Time Series Analysis¶
Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Convolution
- Economics and operations. Distributed-lag models convolve past shocks with a response weight; queueing waiting-time distributions are convolutions of service times; depreciation schedules convolve investment with a decay kernel.
This sourceDistributed-lag and moving-average representations convolve past shocks with a response-weight sequence; impulse-response functions are the response of the system to a one-off shock — the econometric convolution.
- Economics and operations. Distributed-lag models convolve past shocks with a response weight; queueing waiting-time distributions are convolutions of service times; depreciation schedules convolve investment with a decay kernel.
- Recurrence
- It separates moments in time (or iterations of a process) and names the dependencies that link them, a formulation Hamilton (1994) treats as canonical for time-series analysis.
This sourceStandard graduate-level reference for time-series econometrics: develops state-at-time-t-depends-on-prior-states (autoregressive, ARMA, state-space) models as the canonical mathematical encoding of temporal recurrence.
- It separates moments in time (or iterations of a process) and names the dependencies that link them, a formulation Hamilton (1994) treats as canonical for time-series analysis.
- Stationarity
- Every stationarity claim specifies (1) the process or quantity whose statistics are being assessed, (2) the notion of stationarity being invoked (strict, wide-sense, cyclostationary), (3) the temporal or spatial window over which stationarity is asserted, and (4) the tests or evidence supporting (or challenging) the claim — because stationarity is almost always an approximation valid on some scale and invalidated by regime change on another
This sourceStandard graduate-level reference for time-series econometrics: develops state-at-time-t-depends-on-prior-states (autoregressive, ARMA, state-space) models as the canonical mathematical encoding of temporal recurrence.
- Every stationarity claim specifies (1) the process or quantity whose statistics are being assessed, (2) the notion of stationarity being invoked (strict, wide-sense, cyclostationary), (3) the temporal or spatial window over which stationarity is asserted, and (4) the tests or evidence supporting (or challenging) the claim — because stationarity is almost always an approximation valid on some scale and invalidated by regime change on another
Mechanisms¶
- Autoregressive Stochastic Sequence Model
- It misreads regime shifts (a structural break shows up as spurious high persistence), long memory, and nonlinear dynamics, and a series with a near-unit-root coefficient masquerades as strongly predictable when it is really close to a random walk.
This sourceShows that an autoregressive process with a coefficient near unity is close to a random walk.
- It misreads regime shifts (a structural break shows up as spurious high persistence), long memory, and nonlinear dynamics, and a series with a near-unit-root coefficient masquerades as strongly predictable when it is really close to a random walk.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Not recorded. Neither this nor any other of the 4 citations of this work carries a recorded support check.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:64f82152d64e · see in the full table