nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒08‒30
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Frequency Regression and Smoothing for Noisy Nonstationary Time Series By Seisho Sato; Naoto Kunimoto
  2. Money and Foreign Exchange Markets Dynamics in Nigeria: A Multivariate GARCH Approach By Atoi, Ngozi Victor; Nwambeke, Chinedu G.
  3. A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation By Zhihao Xu; Clifford M. Hurvich
  4. Multivariate self-exciting jump processes with applications to financial data By Heidar Eyjolfsson; Dag Tj{\o}stheim
  5. A Theoretical Analysis of the Stationarity of an Unrestricted Autoregression Process By Varsha S. Kulkarni
  6. On the evaluation of hierarchical forecasts By George Athanasopoulos; Nikolaos Kourentzes

  1. By: Seisho Sato (University of Tokyo); Naoto Kunimoto (Tokyo Keizai University)
    Abstract: We develop a new regression method called frequency regression and smoothing. This method is based on the separating information maximum likelihood developed by Kunitomo and Sato (2021) and Sato and Kunitomo (2020) for estimating the hidden states of random variables and handling noisy nonstationary (small sample) time series data. Many economic time series include not only the trend-cycle, seasonal, and measurement error components, but also factors such as structural breaks, abrupt changes, trading-day effects, and institutional changes. Frequency regression and smoothing can be applied to handle such factors in nonstationary time series. The proposed method is simple and applicable to several problems when analyzing nonstationary economic time series and handling seasonal adjustments. An illustrative empirical analysis of the macroconsumption in Japan is provided.
    Date: 2021–08
  2. By: Atoi, Ngozi Victor; Nwambeke, Chinedu G.
    Abstract: This study examines money market and foreign exchange market dynamics in Nigeria by estimating the dynamic correlation and volatility spillovers between Nigeria Naira/US Dollar Bureau De Change (BDC) exchange rate and interbank call rate with data from January 2007 to August 2019. The study employs a dynamic conditional correlation form of GARCH model (DCC-GARCH) to access the nature of correlation, while an unrestricted bivariate BEKK-GARCH (1, 1) form of multivariate GARCH model is utilized to investigate shocks and volatility spillover of the rates. The estimated DCC-GARCH (1, 1) reveals that interest rate and exchange rate are dynamically linked negatively, suggesting that exchange rate (or interest rate) is inversely sensitive to interest rate (or exchange rate) in Nigeria. This result was substantiated by the estimated BEKK-GARCH(1, 1) model. Furthermore, the effects of news (shocks spillover) are bi-directional across the markets. However, volatility spillover is unidirectional, from exchange rate to interest rate, suggesting that, calming the volatility in foreign exchange market does guarantee moderation of volatility in the money market, whereas the reverse is not the case. The results underscore the growing influence of foreign exchange market in the financial space of the Nigerian economy. Thus, the study recommends that foreign exchange policies aimed at maintaining exchange rate stability should be sustained, having found exchange rate to be more effective in moderating interest rate volatility in Nigeria.
    Keywords: Exchange rate, interest rate, multivariate GARCH, volatility spillover
    JEL: C4 E52 F31 G10
    Date: 2021–08–16
  3. By: Zhihao Xu; Clifford M. Hurvich
    Abstract: We propose a unified frequency domain cross-validation (FDCV) method to obtain an HAC standard error. Our proposed method allows for model/tuning parameter selection across parametric and nonparametric spectral estimators simultaneously. Our candidate class consists of restricted maximum likelihood-based (REML) autoregressive spectral estimators and lag-weights estimators with the Parzen kernel. We provide a method for efficiently computing the REML estimators of the autoregressive models. In simulations, we demonstrate the reliability of our FDCV method compared with the popular HAC estimators of Andrews-Monahan and Newey-West. Supplementary material for the article is available online.
    Date: 2021–08
  4. By: Heidar Eyjolfsson; Dag Tj{\o}stheim
    Abstract: The paper discusses multivariate self- and cross-exciting processes. We define a class of multivariate point processes via their corresponding stochastic intensity processes that are driven by stochastic jumps. Essentially, there is a jump in an intensity process whenever the corresponding point process records an event. An attribute of our modelling class is that not only a jump is recorded at each instance, but also its magnitude. This allows large jumps to influence the intensity to a larger degree than smaller jumps. We give conditions which guarantee that the process is stable, in the sense that it does not explode, and provide a detailed discussion on when the subclass of linear models is stable. Finally, we fit our model to financial time series data from the S\&P 500 and Nikkei 225 indices respectively. We conclude that a nonlinear variant from our modelling class fits the data best. This supports the observation that in times of crises (high intensity) jumps tend to arrive in clusters, whereas there are typically longer times between jumps when the markets are calmer. We moreover observe more variability in jump sizes when the intensity is high, than when it is low.
    Date: 2021–08
  5. By: Varsha S. Kulkarni
    Abstract: The higher dimensional autoregressive models would describe some of the econometric processes relatively generically if they incorporate the heterogeneity in dependence on times. This paper analyzes the stationarity of an autoregressive process of dimension $k>1$ having a sequence of coefficients $\beta$ multiplied by successively increasing powers of $0
    Date: 2021–08
  6. By: George Athanasopoulos; Nikolaos Kourentzes
    Abstract: The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of sparsity, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations.
    Keywords: Aggregation, coherence, hierarchical time series, reconciliation
    JEL: C18 C53 C55
    Date: 2021

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