Probability Forecasts of Macroaggregates in the Turkish Economy

We provide probability forecasts of key Turkish macroeconomic variables such as inflation and output growth. The probability forecasts are derived from a core vector error correction model of the Turkish economy and its several variants. We use model and window averaging to address uncertainties arising from estimated models and possible structural breaks. The performances of the different models and their combinations are evaluated using relevant forecast accuracy tests in different pseudo out-of-sample settings. The results indicate that successful directional forecasts can be obtained for output growth and inflation. Averaging over both the models and the estimation windows improves the level of accuracy of the forecasts.     

A comparison of inflation forecasts

Interest rate models provide slightly better monthly forecasts and substantially better eight- and fourteen-month forecasts of inflation than a univariate time series model. The Livingston surveys underestimate eight- and fourteen-month inflation rates, especially during the high inflation period of 1978–81. In contrast, eight- and fourteen-month inflation forecasts extrapolated from one-month interest rates show little bias and track ex post eight- and fourteen-month inflation rates better than the survey forecasts.     

Disagreement and Biases in Inflation Expectations

Disagreement in inflation expectations observed from survey data varies systematically over time in a way that reflects the level and variance of current inflation. This paper offers a simple explanation for these facts based on asymmetries in the forecasters' costs of over- and underpredicting inflation. Our model implies (i) biased forecasts, (ii) positive serial correlation in forecast errors, (iii) a cross-sectional dispersion that rises with the level and the variance of the inflation rate, and (iv) predictability of forecast errors at different horizons by means of the spread between the short- and long-term variance of inflation. We find empirically that these patterns are present in inflation forecasts from the Survey of Professional Forecasters. A constant bias component, not explained by asymmetric loss and rational expectations, is required to explain the shift in the sign of the bias observed for a substantial portion of forecasters around 1982.     

Forecasting Changes in Copper Futures Volatility with GARCH Models Using an Iterated Algorithm

There is a gap in the literature regarding the out-of-sample forecasting ability of GARCH-type models applied to derivatives. A practitioner-oriented method (iterated cumulative sum of squares) is applied to detecting breakpoints in the variance of two copper futures series. Short-, intermediate-, and long-term out-of-sample forecasts of copper future series are compared to forecasts from a benchmark random walk model for each series. Not only do the GARCH-type models dominate the random walk model, but the relative improvement is fairly consistent across series, forecast horizon, and GARCH-type model. The evidence makes clear that, with few exceptions, the forecast improvement of the GARCH-type models over the RW model lies somewhere between 20–30%. It is particularly true that for the long-term close to close forecasts, there is great coherence among the forecasts. These all fall within a fairly narrow range.     

Survey forecasts in Brazil: A prismatic assessment of epidemiology,performance, and determinants

This paper assesses the behavior of survey forecasts in Brazil during the inflation targeting regime, when managing expectations is one of the cornerstones of the conduct of monetary policy. The distinctive database of the survey conducted by the Central Bank of Brazil (BCB) among professional forecasters allows for a thorough investigation of the epidemiology, determinants, and performance of forecasts. The main results are: i) top performing forecasters are influential to other forecasters; ii) survey forecasts perform better than vector autoregressive model-based forecasts; iii) common forecast errors prevail over idiosyncratic components across respondents; iv) inflation targets play an important role in inflation expectations; and v) agents perceive the BCB as following a Taylor rule consistent with inflation targeting. The last two suggest high credibility of the monetary authority.     

The term structure of inflation expectations

We use information in the term structure of survey-based forecasts of inflation to estimate a factor hidden in the nominal yield curve. We construct a model that accommodates forecasts over multiple horizons from multiple surveys and Treasury real and nominal yields by allowing for differences between risk-neutral, subjective, and objective probability measures. We establish that model-based inflation expectations are driven by inflation, output, and one latent factor. We find that this factor affects inflation expectations at all horizons but has almost no effect on the nominal yields; that is, the latent factor is hidden. We show that this hidden factor is not related to either current and past inflation or the standard set of macro variables studied in the literature. Consistent with the theoretical property of a hidden factor, our model outperforms a standard macro-finance model in its forecasting of inflation and yields.     

A New Model of Inflation,Trend Inflation,and Long‐Run Inflation Expectations

This paper develops a bivariate model of inflation and a survey‐based long‐run forecast of inflation that allows for the estimation of the link between trend inflation and the long‐run forecast. Thus, our model allows for the possibilities that long‐run forecasts taken from surveys can be equated with trend inflation, that the two are completely unrelated, or anything in between. Using a variety of inflation measures and survey‐based forecasts for several countries, we find that long‐run forecasts can provide substantial help in refining estimates and fitting and forecasting inflation. It is less helpful to simply equate trend inflation with the long‐run forecasts.     

