An analysis of RSQE forecasts: 1971–1992

The purpose of this paper is to evaluate the accuracy of ex ante econometric model forecasts of four key macroeconomic variables: real GNP growth, the rate of price inflation measured by the GNP deflator, the civilian unemployment rate, and the Treasury Bill rate. Annual forecasts produced by the Research Seminar in Quantitative Economics (RSQE) based on the Michigan Quarterly Econometric Model of the U.S. Economy are compared with quasi ex ante forecasts from a four-variable vector autoregressive (VAR) model. Statistical tests of the equality of forecast error variances as well as univariate and multivariate forecast encompassing-type tests are conducted. The forecast error variance comparisons indicate that for three of the four variables the RSQE forecasts are more accurate than the VAR forecasts and for one of the variables (real GNP growth) only slightly less accurate. The forecast encompassing-type tests indicate that the RSQE forecasts contain information not contained in the VAR forecasts and, conversely, that VAR forecasts contain information not included in the RSQE forecasts. The scope for improving RSQE forecasts by combining them with VAR forecasts is rather limited, however.     

Short-Term Water Consumption Patterns in Ciudad Juárez, Mexico

Water utility planning efforts are becoming increasingly difficult throughout the world. Located in a semi-arid region, Ciudad Juárez, Mexico is a fast growing municipality that faces both physical infrastructure and water supply constraints. This paper examines monthly water consumption in Ciudad Juárez utilizing a linear transfer function procedure (LTF). Analysis is carried out for per customer usage and for the total number of municipal water system accounts. Models estimated for both series are subjected to a series of simulation benchmark tests. Findings suggest that water consumption in Ciudad Juárez reacts quickly to changes in economic and weather conditions. Out-of-sample simulation results are mixed. Per customer usage forecasts do not fare as well those for total customers relative to random walk benchmarks.Financial support for this research was provided by Southwest Center for Environmental Research Policy Grant W-04-03. Additional financial support was provided by El Paso Electric Company, Wells Fargo Bank of El Paso, and National Science Foundation Grant SES-0332001. Helpful comments and suggestions were provided by Cely Ronquillo, Brian Kelley, and Cesar Olivas. Econometric research assistance was provided by Marycruz De Leon and Irma Torres.     

Exchange Rate Forecasting: Results from a Threshold Autoregressive Model

Structural models of exchange rate determination rarely forecast the exchange rate more accurately than a naive random walk model. Recent innovations in exchange rate modeling indicate that changes in the exchange rate may follow a self-exciting threshold autoregressive model (SETAR). We estimate a SETAR model for various monthly US dollar exchange rates and generate forecasts for the estimated models. We find: (1) nonlinearities in the data not uncovered by the standard nonlinearity tests and (2) that the SETAR model produces better forecasts than the naive random walk model.     

The implications of monetary expansion in China for the US dollar

We examine the influence of rapid growth in China's money supply on the US dollar within a framework of monetary models of exchange rates. We develop out-of-sample forecasts of the US dollar exchange rate using US and global data on price level, output, and interest rates, and money supply data for the US, China, and the rest of the world for the period 1996–2013. Monetary model forecasts significantly outperform a random walk forecast in terms of mean squared forecast error in the long run. A monetary error correction model with sticky prices performs best. Rolling sample analysis indicates changes over time in the influence of Chinese money supply in forecasting the US dollar. The expectation is that rapid money growth in China would increase the demand for dollars thus raising the value of the dollar, yet our forecasts are to the contrary for the mid 2000s. This is consistent with anticipation of renminbi appreciation under China’s managed exchange rate, which made holding renminbi more attractive. With the break from a dollar peg in 2005 and subsequent currency appreciation, the distortion was alleviated and the forecast direction for the dollar became as expected.     

