While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. To do so, we construct factor models to forecast Japanese GDP and its subcomponents, using 38 data series (including daily, monthly and quarterly variables) over the period 1991 to 2010. Overall, we find that factor models perform well at tracking GDP movements and anticipating turning points. For most of the components, we report that factor models yield lower forecasting errors than a simple AR process or an indicator model based on Purchasing Managers' Indicators (PMIs). In line with previous studies, we conclude that the largest improvements in terms of forecasting accuracy are found for more volatile periods, such as the recent financial crisis. However, unlike previous studies, we do not find evident links between the volatility of the components and the relative advantage of using factor models. Finally, we show that adding the PMI index as an independent explanatory variable improves the forecasting properties of the factor models. JEL Classification: C50, C53, E37, E47
{"title":"Short-Term Forecasting of the Japanese Economy Using Factor Models","authors":"Claudia Godbout, Marco J. Lombardi","doi":"10.2139/ssrn.2281770","DOIUrl":"https://doi.org/10.2139/ssrn.2281770","url":null,"abstract":"While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. To do so, we construct factor models to forecast Japanese GDP and its subcomponents, using 38 data series (including daily, monthly and quarterly variables) over the period 1991 to 2010. Overall, we find that factor models perform well at tracking GDP movements and anticipating turning points. For most of the components, we report that factor models yield lower forecasting errors than a simple AR process or an indicator model based on Purchasing Managers' Indicators (PMIs). In line with previous studies, we conclude that the largest improvements in terms of forecasting accuracy are found for more volatile periods, such as the recent financial crisis. However, unlike previous studies, we do not find evident links between the volatility of the components and the relative advantage of using factor models. Finally, we show that adding the PMI index as an independent explanatory variable improves the forecasting properties of the factor models. JEL Classification: C50, C53, E37, E47","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125195612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Measuring the quantitative effects of monetary policy on the economy has been playing a central role in promoting economic growth and stability. However, in the presence of numerous macroeconomic variables, traditional vector autoregression (VAR) could only accommodate a few data series, and thus may ignore the information set which is actually observed by central banks and financial market participants. In this paper, we propose a novel VAR model with the aid of new developments in high-dimensional statistical inference. Our approach could handle hundreds of observed data series simultaneously, and increase the prediction accuracy as well as the robustness of monetary policy analysis in a data-rich environment. It has been shown that our model outperforms factor-augmented VAR in terms of in-sample-fit and out-of-sample forecasts. Moreover, impulse responses are observed for all macroeconomic variables, where “price puzzle”, a commonly observed empirical anomaly, is resolved.
{"title":"Monetary Policy Analysis Based on Lasso-Assisted Vector Autoregression (Lavar)","authors":"Jiahan Li","doi":"10.2139/ssrn.2017877","DOIUrl":"https://doi.org/10.2139/ssrn.2017877","url":null,"abstract":"Measuring the quantitative effects of monetary policy on the economy has been playing a central role in promoting economic growth and stability. However, in the presence of numerous macroeconomic variables, traditional vector autoregression (VAR) could only accommodate a few data series, and thus may ignore the information set which is actually observed by central banks and financial market participants. In this paper, we propose a novel VAR model with the aid of new developments in high-dimensional statistical inference. Our approach could handle hundreds of observed data series simultaneously, and increase the prediction accuracy as well as the robustness of monetary policy analysis in a data-rich environment. It has been shown that our model outperforms factor-augmented VAR in terms of in-sample-fit and out-of-sample forecasts. Moreover, impulse responses are observed for all macroeconomic variables, where “price puzzle”, a commonly observed empirical anomaly, is resolved.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127530547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an empirical comparative study of di fferent covariance estimators. The Engle-Colacito test is used for an indirect evaluation of alternative out-of-sample covariance forecasts in a portfolio setting for varying sample sizes, short selling constraints and market conditions. Errors in the estimation of variances have a higher impact on realized portfolio variance than errors in the estimation of covariances. Bayesian shrinkage estimators and the orthogonal GARCH estimator of covariance matrices lead to signi ficantly lower realized portfolio volatility compared to benchmark estimators.
