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Ullah, A.
Ullah, A.
unemployment, 12, 184, 200, 216, 236, 332, 490, 493, 499; predictability of future unemployment, 213–24; unemployment dynamics, 179. See also unemployment rate
unemployment rate, 15, 140, 146, 164, 171, 176, 177, 183, 220, 239, 241, 322, 324, 325, 362, 480; from 1970 to 2014, 212. See also three-variable system (CPI inflation rate, unemployment rate, three-month T-bill rate), empirical example of
univariate forecasting models, and trending variables, 470–72; long-horizon forecasts, 475–77, 476nn8–9; and out-of-sample (OoS) evaluation, 486–89; and risk for differing approaches to long-horizon forecasts, 477–78; short-horizon forecasts, 472–75
univariate Gaussian kernel, 246
univariate linear prediction models, 133–34, 166. See also autoregressive moving average (ARMA) models
univariate nonlinear prediction models, 166–67; testing for nonlinearity, 179–80. See also autoregressive (AR) models; nonlinear univariate models
University of Michigan, survey of U.S. consumers, 322
Vahid, F.
Valkanov, R., 481, 484, 485, 501, 502
van Dijk, D.
Varian, H. R.
VARMA models, 187n1, 218
vector autoregressions (VARs), 7, 117, 186, 212, 217, 218, 238, 408, 463, 478; and the choice of lag length, 190; constrained VARs, 189; estimation of VAR( p) models, 189–90; factor-augmented VAR (FAVAR) models, 8,
193–94, 232; first-order Markov switching VAR (MSVAR), 456; misspecification for, 192; multiperiod forecasts with, 191–93; OLS estimation of, 189–90, 191, 194, 197, 198, 201, 203, 221; popularity of, 214; Qual VAR, 268; specification of vector auto regressions, 186–88; use of to approximate covariance stationary multivariate processes, 186–87; VAR forecasts, 39. See also Bayesian VARs (BVARs)
Villani, M.
Violante, F.
Vogelsang, T. J.
volatility, and density forecasting, 275–77, 308–9, 427–28; forecasts using realized volatility measures, 288–90; role of the loss function in (optimal forecasts in first and second moments under different loss functions), 277–78
volatility models, 276, 278, 284, 284n4; estimation of parameters of location-scale models, 279–80; location-scale models, 278–80; stochastic volatility models, 199, 200, 287, 338, 459. See also generalized autoregressive conditional heteroscedasticity (GARCH) models; multivariate volatility models
volatility forecast comparisons
Waggoner, D. F.
Wahba, G.
Wald test
Waldmann, R.
Wallis, K. F.
Watson, M. W.
weak LNN results
weather forecasts/forecasters, 13, 38, 435n3
Weibull distribution
Weinbach, G. C.
Weiss, A. A.
Welch, I.
West, K. D. See also out-of-sample (OoS) average loss, conducting inference on
West, M.
White, H. See also Reality Check (White’s bootstrap Reality Check approach)
white noise, 135, 136–37, 138, 143, 227, 290, 359,359n5, 471, 508
Whiteman, C. H.
White’s QuickNet Algorithm
Wiener-Kolmogorov prediction formulas
Wilson, E. B.
Winkler, R. L.
Wishart distribution
Wohar, M. E.
Wolak, F. A.
Wold representation theorem, 133, 135, 140–41, 148, 186, 359
Wolf, M.
Woolridge, J. M.
Wouters, R.
Wright, J. H.
Wu, J. C.
Yang, Y.
Yogo, M.
Yoldas, E.
Yoo, B. S.
Zanakis, S. H.
Zarnowitz, V.
Zellner, A. exposition by of the Bayesian analysis of regression models, 85; g -prior approach of, 82, 459n13
Zha, T.
Zhang, H. H.
Zopounidis, C.
Zou, H.
练习题
What is the primary focus of Ullah's research on unemployment?
Which period is specifically mentioned for the unemployment rate in the source material?
What type of forecasting models are discussed in relation to trending variables?
Which of the following are mentioned as components of vector autoregressions (VARs)?
Which of the following are types of volatility models mentioned in the source material?
The Wold representation theorem is applicable to both univariate and multivariate time series analysis.
White noise is characterized by a constant mean and variance over time.
The factor-augmented VAR (FAVAR) models are mentioned as an extension of the basic VAR models, specifically in pages ___.
Explain the role of the loss function in volatility and density forecasting.
What is the significance of Zellner's g-prior approach in Bayesian analysis?
Which of the following statements about the Wold representation theorem are correct?
When estimating parameters of a vector autoregression (VAR) model, which tool can be used to construct the likelihood or pseudo-likelihood for the data, especially when the model contains unknown parameters in the coefficient matrices?
Which of the following statements are true regarding the properties and applications of vector autoregressions (VARs)? Select all that apply.
In a vector autoregression (VAR) model, the Kalman smoother is used to estimate the unobserved state variables at time given all past, current, and future data, while the Kalman filter is used to estimate these variables given only past and current data.
The ___ theorem states that any covariance stationary process can be represented as a linear combination of lagged values of a white noise process.
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