In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

SUMMARY Thispaperconsidersmixed,orrandomcoeAcients,multinomiallogit (MMNL)modelsfordiscreteresponse, andestablishesthefollowingresults.Undermildregularityconditions,anydiscretechoicemodelderivedfrom random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNLmodel.PracticalestimationofaparametricmixingfamilycanbecarriedoutbyMaximumSimulated LikelihoodEstimationorMethodofSimulatedMoments,andeasilycomputedinstrumentsareprovidedthat make the latter procedure fairly eAcient. The adequacy of a mixing specification can be tested simply as an omittedvariabletestwithappropriatelydefinedartificialvariables.Anapplicationtoaproblemofdemandfor alternativevehiclesshowsthatMMNL provides aflexible and computationally practical approach todiscrete response analysis. Copyright # 2000 John Wiley & Sons, Ltd.

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Mixed mnl models for discrete response

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

Uncertainty appears to jump up after major shocks like the Cuban Missile crisis, the assassination of JFK, the OPEC I oil-price shock and the 9/11 terrorist attack This paper offers a structural framework to analyze the impact of these uncertainty shocks I build a model with a time varying second moment, which is numerically solved and estimated using firm level data The parameterized model is then used to simulate a macro uncertainty shock, which produces a rapid drop and rebound in aggregate output and employment This occurs because higher uncertainty causes firms to temporarily pause their investment and hiring Productivity growth also falls because this pause in activity freezes reallocation across units In the medium term the increased volatility from the shock induces an overshoot in output, employment and productivity Thus, second moment shocks generate short sharp recessions and recoveries This simulated impact of an uncertainty shock is compared to VAR estimations on actual data, showing a good match in both magnitude and timing The paper also jointly estimates labor and capital convex and non-convex adjustment costs Ignoring capital adjustment costs is shown to lead to substantial bias while ignoring labor adjustment costs does not

The Impact of Uncertainty Shocks

Uncertainty appears to jump up after major shocks like the Cuban Missile crisis, the assassination of JFK, the OPEC I oil-price shock, and the 9/11 terrorist attacks. This paper offers a structural framework to analyze the impact of these uncertainty shocks. I build a model with a time-varying second moment, which is numerically solved and estimated using firm-level data. The parameterized model is then used to simulate a macro uncertainty shock, which produces a rapid drop and rebound in aggregate output and employment. This occurs because higher uncertainty causes firms to temporarily pause their investment and hiring. Productivity growth also falls because this pause in activity freezes reallocation across units. In the medium term the increased volatility from the shock induces an overshoot in output, employment, and productivity. Thus, uncertainty shocks generate short sharp recessions and recoveries. This simulated impact of an uncertainty shock is compared to vector autoregression estimations on actual data, showing a good match in both magnitude and timing. The paper also jointly estimates labor and capital adjustment costs (both convex and nonconvex). Ignoring capital adjustment costs is shown to lead to substantial bias, while ignoring labor adjustment costs does not.

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Using a multivariate vector-autoregression (VAR) approach, this paper investigates causal relations and dynamic interactions among asset returns, real activity, and inflation in the postwar United States. Major findings are (1) stock returns appear Granger-causally prior and help explain real activity, (2) with interest rates in the VAR, stock returns explain little variation in inflation, although interest rates explain a substantial fraction of the variation in inflation, and (3) inflation explains little variation in real activity. These findings seem more compatible with Fama (1981) than with Geske and Roll (1983) or with Ram and Spencer (1983).

Causal Relations Among Stock Returns, Interest Rates, Real Activity, and Inflation

Simulation estimation of time-series models☆

This paper examines the dynamic relations – causal relations and the sign and magnitude of dynamic effects – between stock market trading volume and returns (and volatility) for both domestic and cross-country markets by using the daily data of the three largest stock markets: New York, Tokyo, and London. Major findings are as follows: First, trading volume does not Granger-cause stock market returns on each of three stock markets. Second, there exists a positive feedback relationship between trading volume and return volatility in all three markets. Third, regarding the cross-country relationships, US financial market variables, in particular US trading volume, contains an extensive predictive power for UK and Japanese financial market variables. Fourth, sub-sample analyses show evidence of stronger spillover effects after the 1987 market crash and an increased importance of trading volume as an information variable after the introduction of options in the US and Japan.

The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence

Genome editing is crucial for genetic engineering of organisms for improved traits, particularly in microalgae due to the urgent necessity for the next generation biofuel production. The most advanced CRISPR/Cas9 system is simple, efficient and accurate in some organisms; however, it has proven extremely difficult in microalgae including the model alga Chlamydomonas. We solved this problem by delivering Cas9 ribonucleoproteins (RNPs) comprising the Cas9 protein and sgRNAs to avoid cytotoxicity and off-targeting associated with vector-driven expression of Cas9. We obtained CRISPR/Cas9-induced mutations at three loci including MAA7, CpSRP43 and ChlM, and targeted mutagenic efficiency was improved up to 100 fold compared to the first report of transgenic Cas9-induced mutagenesis. Interestingly, we found that unrelated vectors used for the selection purpose were predominantly integrated at the Cas9 cut site, indicative of NHEJ-mediated knock-in events. As expected with Cas9 RNPs, no off-targeting was found in one of the mutagenic screens. In conclusion, we improved the knockout efficiency by using Cas9 RNPs, which opens great opportunities not only for biological research but also industrial applications in Chlamydomonas and other microalgae. Findings of the NHEJ-mediated knock-in events will allow applications of the CRISPR/Cas9 system in microalgae, including “safe harboring” techniques shown in other organisms.

/pdf/crispr-cas9-induced-knockout-and-knock-in-mutations-in-53wxoekfwv.pdf

CRISPR/Cas9-induced knockout and knock-in mutations in Chlamydomonas reinhardtii

The paper develops a two-country micro-theoretic model consistent with the Ricardian equivalence hypothesis (REH). Specifically, tax increases used to retire government debt will not affect private spending or the current account balance. However, increases in government spending, regard- less of the means of finance, can be expected to induce a current account deficit. An unconstrained vector autoregres- sion shows some patterns in the recent U.S. data which appear to be inconsistent with the REH. Rigorous testing of the model, however, does not allow us to reject the indepen- dence of the record federal government budget and current account deficits.

Bong-Soo Lee

Papers

Causal Relations Among Stock Returns, Interest Rates, Real Activity, and Inflation

Simulation estimation of time-series models☆

The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence

CRISPR/Cas9-induced knockout and knock-in mutations in Chlamydomonas reinhardtii

Current account and budget deficits: twins or distant cousins?