partners’ currencies on MNCs’ firm values, this study investigates the asymmetric effects and the determinants of appreciated and depreciated economic exposure of the U.S. MNCs. Our empirical results reveal several findings: (1) The influences of exchange rate fluctuation on stock returns vary enormously for different currencies. (2) During the U.S. dollar appreciating period, MNCs benefit very little from this appreciation against major trade partners’ currencies, but most MNCs see harmful impacts from a U.S. dollar appreciation against the Brazilian real. (3) During the U.S. dollar depreciating period, most U.S. MNCs benefit from this depreciation against the European Monetary Union’s euro, Mexican new peso and Brazilian real; however, they overall suffer losses against the Chinese yuan, Japanese yen, and British pound. (4) The level of foreign sales is the key determinant of economic exposure.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Alternative econometric implementations of multi-factor models of the U.S. financial markets

This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach introduced by Fama and MacBeth (1973). The second approach is based on a Bayesian latent mixture model with breaks in risk exposures and idiosyncratic volatility. Our application to monthly, 1980–2010 U.S. data on stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window estimation of time-varying betas yields results that are unreasonable. There is evidence that most portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results and a few factor risk premia are precisely estimated with a plausible sign. Predictive log-likelihood scores indicate that discrete breaks in both risk exposures and variances are required to fit the data.     

Understanding the accumulation of bank and thrift reserves during the U.S. financial crisis

The level of aggregate excess reserves held by U.S. depository institutions increased significantly at the peak of the 2007–2009 financial crisis. Although the amount of aggregate reserves is determined almost entirely by the policy initiatives of the central bank that act on the asset side of its balance sheet, the motivations of individual banks in accumulating reserves differ and respond to the impact of changes in the economic environment on individual institutions. We undertake a systematic analysis of this massive accumulation of excess reserves using bank-level data for more than 7000 commercial banks and almost 1000 savings institutions during the U.S. financial crisis. We propose a testable stochastic model of reserves determination when interest is paid on reserves, which we estimate using bank-level data and censored regression methods. We find evidence primarily of a precautionary motive for reserves accumulation with some notable heterogeneity in the response of reserves accumulation to external and internal factors of the largest banks compared with smaller banks. We combine propensity score matching and a difference-in-differences approach to determine whether the beneficiaries of the Capital Purchase Program of the Troubled Asset Relief Program accumulated less cash, including reserves, than non-beneficiaries. Contrary to anecdotal evidence, we find that banks that participated in the program accumulated less cash, including reserves, than nonparticipants in the initial quarters after the capital injection.     

Product diversification,production systems,and economic performance in U.S. agricultural production

The U.S. agricultural sector is experiencing significant structural change. Farm size is rising and activities are broadening, including more off-farm employment, implying economic incentives for larger and more diversified farms, and complementarities among agricultural netputs. We quantify such patterns for farms in the corn belt, by measuring scale economies, and output and input contributions and jointness. We estimate the multi-output and -input production technology by stochastic frontier techniques applied to output and input distance functions. We find that both scope and scale economies have important economic performance implications, and that an input-oriented framework including off-farm income best characterizes agricultural production.     

Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

This paper develops a testing framework for comparing the predictive accuracy of competing multivariate density forecasts with different predictive copulas, focusing on specific parts of the copula support. The tests are framed in the context of the Kullback–Leibler Information Criterion, using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties for realistic sample sizes. In an empirical application to daily changes of yields on government bonds of the G7 countries we obtain insights into why the Student-t and Clayton mixture copula outperforms the other copulas considered; mixing in the Clayton copula with the t-copula is of particular importance to obtain high forecast accuracy in periods of jointly falling yields.     

Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market

This paper studies the asymmetric spillover effect of important economic policy uncertainty (EPU) on the S&P500 index. We use monthly EPU indexes from Australia, Canada, China, Japan, the U.K. and the U.S. and the realized volatility of the U.S. stock market to study the asymmetric pairwise directional spillovers on the U.S. stock market from 2000 to 2019. We find that S&P500 index volatility is a net recipient of spillovers from important EPU indexes. Japanese EPU has the strongest spillover effect on the U.S. stock markets, while EPU from the U.K. plays a very limited role. By decomposing the volatility into good and bad volatility, we find that the relationship between bad stock market volatility and EPU is stronger than between good volatility and EPU. Time-varying spillover characteristics show that bad volatility reacts more strongly to shocks in EPU following the debt crisis and trade negotiations. Several robustness checks are provided to verify the novelty of these findings.     

基于灰关联分析的中美GDP与社会物流成本比较研究

从中美两国社会物流成本结构入手,应用灰关联分析方法分析了1996-2010年中美两国物流总成本变化趋势,GDP与社会物流成本构成成分之间的关联程度、差异性及其成因,并提出了降低我国社会物流成本的对策建议。     

Exploiting linear partial information for optimal use of forecasts: With an application to U.S. economic policy

Traditionally, the link between forecasting and decision making rests on the assumption of a known distribution for the future values of predicted variables. In practice, however, forecasts tend to offer little more than Linear Partial Information (LPI), typically of the form, ‘State 1 is more likely to prevail than state 2, and state 2 more likely to prevail than state 3, among five possible states’. This paper shows how such fuzzy LPI statements can be exploited in decision making. For an illustration, LPI analysis is used for determining (ex post) the optimal economic policy to be followed by the Carter Administration with a view to ensuring reelection in 1980. An optimal adaption of that policy occasioned by the fallible 1980 forecasts made by the Council of Economic Advisors is also derived.