We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.time series analysis, time domain, frequency domain, Research Methods/ Statistical Methods,

Diebold, F.X.

Kilian, L.

Nerlove, Marc

English

Research Papers in Economics

A comparison of a modified Hannan and the BLS seasonal adjustment filters.

A new approach to linear filtering and prediction problems.

A new look at the statistical model identification

A simple estimator of cointegrating vectors in higher order integrated systems.

A Study in the Analysis of Stationary Time Series. Stockholm: Almqvist and Wiksell.

A unified view of statistical forecasting procedures.

Alternative explanations of the money-income correlation.

An algorithm for the exact likelihood of a mixed autoregressive-moving average process.

An approach to time series analysis.

An ARIMA-model-based approach to seasonal adjustment.

Analysis and modeling of seasonal time series.

Analysis of Economic Time Series.

Are forecasting models usable for policy analysis? Quarterly Review, Federal Reserve Bank of

Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation.

Bayesian model averaging for linear regression models.

Calculating the variance of seasonally adjusted series. Working Paper,

Capital and the American Economy: Its Formation and Financing.

Co-integration and error correction: Representation, estimation and testing.

Confidence intervals for the largest autoregressive root in U.S. economic time series.

Desestacionalization y Politica Monetaria. Economic Studies 19, Bank of Spain,

Distributed lags and unobserved components in economic time series.

Estimating the dimension of a model.

Estimation for autoregressive moving average models in the time and frequency domains.

Estimation procedures for structural TSM’s. Working Paper, London School of Economics.

Evaluation of likelihood functions for Gaussian signals.

Forecasting economic time series with structural and Box–Jenkins models: a case study (with discussion).

Forecasting Economic Time Series.

Forecasting sales by exponentially weighted moving averages.

Forecasting seasonals and trends by exponentially weighted moving averages.

Forecasting using principal components from a large number of predictors.

Forecasting. Structural Time Series Models and the Kalman Filter. Cambridge:

Fractionally integrated generalized autoregressive conditional heteroskedasticity.

Further results on forecasting and model selection under asymmetric loss.

Generalized autoregressive conditional heteroskedasticity.

Identifying VARs based on high frequency futures data.

Inference in Hidden Markov Models.

Inference in linear time series models with some unit roots.

Interpolation und Extrapolation von Stationären Zufäligen Folgen.

Introduction to Statistical Time Series.

Issues involved with seasonal analysis of economic time series.

Likelihood Based Inference in Cointegrated Vector Autoregressive Models.

Long cycles – fact or artifact?

Long memory and regime switching.

Long memory processes and fractional integration in econometrics.

Long memory relationships and the aggregation of dynamic models.

Long Run Economic Relations: Readings in Co-integration. Oxford:

Macroeconomic forecasting using diffusion indexes.

Macroeconomics and reality.

Maximum likelihood estimation of regression models with moving average errors when the root lies on the unit circle.

Measuring monetary policy.

model-based treatment of a manic depressive series. Working Paper, Bank of Spain,

Modeling considerations in the seasonal analysis of economic time series.

Modelling Nonlinear Economic Relationships.

Modelling Seasonality.

Multiple time series analysis and the final form of econometric models.

Multiple time series regression with integrated processes.

Multiple Time Series.

Note on searching for hidden periodicities.

On a method of investigating periodicities in disturbed series with special reference to Wolfer’s sunspot numbers.

On adjusting the HP-filter for the frequency of observations.

On the global identification of the dynamic simultaneous equations model with stationary disturbances.

On the investigation of hidden periodicities with application to the supposed 26-day period of meteorological phenomena. Terrestrial Magnetism and Atmospheric Electricity [now

On the optimality of adaptive forecasting.

On the time-correlation problem, with special reference to the variate-difference correlation method.

Optimal Prediction under asymmetric loss.

Optimal properties of exponentially weighted forecasts.

Parametric and nonparametric volatility measurement. Forthcoming

Periodicity and Stochastic Trends in Economic Tim Series.

Postwar U.S. Business Cycles: An Empirical Investigation.

Prediction and Regulation by Linear Least-squares Methods.

Prediction with a generalized cost of error function.

Seasonal adjustment by signal extraction.

Seasonal adjustment when both deterministic and stochastic seasonality are present.

Signal extraction error in nonstationary time series.

Some observations on adaptive forecasting.

Some properties of ‘optimal’ seasonal adjustment.

Some properties of time series data and their use in econometric model specification.

Sources of business cycle fluctuations.

Spectral Analysis and Time Series.

Spectral analysis of seasonal adjustment procedures.

Spectral Methods in Econometrics. Cambridge:

Spurious periodicity in inappropriately detrended time series.

Spurious regressions in econometrics.

Statistical inference in instrumental variables regression with I(1) processes.

Stochastic Processes.

Stochastic trends and economic fluctuations.

Stochastic Volatility: Selected Readings.

The band pass filter.

The combination of forecasts.

The dynamic effects of aggregate demand and supply disturbances.

The Econometric Analysis of Seasonal Time Series. Cambridge:

The Econometric Analysis of Time Series.

The estimation and application of long memory series models.

The estimation of mixed moving average autoregressive systems.

The exact likelihood function for a mixed autoregressive-moving average process.

The Extrapolation, Interpolation and Smoothing of Stationary Time Series with Engineering Applications.

The identification and parameterization of ARMAX and state space forms.

The identification problem for multiple equation systems with moving average errors.

The robustness of co-integration methods when regressors almost have unit roots.

The robustness of identified VAR conclusions about money.

Time Series Analysis

Time Series Analysis

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Research Papers in Economics