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实时数据下样本外预测能力检验的自助法AI 解读

In this paper we develop a block bootstrap approach to out-of-sample inference when real-time data are used to produce forecasts. In particular, we establish its first-order asymptotic validity for West-type (1996) tests of predictive ability in the presence of regular data revisions. This allows the user to conduct asymptotically valid inference without having to estimate the asymptotic variances derived in Clark and McCracken’s (2009) extension of West (1996) when data are…

金融状况指数对经济增长的实时预测信息AI 解读

Summary We provide evidence on the real‐time predictive content of the National Financial Conditions Index (NFCI), for conditional quantiles of U.S. real GDP growth. Our work is distinct from the literature in two specific ways. First, we construct (unofficial) real‐time vintages of the NFCI. This allows us to conduct out‐of‐sample analysis without introducing the kind of look‐ahead biases that are naturally introduced when using a single current vintage. We then develop…

二元变量的条件预测AI 解读

While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this article, we merge elements from the literature on the construction and implementation of conditional forecasts with the literature on forecasting binary variables. We use the Qual-VAR, whose joint VAR-probit structure allows us to form conditional forecasts of…

多期限预测误差的时变不确定性建模AI 解读

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to simple variance approaches, our stochastic…

We investigate claims made in Giacomini and White (2006) and Diebold (2015) regarding the asymptotic normality of a test of equal predictive ability. A counterexample is provided in which, instead, the test statistic diverges with probability 1 under the null.

直接与迭代多步条件预测的实证比较AI 解读

Summary When constructing unconditional point forecasts, both direct and iterated multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions are far more common than simpler DMS models. This is despite the fact that there are theoretical reasons to believe that DMS models are more robust to misspecification than are IMS models. In the context of unconditional forecasts, Marcellino…

业绩费与资产收益可预测性的渐近推断AI 解读

In this article, we provide analytical, simulation, and empirical evidence on a test of equal economic value from competing predictive models of asset returns. We define economic value using the concept of a performance fee—the amount an investor would be willing to pay to have access to an alternative predictive model used to make investment decisions. We establish that this fee can be asymptotically normal under modest assumptions. Monte Carlo evidence shows that our test…

含估计因子的嵌套模型等预测精度检验AI 解读

In this paper we develop asymptotics for tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. We provide conditions under which the estimation of the factors does not affect the asymptotic distributions developed in Clark and McCracken (2001) and McCracken (2007). This enables researchers to use the existing tabulated critical values when conducting inference despite the presence of estimated predictors. As an…

用于条件预测的向量自回归模型预测能力检验AI 解读

Summary Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we examine…

嵌套预测模型比较：检验预测精度相等的新方法AI 解读

We develop methods for testing whether, in a finite sample, forecasts from nested models are equally accurate. Most prior work has focused on a null of equal accuracy in population — basically, whether the additional coefficients of the larger model are zero. Our asymptotic approximation instead treats the coefficients as non-zero but small, such that, in a finite sample, forecasts from the small and large models are expected to be equally accurate. We derive the limiting…

重叠模型的等预测准确性检验AI 解读

SUMMARY This paper examines the asymptotic and finite‐sample properties of tests of equal forecast accuracy when the models being compared are overlapping in the sense of Vuong ( Econometrica 1989; 57 : 307–333). Two models are overlapping when the true model contains just a subset of variables common to the larger sets of variables included in the competing forecasting models. We consider an out‐of‐sample version of the two‐step testing procedure recommended by Vuong but…

预测能力的样本内检验：一种新方法AI 解读

This paper presents evidence linking in-sample tests of predictive content and out-of-sample forecast accuracy. Our approach focuses on the negative effect that finite-sample estimation error has on forecast accuracy despite the presence of significant population-level predictive content. We derive in-sample tests that assess whether a variable has predictive content and whether this content is estimated precisely enough to improve forecast accuracy. Our tests are…

嵌套预测模型的现实检验与比较AI 解读

This article develops a simple bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. Our method combines elements of fixed regressor and wild bootstraps. We first derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to forecasts from multiple models that nest the benchmark model—that is, reality check tests. We then prove the validity of the…

存在不确定不稳定性时的 VAR 预测平均AI 解读

Abstract Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve forecast accuracy. Focusing on models of US output, prices, and interest…

实时数据下的预测能力相等检验AI 解读

This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multistep predictions from both nonnested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with…

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