Dancing with bulls and bears: Nearest-neighbour forecasts for the Nikkei index

In this paper we apply nearest-neighbour local predictors, inspired by the literature on forecasting in nonlinear systems, to the Nikkei 225 Index of the Tokyo Stock Market for the period 1 January 1986–5 June 1997. When forecasting performance is measured by Theil's U statistic, our nearest-neighbour predictors perform worse than a random walk, outperforming the random walk directional forecast. When formally testing for forecast accuracy, the results suggest that predictions from a random walk were statistically significantly better than the nearest-neighbour predictors for the entire forecasting period, as well as for one of the subperiods (a `bull' market episode). Finally, when assessing the economic value of the nearest-neighbour predictors in absence of trading costs, the results of using them as a filter technique are superior to a buy-and-hold strategy for both the entire forecasting period and for `bear' market subperiods, where tests of `forecast conditional efficiency' (or `forecast encompassing') detected that the nearest-neighbour predictors contain useful information for forecasting the Nikkei Index that is not contained in the random walk.     

A Re-Examination of Financial Analysts' Differential Earnings Forecast Accuracy*

Abstract. This research re-examines whether there are differences in the forecast accuracy of financial analysts through a comparison of their annual earnings per share forecasts. The comparison of analyst forecast accuracy is made on both an ex post (within sample) and an ex ante (out of sample) basis. Early examinations of this issue by Richards (1976), Brown and Rozeff (1980), O'Brien (1987), Coggin and Hunter (1989), O'Brien (1990), and Butler and Lang (1991) were ex post and suggest the absence of analysts who can provide relatively more accurate forecasts over multiple years. Contrary to the results of prior research and consistent with the belief in the popular press, we document that differences do exist in financial analysts' ex post forecast accuracy. We show that the previous studies failed to find differences in forecast accuracy due to inadequate (or no) control for differences in the recency of forecasts issued by the analysts. It has been well documented in the literature that forecast recency is positively related to forecast accuracy (Crichfield, Dyckman, and Lakonishok 1978; O'Brien 1988; Brown 1991). Thus, failure to control for forecast recency may reduce the power of tests, making it difficult to reject the null hypothesis of no differences in forecast accuracy even if they do exist. In our analysis, we control for the differences in recency of analysts' forecasts using two different approaches. First, we use an estimated generalized least squares estimation procedure that captures the recency-induced effects in the residuals of the model. Second, we use a matched-pair design whereby we measure the relative forecast accuracy of an analyst by comparing his/her forecast error to the forecast error of another randomly selected analyst making forecasts for the same firm in the same year on or around the same date. Using both approaches, we find that differential forecast accuracy does exist amongst analysts, especially in samples with minimum forecast horizons of five and 60 trading days. We show that these differences are not attributable to differences in the forecast issuance frequency of the financial analysts. In sum, after controlling for firm, year, forecast recency, and forecast issuance frequency of individual analysts, the analyst effect persists. To validate our findings, we examine whether the differences in the forecast accuracy of financial analysts persist in holdout periods. Analysts were assigned a “superior” (“inferior”) status for a firm-year in the estimation sample using percentile rankings on the distribution of absolute forecast errors for that firm-year. We use estimation samples of one- to four-year duration, and consider two different definitions of analyst forecast superiority. Analysts were classified as firm-specific “superior” if they maintained a “superior” status in every year of the estimation sample. Furthermore, they were classified as industry-specific “superior” if they were deemed firm-specific “superior” with respect to at least two firms and firm-specific “inferior” with respect to no firm in that industry. Using either definition, we find that analysts classified as “superior” in estimation samples generally remain superior in holdout periods. In contrast, we find that analysts identified as “inferior” in estimation samples do not remain inferior in holdout periods. Our results suggest that some analysts' earnings forecasts should be weighted higher than others when formulating composite earnings expectations. This suggestion is predicated on the assumption that capital markets distinguish between analysts who are ex ante superior, and that they utilize this information when formulating stock prices. Our study provides an ex ante framework for identifying those analysts who appear to be superior. When constructing weighted forecasts, a one-year estimation period should be used because we obtain the strongest results of persistence in this case.     

