The relationship between forecast dispersion and forecast uncertainty: Evidence from a survey data—arch model

This paper examines empirically the relationship between measures of forecast dispersion and forecast uncertainty from data on inflation expectations from the Livingston survey series and the Survey Research Center (SRC) survey series. Because the survey series do not provide probabilistic forecasts of inflation, we derive measures of inflation uncertainty by modelling the conditional variance of the inflation forecast errors from the survey series as an autoregressive conditional heteroscedastic (ARCH) process. The analysis is complicated by the fact that the overlap of forecast horizons for the survey series does not preclude the model's disturbance terms from displaying autocorrelation, and also places a restriction on the specification for the ARCH measures of inflation uncertainty. We estimate the model using Hansen's (1982) generalized method of moments (GMM) procedure to account for the presence of serial correlation and conditional heteroscedasticity in the disturbance terms. The results generally support the hypothesis that the measures of forecast dispersion across survey respondents are positively and statistically significantly associated with the measures of inflation uncertainty. However, the appropriateness of using forecast dispersion measures as proxies for inflation uncertainty is sensitive to the choice of the survey series.     

Macroeconomic forecasting in Austria: An analysis of accuracy

In the present paper, we attempt a critical evaluation of macroeconomic forecasting in Austria. For this purpose, we calculate conventional magnitude measures of accuracy as well as probabilities of correctly predicting directional change for the forecasts made by two Austrian institutions (WIFO and IHS) and by the OECD. ARIMA models and Holt-Winters exponential smoothing serve as benchmarks for comparison.     

Macroeconomic Forecasting using Low‐frequency Filters

We consider univariate low‐frequency filters applicable in real‐time as a macroeconomic forecasting method. This amounts to targeting only low frequency fluctuations of the time series of interest. We show through simulations that such approach is warranted and, using US data, we confirm empirically that consistent gains in forecast accuracy can be obtained in comparison with a variety of other methods. There is an inherent arbitrariness in the choice of the cut‐off defining low and high frequencies, which calls for a careful characterization of the implied optimal (for forecasting) degree of smoothing of the key macroeconomic indicators we analyse. We document interesting patterns that emerge: for most variables the optimal choice amounts to disregarding fluctuations well below the standard business cycle cut‐off of 32 quarters while generally increasing with the forecast horizon; for inflation and variables related to housing this cut‐off lies around 32 quarters for all horizons, which is below the optimal level for federal government spending.     

A new approach for detecting shifts in forecast accuracy

Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. However, commonly-used statistical procedures implicitly place a lot of weight on type I errors (or false positives), which results in a relatively low power of the tests to identify forecast breakdowns in small samples. We develop a procedure which aims to capture the policy cost of missing a break. We use data-based rules to find the test size that optimally trades off the costs associated with false positives with those that can result from a break going undetected for too long. In so doing, we also explicitly study forecast errors as a multivariate system. The covariance between forecast errors for different series, although often overlooked in the forecasting literature, not only enables us to consider testing in a multivariate setting, but also increases the test power. As a result, we can tailor our choice of the critical values for each series not only to the in-sample properties of each series, but also to the way in which the series of forecast errors covary.     

Forecasting Euro‐Area Macroeconomic Variables Using a Factor Model Approach for Backdating

We suggest to use a factor model based backdating procedure to construct historical Euro‐area macroeconomic time series data for the pre‐Euro period. We argue that this is a useful alternative to standard contemporaneous aggregation methods. The article investigates for a number of Euro‐area variables whether forecasts based on the factor‐backdated data are more precise than those obtained with standard area‐wide data. A recursive pseudo‐out‐of‐sample forecasting experiment using quarterly data is conducted. Our results suggest that some key variables (e.g. real GDP, inflation and long‐term interest rate) can indeed be forecasted more precisely with the factor‐backdated data.     