{"title":"Evaluating Covariance Forecasts Via Mean-Variance Portfolio Decisions","authors":"M. Franke","doi":"10.2139/ssrn.1986708","DOIUrl":"https://doi.org/10.2139/ssrn.1986708","url":null,"abstract":"This paper presents an empirical comparative study of di fferent covariance estimators. The Engle-Colacito test is used for an indirect evaluation of alternative out-of-sample covariance forecasts in a portfolio setting for varying sample sizes, short selling constraints and market conditions. Errors in the estimation of variances have a higher impact on realized portfolio variance than errors in the estimation of covariances. Bayesian shrinkage estimators and the orthogonal GARCH estimator of covariance matrices lead to signi ficantly lower realized portfolio volatility compared to benchmark estimators.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The application of quantitative statistical modelling in the commuter rail environment is explored in this research paper. The research explored the application of various time series models as well as the ARIMA model and regression analysis. The application of two forecast combinations was also explored to improve the accuracy of the forecasts. The ARIMA model in combination with the seasonal decomposition was used to forecast the data for a period of 18 months.
{"title":"Forecasting Ticket Sales – the Case of Commuter Rail in South Africa","authors":"J. Kruger, Anna-Marie","doi":"10.2139/ssrn.1935196","DOIUrl":"https://doi.org/10.2139/ssrn.1935196","url":null,"abstract":"The application of quantitative statistical modelling in the commuter rail environment is explored in this research paper. The research explored the application of various time series models as well as the ARIMA model and regression analysis. The application of two forecast combinations was also explored to improve the accuracy of the forecasts. The ARIMA model in combination with the seasonal decomposition was used to forecast the data for a period of 18 months.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lennart F. Hoogerheide, F. Ravazzolo, H. K. van Dijk
Patton and Timmermann (2011, 'Forecast Rationality Tests Based on Multi-Horizon Bounds', Journal of Business & Economic Statistics, forthcoming) propose a set of useful tests for forecast rationality or optimality under squared error loss, including an easily implemented test based on a regression that only involves (long-horizon and short-horizon) forecasts and no observations on the target variable. We propose an extension, a simulation-based procedure that takes into account the presence of errors in parameter estimates. This procedure can also be applied in the field of 'backtesting' models for Value-at-Risk. Applications to simple AR and ARCH time series models show that its power in detecting certain misspecifications is larger than the power of well-known tests for correct Unconditional Coverage and Conditional Coverage.
{"title":"Backtesting Value-at-Risk Using Forecasts for Multiple Horizons, a Comment on the Forecast Rationality Tests of A.J. Patton and A. Timmermann","authors":"Lennart F. Hoogerheide, F. Ravazzolo, H. K. van Dijk","doi":"10.2139/ssrn.1930841","DOIUrl":"https://doi.org/10.2139/ssrn.1930841","url":null,"abstract":"Patton and Timmermann (2011, 'Forecast Rationality Tests Based on Multi-Horizon Bounds', Journal of Business & Economic Statistics, forthcoming) propose a set of useful tests for forecast rationality or optimality under squared error loss, including an easily implemented test based on a regression that only involves (long-horizon and short-horizon) forecasts and no observations on the target variable. We propose an extension, a simulation-based procedure that takes into account the presence of errors in parameter estimates. This procedure can also be applied in the field of 'backtesting' models for Value-at-Risk. Applications to simple AR and ARCH time series models show that its power in detecting certain misspecifications is larger than the power of well-known tests for correct Unconditional Coverage and Conditional Coverage.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"49 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114125754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boochun Jung, Philip B. Shane, Yanhua Sunny Sunny Yang
Prior literature portrays long-term growth (LTG) forecasts as nonsensical from a valuation perspective. Instead, we hypothesize that LTG forecasts signal high effort and ability to analyze firms' long-term prospects. We document stronger market response to stock recommendation revisions of analysts who publish accompanying LTG forecasts. We also hypothesize and find that these analysts are less likely to leave the profession or move to smaller brokerage houses. Consistent with Reg. FD's intention to promote fundamental analysis of long-term earnings prospects, post-Reg. FD observations drive our results. Overall, we identify previously undocumented benefits accruing to analysts who publish LTG forecasts.
{"title":"Do Financial Analysts’ Long-Term Growth Forecasts Matter? Evidence from Stock Recommendations and Career Outcomes","authors":"Boochun Jung, Philip B. Shane, Yanhua Sunny Sunny Yang","doi":"10.2139/ssrn.1927606","DOIUrl":"https://doi.org/10.2139/ssrn.1927606","url":null,"abstract":"Prior literature portrays long-term growth (LTG) forecasts as nonsensical from a valuation perspective. Instead, we hypothesize that LTG forecasts signal high effort and ability to analyze firms' long-term prospects. We document stronger market response to stock recommendation revisions of analysts who publish accompanying LTG forecasts. We also hypothesize and find that these analysts are less likely to leave the profession or move to smaller brokerage houses. Consistent with Reg. FD's intention to promote fundamental analysis of long-term earnings prospects, post-Reg. FD observations drive our results. Overall, we identify previously undocumented benefits accruing to analysts who publish LTG forecasts.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122451618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.