Implicit Inflation and Risk Premiums in the Brazilian Fixed Income Market

The breakeven inflation, the differential between nominal and real yields of bonds, is often used as a predictor of future inflation. The model presented here decomposes this interest rate differential into a risk premium and implicit inflation using a parametric formulation based on no-arbitrage conditions using nominal and indexed yield curves in Brazil, via an affine model of the Nelson–Siegel family. The measures of implicit inflation obtained from the model are shown to be unbiased estimators of future inflation for short horizons and carry some information for long horizons, and the model forecasts are superior to market surveys.     

Extrapolations of yields over the short run: Forecast or folly?

This paper is concerned with the usefulness of extrapolations in short-term forecasts of long-term interest rates. Four extrapolative models were tested for accuracy in forecasting average yields of new issues of A-rated public utility bonds for forward periods of less than one year. The period 1953–1971 was chosen for study. The best model was found to be the one which assumed that each predicted yield was the same as the current yield at the time the prediction was made. Thus, the best forecasts were forecasts of no change in yields. This finding is supportive of the efficient capital-market thesis.     

预测中国宏观经济变量:专家与模型的组合预测

本文使用组合预测方法,探究以“朗润预测”为代表的专家预测以及计量模型对于中国宏观经济变量的预测效果,并研究对不同预测进行组合预测是否有助于改进预测效果。本文发现,对我国CPI和GDP的增长率,专家预测效果总体上优于模型预测。从原因看,一方面,专家在预测时已经考虑了计量模型的预测信息;另一方面,在经济出现“拐点”的时期,专家通过对实际经济环境和政策的把握,得出更准确的经济预测。组合预测有助于提升预测精度,对专家预测进行组合得到的预测效果优于大多数的专家预测,“模型—专家”组合预测的效果也优于所有的模型和大部分专家预测。     

Forecasting hedge fund volatility: a Markov regime-switching approach

The article addresses forecasting volatility of hedge fund (HF) returns by using a non-linear Markov-Switching GARCH (MS-GARCH) framework. The in- and out-of-sample, multi-step ahead volatility forecasting performance of GARCH(1,1) and MS-GARCH(1,1) models is compared when applied to 12 global HF indices over the period of January 1990 to October 2010. The results identify different regimes with periods of high and low volatility for most HF indices. In-sample estimation results reveal a superior performance of the MS-GARCH model. The findings show that regime switching is related to structural changes in the market factor for most strategies. Out-of-sample forecasting shows that the MS-GARCH formulation provides more accurate volatility forecasts for most forecast horizons and for most HF strategies. Inclusion of MS dynamics in the GARCH specification highly improves the volatility forecasts for those strategies that are particularly sensitive to general macroeconomic conditions, such as Distressed Restructuring and Merger Arbitrage.     

Stock returns and expected inflation: evidence from an asymmetric test specification

Previous studies that examined the relationship between stock returns and inflation have used a symmetric test specification, and have reported evidence of an inverse relation. We use an asymmetric model to re-examine this fundamental relationship between stock returns and inflation. We partition the study period into sub-samples of high and low inflation regimes. An inverse relation between stock returns and inflation forecasts is found during only low inflation periods, while a positive relation is detected through high inflation periods. In combination, results from both high and low inflation regimes suggest that stocks have delivered favorable inflation protection.      

Identifying the dynamics of real interest rates and inflation: evidence using survey data

In the context of an equilibrium asset-pricing model, the dynamicsof the instantaneous real interest rate and the instantaneousrate of expected inflation are estimated. Unlike previous models,we allow real interest rates and inflation to be mutually dependentprocesses. The model is estimated as a state-space system thatincludes observations on various maturity Treasury bills andNBER-ASA survey forecasts of inflation. Over the period 1968-1988,we find evidence that instantaneous real interest rates andexpected inflation are significantly negatively correlated.Real interest rates also display greater volatility and weakermean reversion than expected inflation.     

A ‘rational’ explanation for ‘irrational’ forecasts of inflation

This paper analyzes professionals' forecasts of nominal income and the inflation rate. The analysis indicates that both monetary and fiscal policy data was used efficiently to forecast income, but the monetary policy data was not used efficiently to forecast the inflation rate. Further analysis suggests that the apparent inefficient use of the monetary data results from the non-stationary money-inflation relationship predicted by rational expectations models. These models then provide an explanation for the inconsistent conclusions regarding the income forecasts and the inflation rate forecasts.     