The Wage Inflation‐Unemployment Curve at the Macroeconomic Level

Based on the reduced form New Keynesian Wage Phillips Curve, we estimate wage rigidity and indexation at the aggregate level in several advanced countries for the 1985–2014 period. We document that the wage setting process is heterogenous among our sample of countries: nominal wage rigidities are more important in the United States, while wage indexation is dominant in European Countries. We also present evidence that indexation to past inflation has decrease as inflation stabilizes at lower levels. In addition, our results suggest that wage rigidity is not linked to the institutional environment at the macroeconomic level. Finally, we show that there is significant time variation in the estimated coefficients on the implied equation that is usually not taken into account in the theoretical literature.     

服装行业销售预测准确率提升研究

服装行业近年来飞速发展,并逐渐从功能性产品向创新型产品转变,消费者的需求也更加多样化,个性化,因此,消费的需求也愈发难以预测,这给传统服装品牌商带来极大挑战。文章以某服装品牌商为例,采取大数据的分析手段,研究了在客户需求多变的背景下,如何为企业提升销售预测准确率,并建立了一套符合企业现状的销售预测模型体系。     

Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility

This paper compares alternative models of time‐varying volatility on the basis of the accuracy of real‐time point and density forecasts of key macroeconomic time series for the USA. We consider Bayesian autoregressive and vector autoregressive models that incorporate some form of time‐varying volatility, precisely random walk stochastic volatility, stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The results show that the AR and VAR specifications with conventional stochastic volatility dominate other volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree. Copyright © 2014 John Wiley & Sons, Ltd.     

Global and Country‐Specific Output Growth Uncertainty and Macroeconomic Performance

We identify global and country‐specific measures of output growth uncertainty for a large OECD country sample by means of a dynamic factor model with stochastic volatility. We find evidence for major bouts of global uncertainty in the early 1970s and late 2000s, and a number of periods with elevated levels of either global or national uncertainty, particularly in the early 1980s, 1990s and 2000s. VAR impulse responses of national macroeconomic variables to our estimated measures of uncertainty reveal that global uncertainty is the major driver of macroeconomic performance in most countries, whereas the impact of national uncertainty is small and frequently insignificant. We also find that uncertainty is transmitted primarily through investment and trade flows rather than through consumption demand.     

Relative Risk Aversion and Power‐Law Distribution of Macroeconomic Disasters

The coefficient of relative risk aversion is notoriously difficult to estimate. Recently, Barro and Jin (On the size distribution of macroeconomic disasters, Econometrica 2011; 79 (3): 434–455) have come up with a new estimation approach that fits a power‐law model to the tail of distribution of macroeconomic disasters. We show that their results can be successfully replicated using a more refined power‐law fitting methodology and a more comprehensive dataset. Copyright © 2014 John Wiley & Sons, Ltd.     

Real‐time forecast combinations for the oil price

Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338–351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea‐based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor‐based Nelson–Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures‐based model improves forecast accuracy at longer horizons.     

Macroeconomic effects of aggregate accounting conservatism: A cross‐country analysis

This paper examines whether aggregate conditional and unconditional conservatism are associated with economic growth. Prior studies find that conditional conservatism improves contracting efficiency, but that unconditional conservatism has either a neutral or detrimental impact on contracting. We therefore conjecture that country‐level conditional conservatism increases the efficiency of resource allocation in an economy, whereas country‐level unconditional conservatism is not similarly beneficial. Using a cross‐country sample, we construct country‐level estimates of conditional and unconditional conservatism. We find that conditional conservatism is associated with higher level of growth in Gross Domestic Product and Gross Domestic Product per Capita. By contrast, unconditional conservatism shows no or negative association. Our study contributes to the ongoing debate on the desirability of accounting conservatism and also extends the literature on the macroeconomic effects of aggregate financial reporting attributes.     

Politics matter: changes in unionisation rates in rich countries, 1960–2010

We use the Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts database to examine the changes in collective bargaining coverage and union density among 21 OECD countries over the past 50 years. The observed patterns suggest that national politics are a more important determinant of recent trends in unionisation than globalisation or technological change.     

