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on Econometric Time Series |
| By: | Silvia Goncalves; Michael W. McCracken; Yongxu Yao |
| Abstract: | 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 subject to revision. Monte Carlo experiments indicate that the bootstrap can provide satisfactory finite sample size and power even in modest sample sizes. We conclude with an application to inflation forecasting that adapts the results in Ang et al. (2007) to the presence of real-time data. |
| Keywords: | real-time data; bootstrap; prediction; inference |
| JEL: | C53 C12 C52 |
| Date: | 2023–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:97409&r=ets |
| By: | Andrea Renzetti |
| Abstract: | Time-Varying Parameters Vector Autoregressive (TVP-VAR) models are frequently used in economics to capture evolving relationships among the macroeconomic variables. However, TVP-VARs have the tendency of overfitting the data, resulting in inaccurate forecasts and imprecise estimates of typical objects of interests such as the impulse response functions. This paper introduces a Theory Coherent Time-Varying Parameters Vector Autoregressive Model (TC-TVP-VAR), which leverages on an arbitrary theoretical framework derived by an underlying economic theory to form a prior for the time varying parameters. This "theory coherent" shrinkage prior significantly improves inference precision and forecast accuracy over the standard TVP-VAR. Furthermore, the TC-TVP-VAR can be used to perform indirect posterior inference on the deep parameters of the underlying economic theory. The paper reveals that using the classical 3-equation New Keynesian block to form a prior for the TVP- VAR substantially enhances forecast accuracy of output growth and of the inflation rate in a standard model of monetary policy. Additionally, the paper shows that the TC-TVP-VAR can be used to address the inferential challenges during the Zero Lower Bound period. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.11858&r=ets |
| By: | Xitai Yu |
| Abstract: | The ARIMA (Autoregressive Integrated Moving Average model) has extensive applications in the field of time series forecasting. However, the predictive performance of the ARIMA model is limited when dealing with data gaps or significant noise. Based on previous research, we have found that cubic spline interpolation performs well in capturing the smooth changes of stock price curves, especially when the market trends are relatively stable. Therefore, this paper integrates the two approaches by taking the time series data in stock trading as an example, establishes a time series forecasting model based on cubic spline interpolation and ARIMA. Through validation, the model has demonstrated certain guidance and reference value for short-term time series forecasting. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.10759&r=ets |
| By: | Martin Winist\"orfer; Ivan Zhdankin |
| Abstract: | Assessing the predictive power of both data and models holds paramount significance in time-series machine learning applications. Yet, preparing time series data accurately and employing an appropriate measure for predictive power seems to be a non-trivial task. This work involves reviewing and establishing the groundwork for a comprehensive analysis of shaping time-series data and evaluating various measures of dependence. Lastly, we present a method, framework, and a concrete example for selecting and evaluating a suitable measure of dependence. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.12129&r=ets |
| By: | Yingjie Feng |
| Abstract: | This paper studies optimal estimation of large-dimensional nonlinear factor models. The key challenge is that the observed variables are possibly nonlinear functions of some latent variables where the functional forms are left unspecified. A local principal component analysis method is proposed to estimate the factor structure and recover information on latent variables and latent functions, which combines $K$-nearest neighbors matching and principal component analysis. Large-sample properties are established, including a sharp bound on the matching discrepancy of nearest neighbors, sup-norm error bounds for estimated local factors and factor loadings, and the uniform convergence rate of the factor structure estimator. Under mild conditions our estimator of the latent factor structure can achieve the optimal rate of uniform convergence for nonparametric regression. The method is illustrated with a Monte Carlo experiment and an empirical application studying the effect of tax cuts on economic growth. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07243&r=ets |
| By: | Gadea Rivas, María Dolores; Gonzalo, Jesús; Ramos, Andrey |
| Abstract: | Determining whether Global Average Temperature (GAT) is an integrated process of order 1, I(1), or is a stationary process around a trend function is crucial for detection, attribution, impact and forecasting studies of climate change. In this paper, we investigate the nature of trends in GAT building on the analysis of individual temperature grids. Our 'micro-founded' evidence suggests that GAT is stationary around a non-linear deterministic trend in the form of a linear function with a one-period structural break. This break can beattributed to a combination of individual grid breaks and the standard aggregation method under acceleration in global warming. We illustrate our findings using simulations. |
| Keywords: | Trends; Unit Roots; Structural Breaks; Temperature; Aggregation |
| JEL: | C32 Q54 |
| Date: | 2023–12–05 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:39045&r=ets |