Investors' Misweighting of Firm-Level Information and the Market's Expectations of Earnings

Prior studies use fundamental earnings forecasts to proxy for the market's expectations of earnings because analyst forecasts are biased and are available for only a subset of firms. We find that as a proxy for market expectations, fundamental forecasts contain systematic measurement errors analogous to those in analysts' biased forecasts. Therefore, these forecasts are not representative of investors' beliefs. The systematic measurement errors from using fundamental forecasts to proxy for market expectations occur because investors misweight the information in many firm-level variables when estimating future earnings, but fundamental forecasts are formed using the historically efficient weights on firm-level variables. Thus, we develop an alternative ex ante proxy for the market's expectations of future earnings (“the implied market forecast”) using the historical (and inefficient) weights, as reflected in stock returns, that the market places on firm-level variables. A trading strategy based on the implied market forecast error, which is measured as the difference between the implied market forecast and the fundamental forecast, generates excess returns of approximately 9 percent per year. These returns cannot be explained by investors' reliance on analysts' biased forecasts. Overall, our results reveal that market expectations differ from both fundamental forecasts and analysts' forecasts.     

The Incremental Informativeness of Stock Prices for Future Accounting Earnings*

This study extends previous research that documents a stock price reaction leading accounting earnings. The primary issue is that prior studies use a naive earnings expectation model (random walk) as the benchmark for the information content of lagged returns and do not adequately address the “incremental” information content of lagged returns. This study identifies and estimates firm-specific models of earnings to control directly for the autocorrelation in earnings. The explanatory power of lagged prices with respect to this earnings residual is investigated using both a multiple regression model of lagged returns and a multiple time-series vector autoregressive model. In-sample estimation of the models provides clear evidence that stock prices impound information about future earnings incremental to the information contained in historical earnings data. Holdout period analysis of the earnings forecasts from these lagged return models finds that both models outperform the naive seasonal random walk expectation, but neither model outperforms the more sophisticated Box-Jenkins forecasts. On an individual firm basis, earnings forecasts supplemented with the lagged return data tend to be less precise than the Box-Jenkins forecasts, but the price-based models demonstrate an ability to rank the earnings forecast errors from the time-series models. The analysis helps to characterize the limitations of lagged returns as a means of predicting future earnings innovations.     

Who Herds? Who Doesn't? Estimates of Analysts’ Herding Propensity in Forecasting Earnings

We develop parametric estimates of the imitation‐driven herding propensity of analysts and their earnings forecasts. By invoking rational expectations, we solve an explicit analyst optimization problem and estimate herding propensity using two measures: First, we estimate analysts’ posterior beliefs using actual earnings plus a realization drawn from a mean‐zero normal distribution. Second, we estimate herding propensity without seeding a random error, and allow for nonorthogonal information signals. In doing so, we avoid using the analyst's prior forecast as the proxy for his posterior beliefs, which is a traditional criticism in the literature. We find that more than 60 percent of analysts herd toward the prevailing consensus, and herding propensity is associated with various economic factors. We also validate our herding propensity measure by confirming its predictive power in explaining the cross‐sectional variation in analysts’ out‐of‐sample herding behavior and forecast accuracy. Finally, we find that forecasts adjusted for analysts’ herding propensity are less biased than the raw forecasts. This adjustment formula can help researchers and investors obtain better proxies for analysts’ unbiased earnings forecasts.     

Restaurant Prices and the Mexican Peso

Of prime interest to border economies is exchange rate performance and currency valuation. Commonly used tools for this task include purchasing power parity (PPP) nominal benchmarks, and inflation-adjusted trade-weighted indices. The latter have the advantage of relying on commonly available international macroeconomic data but overlook microeeonomic information that may offer additional insight to issues surrounding exchange rate policy debates. Other efforts have utilized small samples of international product price comparisons to shed light on currency valuation questions. This paper develops one such tool by repeated sampling of prices charged for identical menu items sold at restaurant franchises in El Paso, Texas, and Ciudad Juárez, Chihuahua. A battery of statistical tests indicate that the international currency value of the peso consistently differed from the exchange rate implied by the border region restaurant price ratios in 1997, 1998, and 1999.     

