Measuring the economic significance of mean-variance spanning

This paper investigates the economic significance of mean-variance spanning tests using three classical statistical tests in a unified framework. I show how to compute confidence intervals about the Sharpe ratios of tangent portfolios, the variance of return of minimum variance portfolios, as well as the certainty equivalent utility gains. I apply this statistical framework to the question of whether US investors should diversify internationally. The analysis suggests that a strong statistical rejection of the hypothesis that there is no improvement in the minimum variance portfolio’s standard deviation of return does not imply that there are no significant economic benefits to be made in terms of a substantial risk reduction. These results have important implications for empirical tests of mean-variance spanning as well as empirical assets pricing tests and minimum variance bounds on stochastic discount factors.     

Forecasting economic activity from yield curve factors

This paper provides clear-cut evidence that the slope and curvature factors of the term structure of interest rates (yield curve) contain more information about future changes in economic activity than the term spread itself, often used in the literature as a predictive regressor of economic activity. These two factors reflect different information about future economic activity, which is smoothed out by the term spread. The paper shows that the slope factor has predictive power on future economic activity over longer horizons ahead, and thus may be interpreted as reflecting future business cycle conditions. On the other hand, the curvature factor, which enters the term spread with opposite sign than the slope factor, has predictive power on shorter movements of future economic activity which may be associated with changes in the current stance of monetary policy. These results hold for a number of world developed economies.     

Forecasting economic activity with mixed frequency BVARs

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate large numbers of time series that are observed at different intervals into forecasts of economic activity. This paper benchmarks the performances of MF-BVARs for forecasting U.S. real gross domestic product growth against surveys of professional forecasters and documents the influences of certain specification choices. We find that a medium–large MF-BVAR provides an attractive alternative to surveys at the medium-term forecast horizons that are of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices influence its performance strongly, such as model size, prior selection mechanisms, and modeling in levels versus growth rates.     

Measuring differences in economic standard of living between immigrant communities in Italy

Measuring differences in the economic standard of living of between natives and other ethnic groups can inform us about the relative disadvantages and inequalities within Italian society. Despite the importance of this question, the measurement of this gap is not an easy task because, when using the usual design-based approach to survey sampling inference, the available micro-data lack sufficient sample size for the majority of immigrant communities needed to obtain reliable estimates. In this paper, we show that small area estimation (SAE) techniques can be applied in a fruitful way to avoid this issue. In particular, we use an approach based on M-quantile regression for estimating the economic standard of living in each community in Italy. Our findings highlight economic disparities between natives and other ethnic groups and suggest the need to adopt specific policies that target the most vulnerable immigrant communities and are designed to improve their economic standard of living.     

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables; and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators.     

The power of text-based indicators in forecasting Italian economic activity

Can we use newspaper articles to forecast economic activity? Our answer is yes; and, to this end, we propose a high-frequency Text-based Economic Sentiment Index (TESI) and a Text-based Economic Policy Uncertainty (TEPU) for Italy. Novel survey evidence regarding Italian firms and households supports the rationale behind studying text data for the purposes of forecasting. Such indices are extracted from approximately 1.5 million articles from 4 popular newspapers, using a novel Italian economic dictionary with valence shifters. The TESI and TEPU can be updated daily for the whole economy and for specific sectors or economic topics. To test the predictive power of our indicators, we propose two forecasting exercises. Firstly, we use Bayesian Model Averaging (BMA) techniques to show that our monthly text-based indicators greatly reduce the uncertainty surrounding the short-term predictions of the main macroeconomic aggregates, especially during recessions. Secondly, we employ these indices in a weekly GDP tracker, achieving sizeable gains in forecasting accuracy, both in normal and turbulent times.     

