Conditional forecasts on SVAR models using the Kalman filter

This note shows how conditional forecasts from identified VAR models can be computed using Kalman filtering techniques. These techniques are nowadays routine for applied macroeconomists, and hence the computation of conditional forecasts using these methods are simple to implement.     

A dynamic factor model framework for forecast combination

A panel of ex-ante forecasts of a single time series is modeled as a dynamic factor model, where the conditional expectation is the single unobserved factor. When applied to out-of-sample forecasting, this leads to combination forecasts that are based on methods other than OLS. These methods perform well in a Monte Carlo experiment. These methods are evaluated empirically in a panel of simulated real-time computer-generated univariate forecasts of U.S. macroeconomic time series.     

Modeling and forecasting return jumps using realized variation measures

This paper proposes a simple HAR-RV-based model to predict return jumps through a conditional density of jump size with time-varying moments. We model jump occurrences based on a version of the autoregressive conditional hazard model that relies on past continuous realized volatilities. Applying our methodology to seven equity indices on the U.S. and Chinese stock markets, we reach the following key findings: (i) jump occurrence and size are dependent on past realized volatility, (ii) the proposed model yields superior in- and out-of-sample jump size density forecasts compared to an ARMA(1,1)-GARCH(1,1) model, (iii) and the occurrence and sign of return jumps are predictable to some extent.     

Predictive ability of asymmetric volatility models at medium-term horizons

Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.     

Volatility spill-overs in commodity spot prices: New empirical results

The classical rational expectations model of commodity markets implies that expected spot price risk is an explanatory variable in spot price regressions; and also that inventory carryover, which is reduced by a larger price variance, creates autoregressive conditional heteroscedastic processes in spot prices. In order to falsify/verify this theory, it has typically been assumed that the square root of the conditional variance of spot prices, a proxy for spot price risk, enters the conditional mean function of spot prices. Based on this simple representation, a typical but counter intuitive outcome has been that spot price risk has an insignificant impact on spot prices, see, e.g., Beck (Beck, S., 1993. A Rational Expectations Model of Time Varying Risk Premia in Commodities Futures Markets: Theory and Evidence. International Economic Review 34, 149–168, Beck, S., 2001. Autoregressive Conditional Heteroskedasticity in Commodity Spot Prices. Journal of Applied Econometrics 16, 115–132). In this paper, we propose an alternative functional relationship (from GARCH(1,1) to GARCH(1,1)-AR(m)) between spot price risk and spot prices that is fully supported by the classical rational expectations model, and based on this new representation we are able to provide stronger empirical support for Muth's rational expectation theory.     

Understanding forecast failure of ESTAR models of real exchange rates

The forecast performance of the empirical ESTAR model of Taylor et al. (2001) is examined for 4 bilateral real exchange rate series over an out-of-sample evaluation period of nearly 12?years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22 steps head. The study finds that no forecast gains over a simple AR(1) specification exist at any of the forecast horizons that are considered, regardless of whether point or density forecasts are utilised in the evaluation. Non-parametric methods are used in conjunction with simulation techniques to learn about the models and their forecasts. It is shown graphically that the nonlinearity in the conditional means (or point forecasts) of the ESTAR model decreases as the forecast horizon increases. The non-parametric methods show also that the multiple steps ahead forecast densities are normal looking with no signs of bi-modality, skewness or kurtosis.     

Estimating loss functions of experts

We propose a new and simple methodology to estimate the loss function associated with experts’ forecasts. Under the assumption of conditional normality of the data and the forecast distribution, the asymmetry parameter of the lin–lin and linex loss function can easily be estimated using a linear regression. This regression also provides an estimate for potential systematic bias in the forecasts of the experts. The residuals of the regression are the input for a test for the validity of the normality assumption. We apply our approach to a large data set of SKU-level sales forecasts made by experts, and we compare the outcomes with those for statistical model-based forecasts of the same sales data. We find substantial evidence for asymmetry in the loss functions of the experts, with underprediction penalized more than overprediction.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

