A two factor model to combine US inflation forecasts

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.     

Forecast combination when outcomes are difficult to predict

We show that when outcomes are difficult to forecast in the sense that forecast errors have a large common component that (a) optimal weights are not affected by this common component, and may well be far from equal to each other but (b) the relative mean square error loss from averaging over optimal combination can be small. Hence, researchers could well estimate combining weights that indicate that correlations could be exploited for better forecasts only to find that the difference in terms of loss is negligible. The results then provide an additional explanation for the commonly encountered practical situation of the averaging of forecasts being difficult to improve upon.     

Nonparametric kernel estimation applied to forecasting: An evaluation based on the bootstrap

The results reported in this paper lend support to the nonparametric approach to estimating regression functions. This conclusion is based on a comparison of two sets of eight quarterly forecasts of U.S. hog supply generated by a well specified parametric dynamic model and by nonparametric kernel estimation. Despite the relatively small sample size, the nonparametric point forecasts are found to be as accurate as the parametric forecasts according to the mean square error and mean absolute error criteria. Bootstrap resampling is used to estimate the distributions of the forecast errors. The results of this exercise favour the nonparametric forecasts, which are found to have a tighter distribution.     

IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS*

This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias–variance trade‐off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.     

Measuring and testing the long-term impact of terrorist attacks on the US futures market

This study compares the accuracy and information content of economic forecasts for G7 countries made in the 1990s by the OECD and IMF. The benchmarks for comparison are the average forecasts of private sector economists published by Consensus Economics. With few exceptions, the private sector forecasts are less biased and more accurate in terms of mean absolute error and root mean square error. Formal tests show these differences are statistically significant for forecasts of real growth and production, less so for forecasts of inflation and unemployment. Overall, there appears little information in the OECD and IMF forecasts that could be used to reduce significantly the error in the private sector forecasts.     

The diversity of forecasts from macroeconomic models of the US economy

This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated US recessions. We generate forecasts from six different models of the US economy and compare them to professional forecasts from the Federal Reserve??s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates.     

Outline of forecast theory using generalized cost functions

The cost functions used to form forecasts in practice may be quite different than the squared costs that is often assumed in forecast theory. The impact on evaluation procedures is determined and simple properties for the derivate of the cost function of the errors are found to provide simple tests of optimality. For a very limited class of situations are forecasts based on conditional means optimal, generally, the econometricians needs to provide the whole conditional predicted distribution. Implications for multi-step forecasts and the combination of forecasts are briefly considered.     

Demographics and medical care spending: standard and non-standard effects

"In this paper, [the authors] examine the effects of likely demographic changes on medical spending for the elderly. Standard forecasts highlight the potential for greater life expectancy to increase costs: medical costs generally increase with age, and greater life expectancy means that more of the elderly will be in the older age groups. Two factors work in the other direction, however. First, increases in life expectancy mean that a smaller share of the elderly will be in the last year of life, when medical costs generally are very high.... Second, disability rates among the surviving population have been declining in recent years by 0.5 to 1.5 percent annually.... Thus, changes in disability and mortality should, on net, reduce average medical spending on the elderly. However, these effects are not as large as the projected increase in medical spending stemming from increases in overall medical costs."     

Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment

The efficacy of official forecasts in the EU has been under the spotlight since the introduction of the euro, with biases widely reported prior to the 2008–12 financial and sovereign bond market crisis. Changes to the EU fiscal rules and procedures, in the form of the European Semester and Fiscal Compact, in the early 2010s were adopted to improve forecasting, including through providing a role for independent fiscal institutions. Using data for 22 countries between 2013 and 2019, this paper shows that, despite these changes, biases, of a pessimistic form, remain in forecasts of budget balance and output variables in Stability and Convergence Programmes and the European Commission's Spring Forecasts. Econometric analysis indicates forecast errors in both the headline budget balance and the structural budget balance being explained by forecast errors in output variables and by EU fiscal rule requirements. Member states under an excessive deficit procedure provide optimistic headline budget balance forecasts compared to non-EDP countries, while those that have not met their medium-term objective report smaller forecast errors for the structural budget balance. Independent fiscal institutions are linked to a smaller bias to forecasts of the structural budget balance but have no effect on the forecast errors of the headline budget balance.     

Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates

European Monetary Union member countries’ forecasts are often combined to obtain the forecasts of the Euro area macroeconomic aggregate variables. The aggregation weights which are used to produce the aggregates are often considered as combination weights. This paper investigates whether using different combination weights instead of the usual aggregation weights can help to provide more accurate forecasts. In this context, we examine the performance of equal weights, the least squares estimators of the weights, the combination method recently proposed by Hyndman et al.  (Comput Stat Data Anal 55(9):2579–2589, 2011) and the weights suggested by shrinkage methods. We find that some variables like real GDP and the GDP deflator can be forecasted more precisely by using flexible combination weights. Furthermore, combining only forecasts of the three largest European countries helps to improve the forecasting performance. The persistence of the individual series seems to play an important role for the relative performance of the combination.     

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.     

How (Over) Confident Are Financial Analysts?

Extensive research has been devoted to the quality of analysts' earnings forecasts. The common finding is that analysts' forecasts are not very accurate. Prior studies have tended to focus on the mean of forecasts and measure accuracy using various summaries of forecast errors. The present study sheds new light on the accuracy of analysts' forecasts, by measuring how well calibrated these forecasts are. The authors follow the tradition of calibration studies in psychological literature and measure the degree of calibration by the hit rate. They analyze a year's worth of data from the Institutional Brokers Estimate System database, which includes over 200,000 annual earnings forecasts made by over 6,000 analysts for over 5,000 companies. By using different ways to convert analysts' point estimates of earnings into a range of values, the authors establish the bounds that are necessary to determine the hit rates, and examine to what extent the actual earnings announced by the companies are bracketed by these intervals. These hit rates provide a more complete picture of the accuracy of the forecasts.     

Commodity prices and related equity prices

This paper shows that commodity‐sensitive stock price indices have strong power in predicting nominal and real commodity prices at short horizons (one‐month‐ahead predictions) using both in‐ and out‐of‐sample tests. The forecasts based on commodity‐sensitive stock price indices are able to significantly outperform naïve no‐change forecasts. For example, the one‐month‐ahead forecasts for nominal commodity prices reduce the mean squared prediction error by between 1.5% (for natural gas prices) and 20% (for copper prices). Moreover, the one‐month‐ahead directional forecast is found to perform significantly better than a 50:50 coin toss. As stock prices are not subject to revision, the proposed variable, which reflects timely and readily available market information, can potentially be a valuable predictor and thereby help to improve the accuracy of commodity price forecasts.     

Predicting U.S. recessions through a combination of probability forecasts

Recently De Luca and Carfora (Statistica e Applicazioni 8:123–134, 2010) have proposed a novel model for binary time series, the Binomial Heterogenous Autoregressive (BHAR) model, successfully applied for the analysis of the quarterly binary time series of U.S. recessions. In this work we want to measure the efficacy of the out-of-sample forecasts of the BHAR model compared to the probit models by Kauppi and Saikkonen (Rev Econ Stat 90:777–791, 2008). Given the substantial indifference of the predictive accuracy between the BHAR and the probit models, a combination of forecasts using the method proposed by Bates and Granger (Oper Res Q 20:451–468, 1969) for probability forecasts is analyzed. We show how the forecasts obtained by the combination between the BHAR model and each of the probit models are superior compared to the forecasts obtained by each single model.     

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.     

Equilibrium-correction vs. differencing in macroeconometric forecasting

Recent work by Clements and Hendry elucidate why forecasting systems that are in terms of differences, dVARs, can be more accurate than econometric models that include levels variables, EqCMs. For example, dVAR forecasts are in some cases insulated from parameter non-constancies in the long run mean of the cointegration relationships. In this paper, the practical relevance of these issues are investigated for RIMINI, the quarterly macroeconometric model used in Norges Bank (Central Bank of Norway), an example of an EqCM forecasting model. We develop two dVAR versions of the full RIMINI model and compare EqCM and dVAR forecasts for the period 1992.1–1994.4. We also include forecasts from univariate dVAR type models. The results seem to confirm the relevance of the theoretical results. First, dVAR forecasts appear to provide some immunity against parameter non-constancies that could seriously bias the EqCM forecasts. Second, the misspecification resulting from omitting levels information generates substantial biases in the dVAR forecasts 8 and 12 quarters ahead.     

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.     

FORECASTING AUSTRALIAN MACROECONOMIC VARIABLES USING A LARGE DATASET

This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches.     

Real-time uncertainty in budget planning: evidence from euro area countries1

Using rich panel data including potential output for euro area countries, we analyse budget balance forecasts and their errors. We find that budget balance forecasts are systematically biased and subject to mean reversion (tendency towards more balanced budgets). A robust result is that errors in budget balance nowcasts contribute to errors in budget balance forecasts. In addition, we find that nowcasted macroeconomic conditions can affect over-optimism in budget balance forecasts. Overall, our results emphasize the central role of nowcasting in the EU fiscal framework.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.