Stock returns and economic activity in mature and emerging markets

This paper empirically investigates the dynamic interdependencies between stock returns and economic activity in mature and emerging markets. The existence, kind and strength of potential uni-directional and/or bi-directional relations are examined, running from stock returns to future economic activity and/or from economic activity to future stock returns. A bivariate VAR(12) model is applied and Granger causality tests are performed. Monthly data covering the January 1991–December 2006 period are used. The existence of an empirical relationship, with forecasting ability, between stock returns and future economic activity is confirmed. The results are strongly differentiated between mature and emerging markets.     

Energy prices and aggregate economic activity: an interpretative survey

In this article, we survey the theory and evidence linking fluctuations in energy prices to those in aggregate economic activity. We then examine the implications of this research for both monetary policy and energy policy in response to oil price shocks. The currently available research seems to provide relatively reliable guidance for monetary policy. Because the precise channels through which oil price shocks affect economic activity are only partially known, however, research offers less guidance about how countries should design energy policy should cope with oil price shocks.     

Economic policy uncertainty effects for forecasting future real economic activity

Recently introduced measures for economic policy uncertainty (EPU), included in the data from 1997 to 2016, have a role in forecasting out-of-sample values for future real economic activity for both the euro area and UK economies. The inclusion of EPU measures, either for the US, the UK or for overall European economies, improves the forecasting ability of models based on standard financial market information, especially for the period before the 2008 global crisis. However, during and after the crisis period, the slope of the yield curve and excess stock market returns improves the out-of-sample forecast performance the most compared to an AR-benchmark model. Hence, the EPU information is important in times of normal business cycles, but might contain similar information components to financial market return variables during turbulent crisis periods in the financial markets and in the real economy.     

The impact of financial and economic conditions on aggregate merger activity

A model of aggregate merger activity is developed by integrating the literature on aggregate investment in fixed capital into a microfinance framework. Mergers are viewed as the result of firms capital budgeting processes, and two major categories of explanatory variables emerge: (1) cost of capital and related financial effects, and (2) output effects. Regressions, estimated to explain the number of large mining and manufacturing mergers over the sample period 1956–1978, provide evidence consistent with this view. In addition, the model explains the high level of merger activity during the conglomerate boom of 1967–9.     

Further evidence on financial information and economic activity forecasts in the United States

Our study provides substantially robust evidence for the predictive power of financial variables in forecasting the business cycle at a further step. We select several interesting and representative financial variables and reveal that they can predict significant information regarding future equity premiums as well as future macroeconomic activity, which are proxied by comprehensive fresh macroeconomic variables. The predictive power remains stable in out-of-sample estimations and can generate profits in an active market-timing trading strategy in excess of the historical mean forecast strategy. Cochrane provides one of the core interpretations for such forecasts in the theoretical asset pricing framework.     

Oil prices,economic activity and inflation: evidence for some Asian countries

In this paper we study the oil prices–macroeconomy relationship by means of studying the impact of oil price shocks on both economic activity and consumer price indexes for six Asian countries over the period 1975Q1–2002Q2. The results suggest that oil prices have a significant effect on both economic activity and price indexes, although the impact is limited to the short run and more significant when oil price shocks are defined in local currencies. Moreover, we find evidence of asymmetries in the oil prices–macroeconomy relationship for some of the Asian countries.     

The local impact of typhoons on economic activity in China: A view from outer space

We examine the impact of typhoons on local economic activity in coastal China. To capture potential damages from an individual typhoon we use historical typhoon track data in conjunction with a detailed wind-field model. We then combine our damage proxy with satellite derived nightlight intensity data to construct a panel data set that allows us to estimate the impact of typhoons at a spatially highly disaggregated level (approx. 1 km). Our results show that typhoons have a negative and significant, but short-term, impact on local activity – a typhoon that is estimated to destroy 50% of the property reduces local economic activity by 20% for that year. Over our period of analysis (1992–2010) total net economic losses are estimated to be in the region of $US 28.34 billion. To assess the damage risk from future typhoons we use simulated probability distributions of typhoon occurrence and intensity and combine these with our estimated effects. Results suggest that expected annual losses are likely to be around $US 0.54 billion.     