FORECASTING PERFORMANCE OF AN ESTIMATED DSGE MODEL FOR THE SOUTH AFRICAN ECONOMY

We construct a small open‐economy New Keynesian dynamic stochastic general equilibrium (DSGE) model for South Africa with nominal rigidities, incomplete international risk sharing and partial exchange rate pass‐through. The parameters of the model are estimated using Bayesian methods, and its out‐of‐sample forecasting performance is compared with Bayesian vector autoregression (VAR), classical VAR and random‐walk models. Our results indicate that the DSGE model generates forecasts that are competitive with those from other models, and it contributes statistically significant information to combined forecast measures.     

Testing price-exchange rate noncausality: Results from a vector error correction model

Previous tests of the martingale property of the exchange rate have examined the random walk property of the exchange rate or have tested for Granger causality of the exchange rate by macroeconomic variables. Interestingly, if purchasing power parity (PPP) holds in the long run, causality tests should be conducted by estimating a vector error correction model which includes price levels. This paper conducts exchange rate causality tests employing vector error correction models and finds evidence that the Swiss exchange rate is Granger caused by lagged deviations in PPP and, hence, is Granger caused by prices.     

An Examination of the Statistical Significance and Economic Relevance of Profitability and Earnings Forecasts from Models and Analysts

In this paper, we propose and empirically test a cross‐sectional profitability forecasting model which incorporates two major improvements relative to extant models. First, in terms of model construction, we incorporate mean reversion through the use of a two‐stage partial adjustment model and inclusion of a number of additional relevant determinants of profitability. Second, in terms of model estimation, we employ least absolute deviation (LAD) analysis instead of ordinary least squares because the former approach is able to better accommodate outliers. Results reveal that forecasts from our model are more accurate than three extant models at every forecast horizon considered and more accurate than consensus analyst forecasts at forecast horizons of two through five years. Further analysis reveals that LAD estimation provides the greatest incremental accuracy improvement followed by the inclusion of income subcomponents as predictor variables, and implementation of the two‐stage partial adjustment model. In terms of economic relevance, we find that forecasts from our model are informative about future returns, incremental to forecasts from other models, analysts’ forecasts, and standard risk factors. Overall, our results are important because they document the increased accuracy and economic relevance of a cross‐sectional profitability forecasting model which incorporates improvements to extant models in terms of model construction and estimation.     

Some Reflections on the Interactions between the Bank of England's Forecasts and the MPC's Policy Decisions

This paper makes three main points. First, whereas the Monetary Policy Committee's forecasts of inflation and output growth in the UK are comparatively accurate, they cannot forecast deviations around trend, except at short horizons. Second, this is primarily because they adjust policy, the short-term interest rate, to drive inflation/output back to trend at their forecast horizon. This is not apparent when using a Taylor-rule using ex post forecasts, since these are published after taking account of policy changes. I use a rule of thumb to re-engineer estimates of the ex ante forecasts, upon which the policy decision was based. Also, because of the lengthy lags in the transmission mechanism, Central Bank decision-makers relate their interest decisions, not to current variables, but to forecast values for future inflation and output, with a forward-looking interest rate reaction function of the form:

An evaluation of Chinese macroeconomic forecasts

This article evaluates the quality of professional macroeconomic forecasts in China for the years 1995–2009. Using a large panel of forecasts on four macroeconomic variables (GDP, inflation, consumption and investment), we reject the hypothesis of unbiasedness, and find that forecasters have been, on average, overly pessimistic. The source of the bias lies primarily in forecasters' slow adjustment to structural shocks to the level of economic growth. We also reject the hypothesis that forecasters use information efficiently, and find that a large number of forecasters overreact to economic news. Finally, we document large differences of forecast accuracy across both forecasters and variables.     

Why is the forecast error of quarterly GDP in Japan so large? – From an international comparison of quarterly GDP forecast situation

We examined the accuracy of prediction of Canada, Japan, United Kingdom, and United States from the viewpoint of forecast errors. Compared with the forecast error of each country at the around same time, the forecast error of Japan is about 2 times larger. In case of Japan, even immediately before release of quarterly GDP, the forecast error is over 1 %, which is the same level of forecast error as 94 days before in the United States and 135 days before in Canada.Evaluating the characteristics of forecast errors, it can be pointed out that Japan's forecasts are as efficient as those of other countries, and the addition of major economic statistics is unlikely to improve forecast errors. The reason for Japan's large forecast errors is the fluctuations in the GDP growth rate. These results provide evidence that volatile GDP may make the outlook worse.Large fluctuations in Japan's quarterly GDP have already been pointed out. It is necessary to examine the factors behind the large fluctuations in the rate of change in Japan's quarterly GDP.     

Reformulation empirical macroeconomic modelling

The policy implications of estimated macro-econometric systemsdepend on the formulations of their equations, the methodologyof empirical model selection and evaluation, the techniquesof policy analysis, and their forecast performance. Drawingon recent results in the theory of forecasting, we questionthe role of 'rational expectations'; criticize a common approachto testing economic theories; show that impulse-response methodsof evaluating policy are seriously flawed; and question themechanistic derivation of forecasts from econometric systems.In their place, we propose that expectations should be treatedas instrumental to agents' decisions; discuss a powerful newapproach to the empirical modelling of econometric relationships;offer viable alternatives to studying policy implications; andnote modifications to forecasting devices that can enhance theirrobustness to unanticipated structural breaks.     

Forecast accuracy of the Japanese government: Its year-ahead GDP forecast is too optimistic

This paper evaluates accuracy and efficiency of the real GDP forecasts made by the Japanese government over the past 22 years. The government's 16-months-ahead forecast has upward bias of 0.7 percentage points, and is significantly inferior to the mean forecast of private institutions or a vector autoregression forecast that uses real-time data only. Moreover, the government forecast is inferior to these benchmark forecasts not only in the ‘recessionary’ 1990s but also in the ‘prosperous’ 1980s. This result casts serious doubt on the assertion that the government intentionally produced inaccurate forecasts in time of recession.     

Measuring the Accuracy of Federal Reserve Forecasts

Markets across the world pay enormous attention to every economic forecast made by Federal Reserve governors, particularly those from the chair. This article develops a new way that the academic literature can assess the accuracy of these Federal Reserve forecasts. In particular, our proposed method allows for both general and specific predictions to be assessed, while also accounting for the macroeconomic volatility that prevails at the time of the forecast. To develop this measure, we expand upon a methodology proposed by the Wall Street Journal to score the accuracy of forecasts made by the Fed. Our results show that Alan Greenspan was consistently the most accurate forecaster among Fed governors, while the most recent chair in our sample, Janet Yellen, has performed relatively poorly. More generally, we find that the chairs have become less accurate over time with their forecasts and have also tended to make fewer specific predictions.     

The Effect of Enterprise Systems Implementation on the Firm Information Environment

This study uses an archival research design to assess the impact of enterprise systems on a firm's internal information environment as reflected in the production of management earnings forecasts. Specifically, the authors hypothesize that, if enterprise systems improve management's access to decision‐relevant internal information, higher quality management earnings forecasts should ensue. Consistent with disclosure theory and the purported technical characteristics of enterprise systems, the authors find a positive association between enterprise system implementations and subsequent increases in the likelihood of management forecast issuance and the accuracy of the forecasts. Additional robustness tests support the argument that improvements in management forecasts are due to improvements in the firm's internal information environment rather than to enhancements in management's ability to manage earnings. Beyond accumulating financial reporting information, the authors note that such systems provide management with information to make day‐to‐day operational decisions. Moreover, the paper provides a basis for considering management forecast qualities as a measurable proxy for improvements in the firm's internal information environment that result from information technology investments.