Embodying Embodiment in a Structural,Macroeconomic Input-Output Model

In this paper, I develop a regression-based system of labour productivity equations that account for capital-embodied technological change and I incorporate this system into IDLIFT, a structural, macroeconomic input-output model of the US economy. Builders of regression-based forecasting models have long had difficulty finding labour productivity equations that exhibit the "Solowian' property that movements in investment should cause accompanying movements in labour productivity. The production theory developed by Solow and others dictates that this causation is driven by the effect of traditional capital deepening as well as technological change embodied in capital. Lack of measurement of the latter has hampered the ability of researchers to estimate properly the productivity-investment relationship. Recent research by Wilson (2001) has alleviated this difficulty by estimating industry-level embodied technological change. In this paper, I utilize those estimates to construct capital stocks adjusted for technological change and then use these adjusted stocks to estimate Solow-type labour productivity equations. It is shown that replacing IDLIFT's former productivity equations, based on changes in output and time trends, with the new equations, results in a convergence between the dynamic behaviour of the model and that predicted by traditional (Solowian) production theory.     

Analysing the importance of sustainability‐oriented service quality in competition environment

The logistics sector is one of the most important domains in the service sector in understanding the increasing scale of business activities along with globalization. Due to the nature of the service sector, where competition is increasingly concentrated, the main objective of firms is the creation of their strategy on competitive advantage in regard with knowing the level of quality of the services offered and the firm's image from the customers' point of view. Trying to adapt to the changing conditions of the market drives firms to integrate their competing areas into their sustainability dimensions (economic, social, and environmental) with the aim of increasing their perceived quality through differentiating their business to achieve a sustainable competitive advantage. The aim of this study is to examine the quality level perceived by logistics service providers in terms of triple bottom line approach and to determine the relationship between them for sustainable competition. In line with exploratory methods, factor analysis was used with the aim of finding out a new scale to the literature about the examination of the relationship between quality perception and corporate sustainability in the frame of competition. In this context, data of 120 logistics service providers were collected and analysed. According to the results, the content of the subject consists of nine factors and 29 interrelated subdimensions. As a result of the research, firms have emphasized on the importance of reaching the competition by expanding their quality perceptions within the scope of corporate sustainability in parallel with the increasing customer demands and expectations.     

Assessing forecast performance in a cointegrated system

This paper examines the forecast performance of a cointegrated system relative to the forecast performance of a comparable VAR that fails to recognize that the system is characterized by cointegration. The cointegrated system we examine is composed of three vectors, a money demand representation, a Fisher equation, and a risk premium captured by an interest rate differential. The forecasts produced by the vector error correction model (VECM) associated with this system are compared with those obtained from a corresponding differenced vector autoregression, (DVAR) as well as a vector autoregression based upon the levels of the data (LVAR). Forecast evaluation is conducted using both the ‘full-system’ criterion proposed by Clements and Hendry (1993) and by comparing forecast performance for specific variables. Overall our findings suggest that selective forecast performance improvement (especially at long forecast horizons) may be observed by incorporating knowledge of cointegration rank. Our general conclusion is that when the advantage of incorporating cointegration appears, it is generally at longer forecast horizons. This is consistent with the predictions of Engle and Yoo (1987). But we also find, consistent with Clements and Hendry (1995) that relative gain in forecast performance clearly depends upon the chosen data transformation.     

The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State

This paper explores the relationship between institutional change and forecast accuracy via an analysis of the entitlement caseload forecasting process in Washington State. This research extends the politics of forecasting literature beyond the current area of government revenue forecasting to include expenditure forecasting and introduces an in-depth longitudinal study to the existing set of cross-sectional studies. Employing a fixed-effects model and ordinary least squares regression analysis, this paper concludes that the establishment of an independent forecasting agency and subsequent formation of technical workgroups improve forecast accuracy. Additionally, this study finds that more frequent forecast revisions and structured domain knowledge improve forecast accuracy.