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on Econometric Time Series |
| By: | Lloyd, Simon (Bank of England); Manuel, Ed (London School of Economics) |
| Abstract: | A common approach to estimating causal effects in macroeconomics involves constructing orthogonalised ‘shocks’ then integrating them into local projections or vector autoregressions. For a general set of estimators, we show that this two-step ‘shock-first’ approach can be problematic for identification and inference relative to a one-step procedure which simply adds appropriate controls directly in the outcome regression. We show this analytically by comparing one and two-step estimators without assumptions on underlying data-generating processes. In simple ordinary least squares (OLS) settings, the two approaches yield identical coefficients, but two-step inference is unnecessarily conservative. More generally, one and two-step estimates can differ due to omitted-variable bias in the latter when additional controls are included in the second stage or when employing non-OLS estimators. In monetary-policy applications controlling for central-bank information, one-step estimates indicate that the (dis)inflationary consequences of US monetary policy are more robust than previously realised, not subject to a ‘price puzzle’. |
| Keywords: | Identification; instrumental variables; local projections; omitted-variable bias; vector autoregressions |
| JEL: | C22 C26 C32 C36 E50 E60 |
| Date: | 2024–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1079 |
| By: | Shuping Shi (Macquarie University); Jun Yu (University of Macau); Chen Zhang (University of Macau) |
| Abstract: | This paper introduces a novel and easy-to-implement method for accurately approximating the spectral density of discretely sampled fractional Ornstein-Uhlenbeck (fOU) processes. The method offers a substantial reduction in approximation error, particularly within the rough region of the fractional parameter H 2 (0;0:5). This approximate spectral density has the potential to enhance the performance of estimation methods and hypothesis testing that make use of spectral densities. We introduce the approximate Whittle maximum likelihood (AWML) method for discretely sampled fOU processes, utilising the approximate spectral density, and demonstrate that the AWML estimator exhibits properties of consistency and asymptotic normality when H 2 (0;1), akin to the conventional Whittle maximum likelihood method. Through extensive simulation studies, we show that AWML outperforms existing methods in terms of estimation accuracy in finite samples. We then apply the AWML method to the trading volume of 40 financial assets. Our empirical findings reveal that the estimated Hurst parameters for these assets fall within the range of 0:10 to 0:21, indicating a rough dynamic. |
| Keywords: | Fractional Brownian motion; Fractional Ornstein-Uhlenbeck process; Spectral density; Paxson approximation; Whittle maximum likelihood; Realized volatility |
| JEL: | C13 C22 G10 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202416 |
| By: | Abdulnasser Hatemi-J |
| Abstract: | Asymmetric causality tests are increasingly gaining popularity in different scientific fields. This approach corresponds better to reality since logical reasons behind asymmetric behavior exist and need to be considered in empirical investigations. Hatemi-J (2012) introduced the asymmetric causality tests via partial cumulative sums for positive and negative components of the variables operating within the vector autoregressive (VAR) model. However, since the the residuals across the equations in the VAR model are not independent, the ordinary least squares method for estimating the parameters is not efficient. Additionally, asymmetric causality tests mean having different causal parameters (i.e., for positive or negative components), thus, it is crucial to assess not only if these causal parameters are individually statistically significant, but also if their difference is statistically significant. Consequently, tests of difference between estimated causal parameters should explicitly be conducted, which are neglected in the existing literature. The purpose of the current paper is to deal with these issues explicitly. An application is provided, and ten different hypotheses pertinent to the asymmetric causal interaction between two largest financial markets worldwide are efficiently tested within a multivariate setting. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.03137 |