{"title":"Forecasting with Approximate Dynamic Factor Models: The Role of Non-Pervasive Shocks","authors":"Matteo Luciani","doi":"10.2139/ssrn.1925807","DOIUrl":"https://doi.org/10.2139/ssrn.1925807","url":null,"abstract":"This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128154537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examine how to forecast after a recent break. We consider monitoring for change and then combining forecasts from models that do and do not use data before the change; and robust methods, namely rolling regressions, forecast averaging over different windows and exponentially weighted moving average (EWMA) forecasting. We derive analytical results for the performance of the robust methods relative to a full-sample recursive benchmark. For a location model subject to stochastic breaks the relative MSFE ranking is EWMA < rolling regression < forecast averaging. No clear ranking emerges under deterministic breaks. In Monte Carlo experiments forecast averaging improves performance in many cases with little penalty where there are small or infrequent changes. Similar results emerge when we examine a large number of UK and US macroeconomic series.
{"title":"Forecasting in the Presence of Recent Structural Change","authors":"Jana Eklund, G. Kapetanios, Simon Price","doi":"10.2139/ssrn.1888726","DOIUrl":"https://doi.org/10.2139/ssrn.1888726","url":null,"abstract":"We examine how to forecast after a recent break. We consider monitoring for change and then combining forecasts from models that do and do not use data before the change; and robust methods, namely rolling regressions, forecast averaging over different windows and exponentially weighted moving average (EWMA) forecasting. We derive analytical results for the performance of the robust methods relative to a full-sample recursive benchmark. For a location model subject to stochastic breaks the relative MSFE ranking is EWMA < rolling regression < forecast averaging. No clear ranking emerges under deterministic breaks. In Monte Carlo experiments forecast averaging improves performance in many cases with little penalty where there are small or infrequent changes. Similar results emerge when we examine a large number of UK and US macroeconomic series.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132120848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Conference Board’s Leading Economic Indicators Index suffers from construction flaws, which reduce its predictive power as well as one’s ability to interpret its signals. This paper develops a vector autoregression model to address these problems. The model’s out-of-sample GDP forecasts, using revised data, are found to outperform other private-sector forecasters on average over the period considered.
{"title":"Forecasting GDP with the Leading Indicators: A VAR Approach","authors":"R. Kahan","doi":"10.2139/ssrn.2606393","DOIUrl":"https://doi.org/10.2139/ssrn.2606393","url":null,"abstract":"The Conference Board’s Leading Economic Indicators Index suffers from construction flaws, which reduce its predictive power as well as one’s ability to interpret its signals. This paper develops a vector autoregression model to address these problems. The model’s out-of-sample GDP forecasts, using revised data, are found to outperform other private-sector forecasters on average over the period considered.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115901499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We looked at evidence from comparative empirical studies to identify methods that can be useful for predicting demand in various situations and to warn against methods that should not be used. In general, use structured methods and avoid intuition, unstructured meetings, focus groups, and data mining. In situations where there are sufficient data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Otherwise, use methods that structure judgement including surveys of intentions and expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Managers' domain knowledge should be incorporated into statistical forecasts. Methods for combining forecasts, including Delphi and prediction markets, improve accuracy. We provide guidelines for the effective use of forecasts, including such procedures as scenarios. Few organizations use many of the methods described in this paper. Thus, there are opportunities to improve efficiency by adopting these forecasting practices.
{"title":"Demand Forecasting: Evidence-Based Methods","authors":"K. Green, J. Armstrong","doi":"10.2139/ssrn.3063308","DOIUrl":"https://doi.org/10.2139/ssrn.3063308","url":null,"abstract":"We looked at evidence from comparative empirical studies to identify methods that can be useful for predicting demand in various situations and to warn against methods that should not be used. In general, use structured methods and avoid intuition, unstructured meetings, focus groups, and data mining. In situations where there are sufficient data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Otherwise, use methods that structure judgement including surveys of intentions and expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Managers' domain knowledge should be incorporated into statistical forecasts. Methods for combining forecasts, including Delphi and prediction markets, improve accuracy. We provide guidelines for the effective use of forecasts, including such procedures as scenarios. Few organizations use many of the methods described in this paper. Thus, there are opportunities to improve efficiency by adopting these forecasting practices.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130135204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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