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on Econometric Time Series |
| By: | Peter Reinhard Hansen; Yiyao Luo |
| Abstract: | Time-varying volatility is an inherent feature of most economic time-series, which causes standard correlation estimators to be inconsistent. The quadrant correlation estimator is consistent but very inefficient. We propose a novel subsampled quadrant estimator that improves efficiency while preserving consistency and robustness. This estimator is particularly well-suited for high-frequency financial data and we apply it to a large panel of US stocks. Our empirical analysis sheds new light on intra-day fluctuations in market betas by decomposing them into time-varying correlations and relative volatility changes. Our results show that intraday variation in betas is primarily driven by intraday variation in correlations. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.19992&r=ets |
| By: | Alain Hecq; Daniel Velasquez-Gaviria |
| Abstract: | This paper introduces new techniques for estimating, identifying and simulating mixed causal-noncausal invertible-noninvertible models. We propose a framework that integrates high-order cumulants, merging both the spectrum and bispectrum into a single estimation function. The model that most adequately represents the data under the assumption that the error term is i.i.d. is selected. Our Monte Carlo study reveals unbiased parameter estimates and a high frequency with which correct models are identified. We illustrate our strategy through an empirical analysis of returns from 24 Fama-French emerging market stock portfolios. The findings suggest that each portfolio displays noncausal dynamics, producing white noise residuals devoid of conditional heteroscedastic effects. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.19543&r=ets |
| By: | Russell Davidson; Andrea Monticini (Università Cattolica del Sacro Cuore; Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore) |
| Abstract: | The aim of this paper is to illustrate more than one instance of poor bootstrap performance, and to see how available diagnostic techniques can indicate reliably when and how this poor performance can arise. Two particular features that seem to be important to explain bootstrap discrepancy are illustrated by some Monte Carlo experiments. |
| Keywords: | Bootstrap inference, fast double bootstrap, conditional fast double bootstrap, heteroskedasticity. |
| JEL: | C12 C22 C32 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:ctc:serie1:def130&r=ets |
| By: | Annika Camehl (Erasmus University Rotterdam); Tomasz Wo\'zniak (University of Melbourne) |
| Abstract: | We propose a new Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects alternative exclusion restrictions over time and, as a condition for the search, allows to verify identification through heteroskedasticity within each regime. Based on four alternative monetary policy rules, we show that a monthly six-variable system supports time variation in US monetary policy shock identification. In the sample-dominating first regime, systematic monetary policy follows a Taylor rule extended by the term spread and is effective in curbing inflation. In the second regime, occurring after 2000 and gaining more persistence after the global financial and COVID crises, the Fed acts according to a money-augmented Taylor rule. This regime's unconventional monetary policy provides economic stimulus, features the liquidity effect, and is complemented by a pure term spread shock. Absent the specific monetary policy of the second regime, inflation would be over one percentage point higher on average after 2008. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.05883&r=ets |
| By: | Christis Katsouris |
| Abstract: | This survey study discusses main aspects to optimal estimation methodologies for panel data regression models. In particular, we present current methodological developments for modeling stationary panel data as well as robust methods for estimation and inference in nonstationary panel data regression models. Some applications from the network econometrics and high dimensional statistics literature are also discussed within a stationary time series environment. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.03471&r=ets |
| By: | Byoung Hark Yoo |
| Abstract: | This paper describes how the Congressional Budget Office uses a Bayesian vector autoregression (BVAR) method to generate alternative economic projections to the agency’s baseline. The BVAR includes a wide range of key economic variables that are needed to approximate budget outcomes. Its estimation methods avoid overfitting, a situation in which a model fits historical data well while having a poor ability to project future values.Given targets of future values of some variables such as inflation, the BVAR generates economic projections consistent with |
| JEL: | C32 C53 |
| Date: | 2023–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:cbo:wpaper:59629&r=ets |
| By: | Scott Alan Carson; Wael M. Al-Sawai; Scott A. Carson |
| Abstract: | Regression model error assumptions are essential to estimator properties. Least squares model parameters are consistent and efficient when the underlying error terms are normally distributed but yield inefficient estimators when errors are not normally distributed. Partially adaptive and M-estimation are alternatives to least squares when regression model errors are not normally distributed. Vertically Integrated firms in the oil and gas industry is one industrial sector where error mis-specification is consequential. Equity returns are a common area where returns are not normally distributed, and inappropriate error distribution specification has substantive effect when estimating capital costs. Vertically Integrated Major equity returns and accompanying regression model error terms are not normally distributed, and this study considers error returns for Integrated oil and gas producers. Vertically Integrated firm returns and their regression model error are not normally distributed, and alternative estimators to least squares have desirable properties. |
| Keywords: | partially adaptive regression models, oil and gas industry, Integrated Majors, vertical integration |
| JEL: | G12 L71 L72 Q40 Q41 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10733&r=ets |
| By: | John Y. Campbell (Harvard University; NBER); Can Gao (University of St. Gallen; Swiss Finance Institute); Ian Martin (London School of Economics) |
| Abstract: | We introduce a new measure of a government’s fiscal position that exploits cointegrating relationships among fiscal variables and output. The measure is a loglinear combination of tax revenue, government spending and the market value of government debt that—unlike the debt-GDP ratio—is stationary in the US and the UK since World War II. Fiscal deterioration forecasts a long-run decline in spending rather than increased tax revenue or low returns for bondholders. Fiscal adjustment to tax and spending shocks occurs through mean-reversion in tax and spending growth, with a negligible contribution from debt returns. |
| Keywords: | Debt, deficits, primary surplus, stationarity, cointegration |
| JEL: | H62 H63 G12 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23101&r=ets |