西部三大经济区城市经济增长收敛性研究

以塞尔指数衡量的我国西部三大经济区区域经济增长α收敛结果表明,西部大开发战略实施以来,该区域整体经济差异的主要构成为区域内经济差异,区域内差异与区域间差异之间存在着较大差距,而成渝经济区区域内经济差异所占整体经济差异的比重在三大经济区中最大。通过构建固定效应变系数条件收敛方程模型所进行的条件β收敛性分析进一步显示,在整体区域经济差异相对乐观的同时,区域核心城市经济增长多呈现发散状态,这将使该区域经济差异扩大。     

Assessing the economic knowledge and economic opinions of adults

This study investigates public knowledge of basic economics and public opinion on economic issues. The primary data sources are five national surveys, administered from 1992 to 1999, which contain a rich set of questions to conduct multiple tests and comparisons of the factors that affect economic knowledge and public opinion. As a whole, the results offer significantly stronger evidence of factors that influence knowledge and opinion than is possible from a study of a particular sample of adults using a single set of survey questions.The analysis proceeds in two ways following methods that were originally used with one of the five data sets, a 1992 survey of adults (Walstad, 1997). First, a regression model is specified and estimated with each data set to identify how personal characteristics, general education, course work in economics, income, and political party affect economic knowledge. Second, probit analysis is used to evaluate the effect of economic knowledge on public opinion on selected economic issues after controlling for the above variables. The results from the 1992 data serve as the baseline for comparing the findings across the other surveys.     

An analysis of the economic impact of north sea oil activity on the scottish economy in 1973

There was no production of crude oil and natural gas in 1973. However, exploration and preparatory production work were well underway. The paper examines the impact within Scotland in 1973 of the expenditure required to supply these activities. The data used for the study are those contained in or associated with the 1973 Scottish Input Output tables and the analysis uses input-output techniques. The pattern of crude oil consumption within the Scottish economy in 1973 is also examined.     

Brexit fears mask underlying pressures on investment

• ? Brexit uncertainty is seen as a likely culprit behind weakness in UK business investment. But structural shifts in the economy alongside issues relating to how investment is measured suggest that even without the uncertainty factor, a significant revival in measured investment spending may not materialise.
• ? Movements in relative prices continue to favour expansion by relatively labour‐intensive rather than capital‐intensive sectors. Relatedly, a shift towards investment‐light services activity is persisting, while recent changes in the make‐up of employment are reducing the extent of likely automation.
• ? Moreover, the ongoing pace of technological change and corresponding fears of rapid obsolescence could deter investment. And an increasingly fuzzy division between investment and consumer spending suggests that official data may be increasingly understating investment's true level.

     

Analyzing the underlying dimensions of firm profitability

In this paper we decompose a traditional measure for firm's performance, return on sales, into four components that capture the impact of productivity, price recovery, product mix and capacity utilization, respectively, on a firm's profitability. The new measures are used as an illustration to explain changes in the performance of firms in the US telecommunications industry following deregulation. Changes in the overall profitability margin of these firms are explained by substantial but offsetting changes in their productivity, price recovery ability, product-mix maximization and capacity utilization, that have occurred as a consequence of deregulation. The new measures enable us not only to illustrate relative differences between firms in a given cross-section but also to shed light on how changes take place over time in the different components that underlie firms' profitability.     

Six Sigma: Definition and underlying theory

Six Sigma has been gaining momentum in industry; however, academics have conducted little research on this emerging phenomenon. Understanding Six Sigma first requires providing a conceptual definition and identifying an underlying theory. In this paper we use the grounded theory approach and the scant literature available to propose an initial definition and theory of Six Sigma. Our research argues that although the tools and techniques in Six Sigma are strikingly similar to prior approaches to quality management, it provides an organizational structure not previously seen. This emergent structure for quality management helps organizations more rigorously control process improvement activities, while at the same time creating a context that enables problem exploration between disparate organizational members. Although Six Sigma provides benefits over prior approaches to quality management, it also creates new challenges for researchers and practitioners.