Forecasting with trees

The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition. This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks.     

提请印花税法草案的再思考

印花税,是对合同、凭证、收据、账簿及权利许可证件等征收税费的一类税种,也是古老的、为人所熟知的我国现行小税种之一.17世纪,资本主义政治经济的需要催生了印花税,自清朝末期我国首次引入印花税,历经100多年的演进,印花税蕴涵了更具价值的历史意义与现实意义.     

关于完善我国证券交易印花税的思考

随着我国证券业的发展和证券市场的扩大,证券交易印花税作为国家对资本市场进行调控的重要手段发挥了极其重要的作用.但是,证券交易印花税税率的频繁调整使得我国证券市场具有浓厚的行政干预特点,这与我国市场经济改革是不相适宜的,也不利于我国资本市场的健康发展.笔者认为,应当从全面完善证券税收法律体系、对证券的发行和流通环节分别开征印花税和证券交易税、将征税范围扩展到全部证券交易品种、实行差别税率并开征差异性证券交易税、以资本利得税逐步取代证券交易印花税等方面改进和完善我国证券交易印花税制度.     

Forecasting inflation with online prices

Are survey-based forecasts unbeatable? They are not. This paper uses online price indices to forecast the Consumer Price Index. We find that online price indices anticipate changes in official inflation trends more than one month in advance. Our baseline one-month forecast outperforms Bloomberg surveys of forecasters, which only predict the contemporaneous inflation rate. Our baseline specification also outperforms statistical benchmark forecasts for Australia, Canada, France, Germany, Greece, Ireland, Italy, the Netherlands, the United Kingdom, and the United States. Similarly, our quarterly forecast for the US inflation rate substantially outperforms the Survey of Professional Forecasters.     

关于完善我国证券交易印花税的思考

随着我国证券业的发展和证券市场的扩大,证券交易印花税作为国家对资本市场进行调控的重要手段发挥了极其重要的作用。但是,证券交易印花税税率的频繁调整使得我国证券市场具有浓厚的行政干预特点,这与我国市场经济改革是不相适宜的,也不利于我国资本市场的健康发展。笔者认为,应当从全面完善证券税收法律体系、对证券的发行和流通环节分别开征印花税和证券交易税、将征税范围扩展到全部证券交易品种、实行差别税率并开征差异性证券交易税、以资本利得税逐步取代证券交易印花税等方面改进和完善我国证券交易印花税制度。     

Forecasting with factor-augmented error correction models

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.     

Forecasting with nonstationary dynamic factor models

In this paper we analyze the structure and the forecasting performance of the dynamic factor model. It is shown that the forecasts obtained by the factor model imply shrinkage pooling terms, similar to the ones obtained from hierarchical Bayesian models that have been applied successfully in the econometric literature. Thus, the results obtained in this paper provide an additional justification for these and other types of pooling procedures. The expected decrease in MSE for using a factor model versus univariate ARIMA and shrinkage models are studied for the one factor model. Monte Carlo simulations are presented to illustrate this result. A factor model is also built to forecast GNP of European countries and it is shown that the factor model can provide a substantial improvement in forecasts with respect to both univariate and shrinkage univariate forecasts.     

Forecasting interest rates with shifting endpoints

We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time‐varying mean or ‘shifting endpoint’. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii) long‐range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out‐of‐sample predictive accuracy, relative to stationary and random walk benchmarks. Forecast improvements are largest for long‐maturity interest rates and for long‐horizon forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting aggregates and disaggregates with common features

This paper focuses on the provision of consistent forecasts for an aggregate economic indicator, such as a consumer price index and its components. The procedure developed is a disaggregated approach based on single-equation models for the components, which take into account the stable features that some components share, such as a common trend and common serial correlation. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of this classification is a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We use the procedure to forecast inflation in the Euro area, the UK and the US. Our forecasts are significantly more accurate than either a direct forecast of the aggregate or various other indirect forecasts.     

Forecasting with equilibrium-correction models during structural breaks

When location shifts occur, cointegration-based equilibrium-correction models (EqCMs) face forecasting problems. We consider alleviating such forecast failure by updating, intercept corrections, differencing, and estimating the future progress of an ‘internal’ break. Updating leads to a loss of cointegration when an EqCM suffers an equilibrium-mean shift, but helps when collinearities are changed by an ‘external’ break with the EqCM staying constant. Both mechanistic corrections help compared to retaining a pre-break estimated model, but an estimated model of the break process could outperform. We apply the approaches to EqCMs for UK M1, compared with updating a learning function as the break evolves.     

Forecasting in dynamic models with stochastic regressors

When a dependent variable y is related to present and past values of an exogenous variable x in a dynamic regression (distributed lag) model, and when x must be forecast in order to forecast y, necessary and sufficient conditions are derived in order for optimal forecasts of y to possess lower mean square error as a result of including x in the model, relative to forecasting y solely from its own past. The contribution to this forecast MSE reduction of non-invertibility in the lag distribution is assessed. Examples from econometrics and engineering are provided to illustrate the results.     

Forecasting crude oil prices with DSGE models

In this study, we conducted an oil prices forecasting competition among a set of structural models, including vector autoregression and dynamic stochastic general equilibrium (DSGE) models. Our results highlight two principles. First, forecasts should exploit the fact that real oil prices are mean reverting over long horizons. Second, models should not replicate the high volatility of the oil prices observed in samples. By following these principles, we show that an oil sector DSGE model performs much better at real oil price forecasting than random walk or vector autoregression.     

Forecasting economic activity with mixed frequency BVARs

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate large numbers of time series that are observed at different intervals into forecasts of economic activity. This paper benchmarks the performances of MF-BVARs for forecasting U.S. real gross domestic product growth against surveys of professional forecasters and documents the influences of certain specification choices. We find that a medium–large MF-BVAR provides an attractive alternative to surveys at the medium-term forecast horizons that are of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices influence its performance strongly, such as model size, prior selection mechanisms, and modeling in levels versus growth rates.     

Forecasting with Medium and Large Bayesian VARS

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases factor methods have been traditionally used, but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic dataset containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Typically, we find that the simple Minnesota prior forecasts well in medium and large VARs, which makes this prior attractive relative to computationally more demanding alternatives. Our empirical results show the importance of using forecast metrics based on the entire predictive density, instead of relying solely on those based on point forecasts. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

Forecasting with news sentiment: Evidence with UK newspapers

We investigate the performance of newspapers for forecasting inflation, output and unemployment in the United Kingdom. We concentrate on whether the economic policy content reported in popular printed media can improve on existing point forecasts. We find no evidence supporting improved nowcasts or short-term forecasts for inflation. The sentiment inferred from printed media, can however be useful for forecasting unemployment and output. Considerable improvements are also noted when using individual newspapers and keyword based indices.     

Forecasting economic and financial variables with global VARs

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.     

Forecasting inflation in Latin America with core measures

We explore the ability of core inflation to predict headline CPI annual inflation for a sample of eight developing economies in Latin America over the period January 1995–May 2017. Our in-sample and out-of-sample results are roughly consistent in providing robust evidence of predictability in four of the countries in our sample. Mixed evidence is found for the other four countries. The bulk of the out-of-sample evidence of predictability concentrates on the short horizons of one and six months. In contrast, at the longest horizon of 24 months, we only find out-of-sample evidence of predictability for two countries: Chile and Colombia, with robust results only for the latter. This is both important and challenging, given that the monetary authorities in our sample of developing countries are currently implementing or are taking steps toward the future implementation of inflation targeting regimes, which are based heavily on long-run inflation forecasts.