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1.
RmuloA Chumacero 《Journal of forecasting》2001,20(1):37-45
This paper presents a statistical comparison between the actual and predicted evolution of the Chilean GDP for the period 1986 – 1998 made by several forecasters. We show that the forecasters systematically underestimate the true growth rate of the economy. The magnitude of this bias tends to be correlated with the phase of the business cycle. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
2.
Standard measures of prices are often contaminated by transitory shocks. This has prompted economists to suggest the use of measures of underlying inflation to formulate monetary policy and assist in forecasting observed inflation. Recent work has concentrated on modelling large data sets using factor models. In this paper we estimate factors from data sets of disaggregated price indices for European countries. We then assess the forecasting ability of these factor estimates against other measures of underlying inflation built from more traditional methods. The power to forecast headline inflation over horizons of 12 to 18 months is adopted as a valid criterion to assess forecasting. Empirical results for the five largest euro area countries, as well as for the euro area itself, are presented. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
3.
A new multivariate stochastic volatility model is developed in this paper. The main feature of this model is to allow threshold asymmetry in a factor covariance structure. The new model provides a parsimonious characterization of volatility and correlation asymmetry in response to market news. Statistical inferences are drawn from Markov chain Monte Carlo methods. We introduce news impact analysis to analyze volatility asymmetry with a factor structure. This analysis helps us to study different responses of volatility to historical market information in a multivariate volatility framework. Our model is successful when applied to an extensive empirical study of twenty stocks. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
4.
We propose a new framework for building composite leading indicators for the Spanish economy using monthly targeted predictors and small‐scale dynamic factor models. Our leading indicator index, based on the low‐frequency components of four monthly economic variables, is able to predict the onset of the Spanish recessions as well as the gross domestic product (GDP) growth cycles and classical industrial production cycles, both historically and in real time. Also, our leading indicator provides substantial aid in forecasting annual and quarterly GDP growth rates. Using only real data available at the beginning of each forecast period, our indicator one‐step‐ahead forecasts shows substantial improvements over other alternatives. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
5.
This paper presents short‐ and long‐term composite leading indicators (CLIs) of underlying inflation for seven EU countries, namely Belgium, Germany, France, Italy, the Netherlands, Sweden and the UK. CLI and CPI reference series are calculated in terms of both growth rates and in deviations from its trend. The composite leading indicators are based on leading basic series, such as sources of inflation, series containing information on inflation expectations and prices of intermediate goods and services. Neftci's decision rule approach has been applied to transfer movements in the CLIs into a measure of the probability of a cyclical turning point, which enables the screening out of false turning point predictions. Finally, CLIs have been used to analyse the international coherence of price cycles. The forecast performance of CLIs of inflation over the past raises hope that this forecast instrument can be useful in predicting future price movements. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
6.
Patricia Toledo;Roberto Duncan; 《Journal of forecasting》2024,43(4):1087-1113
In this paper, we consider the forecasting of domestic food price inflation (DFPI) using global indicators, with emphasis on episodes of macroeconomic turbulence, namely, the Global Financial Crisis (GFC) and the COVID-19 pandemic and its subsequent repercussions. Our monthly dataset covers about two decades for more than a hundred economies. We employ dynamic model averaging (DMA) to tackle both model uncertainty and parameter instability and produce pseudo out-of-sample forecasts. Thus, we are able to focus on the forecasting ability of the global predictors of DFPI before and during the global crises. We find evidence that the DMA specification tends to outperform statistical models frequently used in the literature such as random walks, autoregressive models, and time-varying parameter models, especially during global crises. We also identify the most successful predictors during the crises using their posterior probabilities of inclusion. By comparing the distributions of such probabilities, we find that the international food price inflation is the most useful predictor of DFPI for numerous countries during both crises. Other indicators such as domestic CPI inflation as well as the international inflation of agricultural commodities, fertilizers, and other food categories improved their forecasting ability, particularly during the COVID-19 period. 相似文献
7.
Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk. 相似文献
8.
The leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model. 相似文献
9.
This paper uses an extension of the Euro‐Sting single‐index dynamic factor model to construct short‐term forecasts of quarterly GDP growth for the euro area by accounting for financial variables as leading indicators. From a simulated real‐time exercise, the model is used to investigate the forecasting accuracy across the different phases of the business cycle. Our extension is also used to evaluate the relative forecasting ability of the two most reliable business cycle surveys for the euro area: the PMI and the ESI. We show that the latter produces more accurate GDP forecasts than the former. Finally, the proposed model is also characterized by its great ability to capture the European business cycle, as well as the probabilities of expansion and/or contraction periods. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
10.