Estimation and Testing of Forecast Rationality under Flexible Loss

In situations where a sequence of forecasts is observed, a common strategy is to examine „rationality” conditional on a given loss function. We examine this from a different perspective—supposing that we have a family of loss functions indexed by unknown shape parameters, then given the forecasts can we back out the loss function parameters consistent with the forecasts being rational even when we do not observe the underlying forecasting model? We establish identification of the parameters of a general class of loss functions that nest popular loss functions as special cases and provide estimation methods and asymptotic distributional results for these parameters. This allows us to construct new tests of forecast rationality that allow for asymmetric loss. The methods are applied in an empirical analysis of IMF and OECD forecasts of budget deficits for the G7 countries. We find that allowing for asymmetric loss can significantly change the outcome of empirical tests of forecast rationality.     

Cost-based Phillips Curve forecasts of inflation

It is a well-established idea that prices are a function of marginal cost, yet estimating a reliable measure of marginal cost is difficult to do. Stock and Watson (1999) use the Phillips Curve to forecast inflation for a variety of existing activity variables that researchers commonly use to proxy for marginal cost. This paper uses a similar type of approach to examine the performance of a new candidate for the activity variable, which is marginal cost measured following the theoretical methodology of Bils (1987), which we find to be simple yet powerful when implemented empirically. We then use the Phillips Curve to conduct pseudo out-of-sample inflation forecasts for the US using: output, unemployment, hours, the labor share, the capacity utilization rate, and the new measure of marginal cost. For almost all cases, forecast errors are lowest in the regressions with the new marginal cost variable, indicating that this new measure is an improvement over previous attempts to proxy for marginal cost.     

Forecasting demand for electricity in Maryland: An econometric approach

Most of the models for forecasting demand for energy are based on simple extrapolations of past trends or on a simple regression equation with price of the energy and the stock of appliances as explanatory variables. In this paper, an attempt has been made to derive static and dynamic multiple regression equations from economic theory of consumption and production (Section II). Historical data were fitted to these theoretical constructs to test the equations in terms of econometric theory and forecast the demand according to “higher order conditional interval forecasts”. The residential demand for electricity is a function of its price, price of its substitute, per capita income and a lagged demand variable for dynamic adjustment of actual demand to equilibrium demand for electricity. The forecasts of residential demand to 1990 are based on projections of exogenous variables such as residential price of electricity, per capita income and the estimated long run elasticity of demand (Section III). The nonresidential demand for electricity is a function of employment in that sector, sectoral prices of electricity and the lagged sectoral demand. The forecasts of nonresidential electricity demand are also based on projections of its independent variables (Section IV). The last section converts the total demand for electricity into the required generating capacities and juxtaposes them against the estimates of expected supplies available from the forecasts of the utilities. The paper concludes that the eighties will be faced with excess supply of electricity in Maryland, in case the assumptions of projections of independent variables hold good. The misallocation of resources inherent in such excess supplies could be avoided if realistic scenerios of future demand, as attempted in this paper, could be predicted.     

Rationality tests in the presence of instabilities in finite samples

The importance of expectations in modern macroeconomic models and in particular of policy makers expectations for forward looking policy rules has generated a lot of interest in time series of professional forecasts (including central bank staff forecasts). This has spawned a large literature on the evaluation of forecasts that are not model based or where the model is unknown. Although, the available time series of historical forecasts are typically short, this literature has so far mostly disregarded the small sample properties of the proposed tests and estimators. In this paper we fill this gap in the literature, focusing on a set of recently proposed rationality tests for unstable environments. Using a Monte Carlo study we demonstrate that the asymptotic tests are substantially oversized in finite samples including any sample size that is practically available. We provide finite sample adjusted critical values, that allow those tests to be properly applied to sample sizes of typically available forecasts such as the Survey of Professional Forecasters, the Federal Open Market Committee. The critical values we provide will help to avoid false rejections using those data.     

A simple and effective misspecification test for the double-hurdle model

The commonly-used version of the double-hurdle model rests on a rather restrictive set of statistical assumptions, which are very seldom tested by practitioners, mainly because of the lack of a standard procedure for doing so, although violation of such assumptions can lead to serious modelling flaws. We propose here a bootstrap-corrected conditional moment portmanteau test which is simple to implement and has good size and power properties.     

Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

Modeling Expectation Formation Involving Several Sources of Information

Abstract. Experimental studies of expectation formation of subjects are predominantly limited to the prediction of one single time series despite the practical relevance of expectations in situations with multiple sources of information. In this paper, we report on an experiment in which subjects are given time series (indicators) as additional information for the judgemental forecast of a stationary time series. The quality and the number of these indicators are varied in three versions of a forecasting experiment. We explore the effects on forecasting accuracy and we test the average forecasts of the subjects for consistency with the rational expectations hypothesis. A simple heuristic is presented that explains the average forecasting behavior better than the rational expectations if indicators are presented to the subjects. It is demonstrated by a simulation study that this result is representative for the considered stationary stochastic processes.     

FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out‐of‐sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy‐and‐hold strategy.     

The Performance of Exchange Rate Forecasts

Since the floating of the Australian dollar the forecasting of exchange rate movements has become more difficult and received much more attention. As a result, some participants in the foreign exchange market have, on a number of occasions, come under criticism for their inability to predict exchange rate movements. This article seeks to evaluate these criticisms through an examination of exchange rate forecasts made by market participants (as published in the Australian Financial Review from March 1985 to December 1985). The accuracy of the $A/US$ forecasts is compared with that of forecasts generated from a number of simple forecasting rules as well as forecasts of the US$/Yen exchange rate. In general, the simple forecasting rules provide superior forecasts to those provided by the individual market participants. However, under some criteria, the mean of the individual participants' forecasts may be preferred to these simple forecasting rules. Further, the comparison of the US$/Yen forecasts with the $A/US$ forecasts shows the former to be generally more accurate.     

风电场风速预测的不确定性研究

风速预测的超前性导致其预测结果是不确定的,传统的确定性风速预测只提供确切的数值,不能满足电网规划的要求。分析了风速预测结果不确定性影响因素,并在确定性预测结果的基础上,从条件概率和预测误差分布统计规律两个角度对预测概率和置信区间进行分析和计算。仿真试验表明该方法能够为决策者提供概率预测信息,具有一定的实用性和有效性。     

Macroeconomic forecasting in the Euro area: Country specific versus area-wide information

This paper compares several time series methods for short-run forecasting of Euro-wide inflation and real activity using data from 1982 to 1997. Forecasts are constructed from univariate autoregressions, vector autoregressions, single equation models that include Euro-wide and US aggregates, and large-model methods in which forecasts are based on estimates of common dynamic factors. Aggregate Euro-wide forecasts are constructed from models that utilize only aggregate Euro-wide variables and by aggregating country-specific models. The results suggest that forecasts constructed by aggregating the country-specific models are more accurate than forecasts constructed using the aggregate data.     

成本粘性影响管理层自愿性盈利预测了吗？--来自中国A股上市公司的证据

文章研究成本粘性与管理层自愿性盈利预测之间的关联。结果发现：成本粘性增加了管理层发布自愿性盈利预测的倾向及频率,同时,成本粘性程度与乐观盈利预测呈正相关,与悲观盈利预测呈负相关。影响机制表明,成本粘性通过提高信息不对称程度和企业经营风险从而影响了管理层自愿性盈利预测披露。进一步研究发现,政府补助加强了成本粘性与管理层自愿性盈利预测披露之间的关系,企业社会责任与经济政策不确定性削弱了两者之间的关系;然而政府补助、企业社会责任与经济政策不确定性对成本粘性与管理层自愿性盈利预测性质之间的关系没有显著影响。此外,成本粘性对管理层自愿性盈利预测的影响在非国有企业、非国际四大审计的公司中更为显著。最后,管理层自愿性盈利预测披露的增加,降低了权益资本成本,提升了企业价值。本文的发现丰富了成本粘性经济后果和自愿披露信息影响因素的研究,并提供了关于管理层的自由裁量权和管理激励在影响企业成本结构和盈利预测信息披露方面的证据。