The Taylor Rule and Forecast Intervals for Exchange Rates

In this paper, we examine the Meese–Rogoff puzzle from a different perspective: out‐of‐sample interval forecasting. While most studies in the literature focus on point forecasts, we apply semiparametric interval forecasting to a group of exchange rate models. Forecast intervals for 10 OECD exchange rates are generated and the performance of the empirical exchange rate models are compared with the random walk. Our contribution is twofold. First, we find that in general, exchange rate models generate tighter forecast intervals than the random walk, given that their intervals cover out‐of‐sample exchange rate realizations equally well. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting distributions of exchange rates. We also find that the benchmark Taylor rule model performs better than the monetary, PPP and forward premium models, and its advantages are more pronounced at longer horizons. Second, the bootstrap inference framework proposed in this paper for forecast interval evaluation can be applied in a broader context, such as inflation forecasting.     

An Empirical Evaluation of the Friedman Hypothesis of Inflation on Capital Asset Pricing

An empirical model incorporating the Friedman inflation uncertainty hypothesis is evaluated on capital asset pricing in conjunction with the covariance effect of the Fisher inflation hypothesis. The Fisher-Friedman capital asset pricing empirical model (FFCAPM) is compared to the capital asset pricing model (CAPM) and the Chen-Boness (C-B) model for explanatory power and significance over three periods. The FFCAPM performed better than the two competing models in explaining the variation in equity returns. The Friedman hypotheses of a positive economy-wide inflation adjustment and a negative inflation uncertainty impact are supported. A firm-specific inflation response of Fisher inflation covariance was also supported. These results indicate that empirical support for the Fisher effect may be limited given the normal testing procedure of simply adding an inflation term as in previous studies.     

Do Expected Shifts in Inflation Affect Estimates of the Long-Run Fisher Relation?

Recent empirical studies suggest that nominal interest rates and expected inflation do not move together one-for-one in the long run, a finding at odds with many theoretical models. This article shows that these results can be deceptive when the process followed by inflation shifts infrequently. We characterize the shifts in inflation by a Markov switching model. Based upon this model's forecasts, we reexamine the long-run relationship between nominal interest rates and inflation. Interestingly, we are unable to reject the hypothesis that in the long run nominal interest rates reflect expected inflation one-for-one.     

The heterogeneous impact of macroeconomic information on firms' earnings forecasts

This paper re-examines the role of macroeconomic information in forecasting firm earnings. We adopt a Fama–MacBeth regression model with the important extension of including information from over 140 macroeconomic variables that enter into the model in a reduced dimension form as a consequence of common factor analysis. The resulting factor-augmented model is then used to evaluate the importance of macroeconomic information on earnings forecasts for U.S. firms from 1962 to 2009. The same model is also examined for each individual time window and industry subsample. The results reveal a clear and heterogeneous impact of macroeconomic information on firm-specific earnings forecasts, and that these effects differ markedly during certain periods and across industries. In addition, when compared to analyst forecasts, we show that our model is generally more accurate over longer forecast horizons. The results of the identified heterogeneous impacts are used to define the conditions under which macroeconomic information becomes important for the firm.     

Impact of Adoption of Long-Term Performance Plans on Financial Analysts' Long-Term Forecasts

Prior research demonstrates that firms adopting long-term performance plans experience increased capital investment, earnings, and risk in the post-adoption period. However, these results are subject to distortions that may result from exogenous factors over the long time period examined. To avoid these potential distortions we examine financial analysts' forecasts in the periods immediately preceding and following the adoption of the performance plan. We find that projected long-term capital expenditures per share, earnings per share and cash flow per share are revised upward in the post-adoption period. These results are consistent with the premise that the adoption of long-term performance plans is expected to favorably affect managers' decisions. In addition, we find that the revisions are primarily attributable to firms that were performing poorly in the period prior to plan adoption and in greatest need of change. This finding has not been documented in previous studies.     

On the rationality and efficiency of inflation forecasts: Evidence from advanced and emerging market economies

This paper provides a full characterization of inflation rate forecasts using the mean values from Consensus Economics for a sample of 78 advanced and emerging economies between 1989 and 2014. It also assesses the performance of inflation rate forecasts around business cycles’ turning points. As expected, that inflation forecasts start to mirror the actual data as the forecast horizon draws to a close, particularly in advanced economies. The mean forecast error is positive and larger than one point when we pool all countries, but this masks inter-group differences. Moreover, we find evidence for biasedness, inefficiency or information rigidities, with a clear tendency for “forecast smoothing”. Accounting for cross-country informational linkages is important: forecasters fail to adjust their inflation forecasts quick enough in response to domestic news and news from abroad. Finally, during recession episodes forecasts generally appear to be inefficient. The same holds true for recoveries.