We develop a small model for forecasting inflation for the euro area using quarterly data over the period June 1973 to March 1999. The model is used to provide inflation forecasts from June 1999 to March 2002. We compare the forecasts from our model with those derived from six competing forecasting models, including autoregressions, vector autoregressions and Phillips‐curve based models. A considerable gain in forecasting performance is demonstrated using a relative root mean squared error criterion and the Diebold–Mariano test to make forecast comparisons. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
11.
Marius M. Mihai 《Journal of forecasting》2020,39(6):887-910
This paper investigates the role of bank credit in predicting US recessions since the 1960s in the context of a bivariate probit model. A set of results emerge. First, credit booms are shown to have strong positive effects in predicting declines in the business cycle at horizons ranging from 6 to 9 months. Second, I propose to isolate the effect of credit booms by identifying the contribution of excess bank liquidity alongside a housing factor in the downturn of each cycle. Third, the out-of-sample performance of the model is tested on the most recent credit-driven recession: the Great Recession of 2008. The model performs better than a more parsimonious version where we restrict the effect of credit booms on the business cycle in the system to be zero. 相似文献
12.
Christian Hotz‐Behofsits Florian Huber Thomas Otto Zörner 《Journal of forecasting》2018,37(6):627-640
In this paper we forecast daily returns of crypto‐currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non‐normality of the measurement errors and sharply increasing trends, we develop a time‐varying parameter VAR with t‐distributed measurement errors and stochastic volatility. To control for overparametrization, we rely on the Bayesian literature on shrinkage priors, which enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data, we perform a real‐time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we, moreover, run a simple trading exercise. 相似文献
13.
We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
14.
Jianmin Shi 《Journal of forecasting》2016,35(3):250-262
Model uncertainty and recurrent or cyclical structural changes in macroeconomic time series dynamics are substantial challenges to macroeconomic forecasting. This paper discusses a macro variable forecasting methodology that combines model uncertainty and regime switching simultaneously. The proposed predictive regression specification permits both regime switching of the regression parameters and uncertainty about the inclusion of forecasting variables by employing Bayesian model averaging. In an empirical exercise involving quarterly US inflation, we observed that our Bayesian model averaging with regime switching leads to substantial improvements in forecast performance, particularly in the medium horizon (two to four quarters). Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
15.
We use state space methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2–2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
16.
This paper investigates robust model rankings in out‐of‐sample, short‐horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) “optimal” window forecasts in more than 50% of the times across all rolling windows. 相似文献
17.
Barış Soybilgen 《Journal of forecasting》2020,39(5):827-840
We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time. 相似文献
18.
Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model 
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下载免费PDF全文 A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasticity model, to investigate volatility spillover. The model specification is parsimonious and can be used to analyze the time variation property of the spillover effect. Our in‐sample evidence shows the existence of strong volatility spillover from the US to five major stock markets and indicates that the spillover was stronger during business cycle recessions in the USA. Out‐of‐sample results show that accounting for spillover information from the USA can significantly improve the forecasting accuracy of international stock price volatility. 相似文献
19.
Estimation of the value at risk (VaR) requires prediction of the future volatility. Whereas this is a simple task in ARCH and related models, it becomes much more complicated in stochastic volatility (SV) processes where the volatility is a function of a latent variable that is not observable. In-sample (present and past values) and out-of-sample (future values) predictions of that unobservable variable are thus necessary. This paper proposes singular spectrum analysis (SSA), which is a fully nonparametric technique that can be used for both purposes. A combination of traditional forecasting techniques and SSA is also considered to estimate the VaR. Their performance is assessed in an extensive Monte Carlo and with an application to a daily series of S&P500 returns. 相似文献
20.
Qianjie Geng;Xianfeng Hao;Yudong Wang; 《Journal of forecasting》2024,43(2):309-325
Parameter instability and model uncertainty are two key problems affecting forecasting outcomes. In this paper, we propose a time-dependent weighted least squares with ridge constraint (TWLS-Ridge) to solve the above two problems in the forecasting procedure. The new TWLS-Ridge approach is applied to the heterogenous autoregressive realized volatility model and its various extensions. The empirical results suggest that TWLS-Ridge produces more accurate volatility forecasts than several alternative models dealing with parameter instability and model uncertainty. The superior performance of TWLS-Ridge is robust under different forecast horizons, evaluation periods, and loss functions. An investor with mean–variance preference can improve utility using TWLS-Ridge forecasts of oil volatility instead of ordinary least squares model forecasts. 相似文献