nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒11‒14
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. The Local to Unity Dynamic Tobit Model By Anna Bykhovskaya; James A. Duffy
  2. Forecasting Inflation: The Use of Dynamic Factor Analysis and Nonlinear Combinations By Stephen G. Hall; George S. Tavlas; Yongli Wang
  3. Time-Series Methods for Forecasting and Modeling Uncertainty in the Food Price Outlook By MacLachlan, Matthew; Chelius, Carolyn; Short, Gianna
  4. Modelling Large Dimensional Datasets with Markov Switching Factor Models By Matteo Barigozzi; Daniele Massacci
  5. DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions By Fernando Moreno-Pino; Stefan Zohren
  6. An identification and testing strategy for proxy-SVARs with weak proxies By Giovanni Angelini; Giuseppe Cavaliere; Luca Fanelli

  1. By: Anna Bykhovskaya; James A. Duffy
    Abstract: This paper extends local to unity asymptotics to the non-linear setting of the dynamic Tobit model, motivated by the application of this model to highly persistent censored time series. We show that the standardised process converges weakly to a non-standard limiting process that is constrained (regulated) to be positive, and derive the limiting distributions of the OLS estimates of the model parameters. This allows inferences to be drawn on the overall persistence of a process (as measured by the sum of the autoregressive coefficients), and for the null of a unit root to be tested in the presence of censoring. Our simulations illustrate that the conventional ADF test substantially over-rejects when the data is generated by a dynamic Tobit with a unit root.
    Date: 2022–10
  2. By: Stephen G. Hall (Leicester University, Bank of Greece, and Pretoria University); George S. Tavlas (Bank of Greece and the Hoover Institution, Stanford University); Yongli Wang (University of Birmingham)
    Abstract: This paper considers the problem of forecasting inflation in the United States, the euro area and the United Kingdom in the presence of possible structural breaks and changing parameters. We examine a range of moving window techniques that have been proposed in the literature. We extend previous work by considering factor models using principal components and dynamic factors. We then consider the use of forecast combinations with time-varying weights. Our basic finding is that moving windows do not produce a clear benefit to forecasting. Time-varying combination of forecasts does produce a substantial improvement in forecasting accuracy.
    Keywords: forecast combinations, structural breaks, rolling windows, dynamic factor models, Kalman filter
    JEL: C52 C53
    Date: 2022–10
  3. By: MacLachlan, Matthew; Chelius, Carolyn; Short, Gianna
    Abstract: This technical bulletin describes a time-series-based approach for forecasting food prices that includes prediction intervals to communicate uncertainty. The performance of forecasts created with this approach was compared to that of previously published USDA, Economic Research Service (ERS) Food Price Outlook (FPO) forecast ranges. The methods in this new approach are intended to be used in FPO data releases that provide monthly forecasts of annual food price changes and may also prove useful in other forecasting endeavors. The new approach used an autoregressive integrated moving average (ARIMA) model that was selected based on performance (information loss), generating a more accurate forecast than previously used methods as measured by root-mean-square errors. With the parameter estimates and estimated error distribution from the optimal ARIMA model, Monte Carlo simulations are used to develop prediction intervals, which reflect uncertainty about future food prices. These prediction intervals more often included the actual annual price changes than the archived fore-cast ranges. On average, the prediction intervals also included the actual annual price change earlier in the forecasting process. These properties generally held whether we used a higher (95 percent) or lower (90 percent) confidence level. The use of standardized econometric models and model selection also allowed for the inclusion of data not currently included in FPO. The methods easily tested whether including external variables improved forecast accuracy or could be used to create new forecasts. This report considered new price change forecasts of apples, seafood, and limited-service restaurants in 2020 and the potential forecast performance improvement from incorporating futures prices as case studies.
    Keywords: Consumer/Household Economics, Demand and Price Analysis, Research Methods/ Statistical Methods, Risk and Uncertainty
    Date: 2022–08–18
  4. By: Matteo Barigozzi; Daniele Massacci
    Abstract: We study a novel large dimensional approximate factor model with regime changes in the loadings driven by a latent first order Markov process. By exploiting the equivalent linear representation of the model we first recover the latent factors by means of Principal Component Analysis. We then cast the model in state-space form, and we estimate loadings and transition probabilities through an EM algorithm based on a modified version of the Baum-Lindgren-Hamilton-Kim filter and smoother which makes use of the factors previously estimated. An important feature of our approach is that it provides closed form expressions for all estimators. We derive the theoretical properties of the proposed estimation procedure and show their good finite sample performance through a comprehensive set of Monte Carlo experiments. An important feature of our methodology is that it does not require knowledge of the true number of factors. The empirical usefulness of our approach is illustrated through an application to a large portfolio of stocks.
    Date: 2022–10
  5. By: Fernando Moreno-Pino; Stefan Zohren
    Abstract: Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions to forecast day-ahead volatility by using high-frequency data. We show that the dilated convolutional filters are ideally suited to extract relevant information from intraday financial data, thereby naturally mimicking (via a data-driven approach) the econometric models which incorporate realised measures of volatility into the forecast. This allows us to take advantage of the abundance of intraday observations, helping us to avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate DeepVol's performance. The reported empirical results suggest that the proposed deep learning-based approach learns global features from high-frequency data, achieving more accurate predictions than traditional methodologies, yielding to more appropriate risk measures.
    Date: 2022–09
  6. By: Giovanni Angelini; Giuseppe Cavaliere; Luca Fanelli
    Abstract: When proxies (external instruments) used to identify target structural shocks are weak, inference in proxy-SVARs (SVAR-IVs) is nonstandard and the construction of asymptotically valid confidence sets for the impulse responses of interest requires weak-instrument robust methods. In the presence of multiple target shocks, test inversion techniques require extra restrictions on the proxy-SVAR parameters other those implied by the proxies that may be difficult to interpret and test. We show that frequentist asymptotic inference in these situations can be conducted through Minimum Distance estimation and standard asymptotic methods if the proxy-SVAR is identified by using proxies for the non-target shocks; i.e., the shocks which are not of primary interest in the analysis. The suggested identification strategy hinges on a novel pre-test for instrument relevance based on bootstrap resampling. This test is free from pre-testing issues, robust to conditionally heteroskedasticity and/or zero-censored proxies, computationally straightforward and applicable regardless on the number of shocks being instrumented. Some illustrative examples show the empirical usefulness of the suggested approach.
    Date: 2022–10

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