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利用非线性协整估计货币需求函数AI 解读

Abstract Conventionally, the money demand function is estimated using a regression of the logarithm of money demand on either the interest rate or the logarithm of the interest rate. This equation is presumed to be a cointegrating regression. In this paper, we aim to combine the logarithmic specification, which models the liquidity trap better than a linear model, with the assumption that the interest rate itself is an integrated process. The proposed technique is robust to…

嵌套模型预测准确性相等的近似正态检验AI 解读

Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for…

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概率预测、校准与集中度AI 解读

Summary Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the…

大缓和时期的宏观波动、可预测性与不确定性AI 解读

I examine the extent to which the large decline in observed real output volatility, that is, the great moderation, can be attributed to changes in macroeconomic uncertainty and macroeconomic predictability using forecasts of future real output growth from the Survey of Professional Forecasters (SPF). The results indicate that both predictability and uncertainty have declined over the great moderation. The results indicate that measuring the decline in macroeconomic…

基于市场的货币政策预期度量AI 解读

A number of recent articles have used different financial market instruments to measure near-term expectations of the federal funds rate and the high-frequency changes in these instruments around Federal Open Market Committee announcements to measure monetary policy shocks. This article evaluates the empirical success of a variety of financial market instruments in predicting the future path of monetary policy. All of the instruments we consider provide forecasts that are…

用加权似然比检验比较密度预测AI 解读

We propose a test for comparing the out-of-sample accuracy of competing density forecasts of a variable. The test is valid under general conditions: The data can be heterogeneous and the forecasts can be based on (nested or nonnested) parametric models or produced by semiparametric, nonparametric, or Bayesian estimation techniques. The evaluation is based on scoring rules, which are loss functions defined over the density forecast and the realizations of the variable. We…

利用半参数贝叶斯方法预测美国癌症死亡人数AI 解读

We present two models for the short-term prediction of the number of deaths arising from common cancers in the United States. The first is a local linear model, in which the slope of the segment joining the number of deaths for any two consecutive time periods is assumed to be random with a nonparametric distribution, which has a Dirichlet process prior. For slightly longer prediction periods, we present a local quadratic model. This extension of the local linear model…

社会资本、生产壁垒与资本份额：非平稳面板中的参数异质性AI 解读

Abstract Recent advances in the growth literature have proposed that difficult‐to‐quantify concepts such as social capital may play an important role in explaining the degree of persistent income disparity that is observed among countries. Other recently explored possibilities include institutional mechanisms which generate barriers to aggregate production. An important limitation for empirical work in this area stems from the fact that it is difficult to distinguish sources…

关于共同周期、共同趋势与收敛的札记AI 解读

This article compares and contrasts structural time series models and the common features methodology. The way in which trends are handled is highlighted by describing a recent structural time series model that allows convergence to a common growth path. Postsample data are used to test its forecasting performance for income per head in U.S. regions. A test for common cycles is proposed, its asymptotic distribution is given, and small-sample properties are studied by Monte…

更及时、更有用的先行指标指数AI 解读

Effectively predicting cyclical movements in the economy is a major challenge. The U.S. leading index (LI) has long been used to analyze and predict economic fluctuations. We describe and test a new procedure for making the LI more timely. The new LI significantly outperforms its older counterpart. It offers substantial gains in real-time, out-of-sample forecasts of changes in aggregate economic activity (real GDP, the index of coincident indicators, and industrial…

澳大利亚股票收益的波动率预测AI 解读

This article develops multivariate factor models for forecasting volatility in Australian stocks. We suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large, and also work with volatility measures that have been constructed so that they contain no jump components. Out-of-sample forecast analysis shows that multivariate factor models of volatility outperform univariate models, but there is…

七国集团产出的非线性自回归领先指标模型AI 解读

Abstract This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G‐7 countries. Our models use the spread between short‐term and long‐term interest rates as leading indicators for GDP. We examine data admissibility by determining whether these models have the ability to produce time series with classical cycles that resemble the observed classical cycles in the data, and then we ask whether this data admissibility lends itself to…

美联储预测的评价：跨预测期限的合并方法AI 解读

Abstract The Federal Reserve Greenbook forecasts of real GDP, inflation and unemployment are analysed for the period 1974–1997. We consider whether these forecasts exhibit systematic bias, and whether efficient use is made of information, that is, whether revisions to these forecasts over time are predictable. Rather than analyse the forecasts separately for each horizon of interest, we discuss and implement methods that pool information over horizons. We conclude that there…

用于时间序列预测的广义指数预测器AI 解读

We consider the problem of prediction for stationary and nonstationary univariate time series using a modification suggested by the usual exponentially weighted moving average method. The modification leads to a class of general exponential predictors that can improve on the usual finite approximations to an infinite autoregressive process. We provide the theoretical justifications and suggest a class of predictors that covers modified and finite autoregressive fits as…

利用密度预测面板建模多期通胀不确定性AI 解读

Abstract This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the aggregate time series data would suggest. In addition, the strong link between past forecast errors and current forecast uncertainty, as often noted in the ARCH literature, is…

用于时间序列预测的函数型小波—核方法AI 解读

Summary We consider the prediction problem of a time series on a whole time interval in terms of its past. The approach that we adopt is based on functional kernel nonparametric regression estimation techniques where observations are discrete recordings of segments of an underlying stochastic process considered as curves. These curves are assumed to lie within the space of continuous functions, and the discretized time series data set consists of a relatively small, compared…

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区间预测与参数不确定性AI 解读

Forecast intervals generalize point forecasts to represent and incorporate uncertainty. Forecast intervals calculated from dynamic models typically sidestep the issue of parameter estimation. This paper shows how to construct asymptotic forecast intervals which incorporate the uncertainty due to parameter estimation. Our proposed solution is a simple proportional adjustment to the interval endpoints, the adjustment factor depending on the asymptotic variance of the interval…

宏观时间序列多步预测：直接法与迭代 AR 方法的比较AI 解读

“Iterated” multiperiod ahead time series forecasts are made using a one-period ahead model, iterated forward for the desired number of periods, whereas “direct” forecasts are made using a horizon-specific estimated model, where the dependent variable is the multi-period ahead value being forecasted. Which approach is better is an empirical matter: in theory, iterated forecasts are more efficient if correctly specified, but direct forecasts are more robust to model…

电价的状态转换长记忆模型AI 解读

In this paper we develop a regime switching model which can generate long memory (fractional integration) in each of the regime states. This property is relevant in a number of cases. For instance, the deregulated market for electricity power in the Nordic countries is characterized by electricity spot prices with a high degree of long memory. It occurs that in some time periods bilateral prices are identical whereas in other periods the prices differ. The latter occurs when…

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条件预测能力检验AI 解读

We propose a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite…