Handbook of Economic Forecasting part 62 pot
Handbook of Economic Forecasting part 62 pot
... accords with the forecasting nature of
this regression, if serial correlation were detected we would include lags of the depen-
dent variable in the forecasting regression.
Ch. 11: Forecasting with ... the asymptotic properties of the estimator, we require some additional
assumptions. Jointly we can consider the vector of partial sums of η
t
and we assume
that this partial sum...
Handbook of Economic Forecasting part 10 potx
... Uni-
versity of Iowa. Previously, the Institute had published forecasts of general economic
conditions and had produced tax revenue forecasts for internal use of the state’s De-
partment of Management ... with Bayesian forecasting: The Fed-
eral Reserve Bank of Minneapolis national forecasting project, and The Iowa Economic
Forecast produced by The University of Iowa Instit...
Handbook of Economic Forecasting part 13 pot
... empirical application because of a possible failure
of stationarity. Specifically, Christiano compared predictions of models estimated over
samples of increasing size: the first of his 96 predictions relied ... (2000b). Economic and statistical measures of forecast accuracy”. Journal
of Forecasting 19, 537–560.
Machina, M.J., Granger, C.W.J., (2005). “Decision-based forecast ev...
Handbook of Economic Forecasting part 14 potx
... distribution of this term was given
in (3.1). The second term on the right-hand side of (5.6) results from reliance of pre-
dictions on estimates of β
∗
. To account for the effects of this second ... discussed
in Sections 6 and 7 below.
4. A small number of nonnested models, Part II
In the vast majority of economic applications, one or more of the models under con-
sidera...
Handbook of Economic Forecasting part 31 pot
... replacing
θ
∗
t,rec
and
θ
t,rec
with
θ
∗
t,rol
and
θ
t,rol
, respectively.
P
ROOF OF PROPOSITION 3.8. The proof is similar to the proof of Proposition 3.7.
P
ROOF OF PROPOSITION 4.5(ii). Note that, via a mean value expansion, ... forecasts”. International Economic Review 39, 841– 862.
Christoffersen, P., Diebold, F.X. (2000). “How relevant is volatility forecasting for...
Handbook of Economic Forecasting part 36 pot
... “Some consequences of temporal aggregation in seasonal time series models”. In: Zell-
ner, A. (Ed.), Seasonal Analysis of Economic Time Series. U.S. Department of Commerce, Bureau of the
Census, ... “Analysis of cointegrated VARMA processes”. Journal of Economet-
rics 80, 223–239.
Lütkepohl, H., Poskitt, D.S. (1996). “Specification of echelon form VARMA models”. Journal of Bu...
Handbook of Economic Forecasting part 38 pot
... significance. Different definitions of turning points might be consid-
ered, for example a change in sign of the cycle, a change in sign of its slope or a change
in sign of the slope of the cycle and the trend ... of different ARIMA models for forecasting and the pos-
sible shortcomings of the approach.
From the theoretical point of view, the autoregressive representation of...
Handbook of Economic Forecasting part 40 pot
... vector of random variables, a Bayesian interpretation of the
Kalman filter as a way of updating a Gaussian prior distribution on the state to give a
posterior is quite natural. The mechanics of filtering, ... its dimension. The total
number of parameters in the structural form is N(N + 1) while in the unrestricted
reduced form, the covariance matrix of ξ
t
consists of N(N + 1)/2 d...
Handbook of Economic Forecasting part 41 pot
... analysis of
leading indicators is essentially the same.
7.5.3. Preliminary observations and data revisions
The optimal use of different vintages of observations in constructing the best estimate
of ... short run.
7.5. Forecasting and nowcasting with auxiliary series
The use of an auxiliary series that is a coincident or leading indicator yields potential
gains for nowcasting and...
Handbook of Economic Forecasting part 43 potx
... model by ML gives an estimate of ω of 0.866. This
results in an estimate of the probability of Oxford winning a future race of 0.833. The
high probability is a reflection of the fact that Oxford won ... distribution of goals in next match.
Number of goals
01234>4
0.471 0.326 0.138 0.046 0.013 0.005
Ch. 7: Forecasting with Unobserved Components Time Series Models 401
forec...
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