nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒06‒13
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Forecasting under Structural Breaks Using Improved Weighted Estimation By Tae-Hwy Lee; Shahnaz Parsaeian; Aman Ullah
  2. "A GARCH Approach to Modeling Chilean Long-Term Swap Yields" By Tanweer Akram; Khawaja Mamun
  3. Rank Determination in Tensor Factor Model By Yuefeng Han; Rong Chen; Cun-Hui Zhang
  4. What were they thinking? Estimating the quarterly forecasts underlying annual growth projections By Christian Hepenstrick; Jason Blunier
  5. Shock persistence, uncertainty and news-driven business cycles By Kevin Lee; Kalvinder Shields; Guido Turnip
  6. High-dimensional Data Bootstrap By Victor Chernozhukov; Denis Chetverikov; Kengo Kato; Yuta Koike

  1. By: Tae-Hwy Lee (Department of Economics, University of California at Riverside, CA 92521); Shahnaz Parsaeian (Department of Economics, University of Kansas, Lawrence, KS 66045); Aman Ullah (Department of Economics, University of California at Riverside, CA 92521)
    Abstract: In forecasting a time series containing a structural break, it is important to determine how much weight can be given to the observations prior to the time when the break occurred. In this context, Pesaran et al. (2013) (PPP) proposed a weighted least squares estimator by giving different weights to observations before and after a break point for forecasting out-of-sample. We revisit their approach by introducing an improved weighted generalized least squares estimator (WGLS) using a weight (kernel) function to give different weights to observations before and after a break. The kernel weight is estimated by cross-validation rather than analytically derived from a parametric model as in PPP. Therefore, the WGLS estimator facilitates implementation of the PPP method for the optimal use of the pre-break and post-break sample observations without having to derive the parametric weights which may be misspecified. We show that the kernel weight estimated by cross-validation is asymptotically optimal in the sense of Li (1987). Monte Carlo simulations and an empirical application to forecasting equity premium are provided for verification and illustration.
    Keywords: Cross-validation; Kernel; Structural breaks; Model averaging
    JEL: C14 C22 C53
    Date: 2022–06
  2. By: Tanweer Akram; Khawaja Mamun
    Abstract: This paper econometrically models the dynamics of the Chilean interbank swap yields based on macroeconomic factors. It examines whether the month-over-month change in the short-term interest rate has a decisive influence on the long-term swap yield after controlling for other factors, such as the change in inflation, change in the growth of industrial production, change in the log of the equity price index, and change in the log of the exchange rate. It applies the generalized autoregressive conditional heteroskedasticity (GARCH) approach to model the dynamics of the long-term swap yield. The change in the short-term interest rate has an economically meaningful and statistically significant effect on the change of the interbank swap yield. This means that the Banco Central de Chile's (BCCH) monetary policy exerts an important influence on interbank swap yields in Chile.
    Keywords: Interest Rate Swaps; Swap Yield; Short-Term Interest Rate; Banco Central de Chile (BCCH); Chile
    JEL: E43 E50 E58 E60 G10 G12
    Date: 2022–05
  3. By: Yuefeng Han; Rong Chen; Cun-Hui Zhang
    Abstract: Factor model is an appealing and effective analytic tool for high-dimensional time series, with a wide range of applications in economics, finance and statistics. This paper develops two criteria for the determination of the number of factors for tensor factor models where the signal part of an observed tensor time series assumes a Tucker decomposition with the core tensor as the factor tensor. The task is to determine the dimensions of the core tensor. One of the proposed criteria is similar to information based criteria of model selection, and the other is an extension of the approaches based on the ratios of consecutive eigenvalues often used in factor analysis for panel time series. Theoretically results, including sufficient conditions and convergence rates, are established. The results include the vector factor models as special cases, with an additional convergence rates. Simulation studies provide promising finite sample performance for the two criteria.
    Date: 2020–11
  4. By: Christian Hepenstrick; Jason Blunier
    Abstract: Many prominent forecasters publish their projections at an annual frequency. However, for applied work, an estimate of the underlying quarterly forecasts is often indispensable. We demonstrate that a simple state-space model can be used to obtain good estimates of the quarterly forecasts underlying annual projections. We validate the methodology by aggregating professional forecasts for quarterly GDP growth in the United States to the annual frequency and then applying our imputation methodology. The imputed forecasts perform as well as the original quarterly forecasts. Applying the imputation methodology to Consensus forecasts for other advanced economies provides further evidence of the good performance of our proposed methodology.
    Keywords: Forecasting, frequency disaggregation, survey expectations
    JEL: C53 E37
    Date: 2022
  5. By: Kevin Lee; Kalvinder Shields; Guido Turnip
    Abstract: This paper considers the macroeconomic effects of shocks with different persistence properties identified from surveys of expectations. Using a GARCH-in-Mean model for the US, we present persistence profiles to illustrate how news about events occurring over different time frames plays different roles in explaining output fluctuations in different circumstances. For example, short-lived ‘noise’ shocks have little influence on output beyond their contemporaneous impact, either directly or via the uncertainty they create. But agents recognise the importance of ‘fundamental’ permanent shocks and these drive the news-driven business cycle, generating immediate stock price reactions and gradually building output effects but also having more immediate effects on output during recessions because of the uncertainties they create.
    Keywords: News-Driven Business Cycles, Persistence, Uncertainty, Expectations, Surveys
    JEL: C32 D84 E32
    Date: 2022–05
  6. By: Victor Chernozhukov; Denis Chetverikov; Kengo Kato; Yuta Koike
    Abstract: This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectangles, bootstrap consistency results in high dimensions, and key techniques used to establish those results. We then review selected applications of high-dimensional bootstrap: construction of simultaneous confidence sets for high-dimensional vector parameters, multiple hypothesis testing via stepdown, post-selection inference, intersection bounds for partially identified parameters, and inference on best policies in policy evaluation. Finally, we also comment on a couple of future research directions.
    Date: 2022–05

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