State Space Modeling Using<i>SAS</i>

Selukar, Rajesh

doi:10.18637/jss.v041.i12

Cited by 13 publications

(14 citation statements)

References 13 publications

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“…The positive and statistically significant γ 7 and γ 8 estimates of the temporary price impact estimation also indicate that such tweets are linked to increasing temporary price impact (.032, p < .05, and <.033, p < .05, respectively), and they suggest that tweet valence generally contributes more noise to stock price than stock-relevant information. The findings reinforce the role of positive and negative valence FGCs and their impact on firm value (Tirunillai and Tellis 2012; Van Heerde, Gijsbrechts, and Pauwels 2015).…”

Section: Sandp 500 It Firms’ Use Of Twittersupporting

confidence: 75%

“…When there are instances of missing (or irregularly spaced) observations in , the Kalman 𝐯 𝐭 filter is unable to use the measurement equation (Equation 1); however, the transition equation (Equation 2) can be used since it depends on the previously estimated state ( depends on ). Indeed, Kalman filtering suggests that with missing 𝐦 𝐭 + 𝟏 𝐦 𝐭 observations in , the best estimation for is simply the evaluation of the transition equation The estimated state-space 𝐯 𝐭 𝐦 𝐭 model's source code in SAS is presented in Selukar (2011). Step three (estimation with the Kalman filter): We use the Kalman filter to evaluate the conditional mean and variances of the state vector (ignoring the stock notation and period 𝐦 𝐭 s…”

Section: Investigating the Permanent And Temporary Price Impact Of Tweetsmentioning

confidence: 99%

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Measuring the Real-Time Stock Market Impact of Firm-Generated Content

Lacka¹,

Boyd²,

Ibikunle³

et al. 2021

Journal of Marketing

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Firms increasingly follow an ‘always on’ philosophy, producing multiple pieces of firm-generated content (FGC) throughout the day. Current methodologies used in marketing are unsuited to unbiasedly capturing the impact of FGC disseminated intermittently throughout the day in stock markets characterized by ultra-high frequency trading. They also neither distinguish between the permanent (i.e. long-term) and temporary (i.e. short-term) price impacts nor identify FGC attributes capable of generating these price impacts. In this study, the authors define price impact as the impact on the variance of stock price. Employing a market microstructure approach to exploit the variance of high frequency changes in stock price the authors estimate the permanent and temporary price impacts of the firm-generated Twitter content of S&P 500 IT firms. The authors find that firm-generated tweets induce both permanent and temporary price impacts, which are linked to tweet attributes; valence and subject matter. Tweets reflecting only valence or subject matter concerning consumer or competitor orientation result in temporary price impacts, while those embodying both attributes generate permanent price impact; negative valence tweets about competitors generate the largest permanent price impacts. Building on these findings, the authors offer suggestions to marketing managers on the design of intraday FGC.

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Section: Sandp 500 It Firms’ Use Of Twittersupporting

confidence: 75%

Section: Investigating the Permanent And Temporary Price Impact Of Tweetsmentioning

confidence: 99%

Measuring the Real-Time Stock Market Impact of Firm-Generated Content

Lacka¹,

Boyd²,

Ibikunle³

et al. 2021

Journal of Marketing

View full text Add to dashboard Cite

show abstract

“…There are some commercial and open-source pieces of software similar and complementary to ECOTOOL: Econometrics and GARCH official MATLAB toolboxes [1], CAPTAIN [2], SCA [3], TRAMO/SEATS [4], forecast package in R [5], gretl [6], STAMP [7], Eviews [8], SAS [9], Stata [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Time series analysis and forecasting with ECOTOOL

Pedregal

2019

PLoS ONE

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This paper presents ECOTOOL, a new free MATLAB toolbox that embodies several routines for identification, validation and forecasting of dynamic models. The toolbox includes a wide range of exploratory, descriptive and diagnostic statistical tools with visual support, designed in easy-to-use Graphical User Interfaces. It also incorporates complex automatic procedures for identification, exact maximum likelihood estimation and outlier detection for many types of models available in the literature (like multi-seasonal ARIMA models, transfer functions, Exponential Smoothing, Unobserved Components, VARX). ECOTOOL is the outcome of a long period of programming effort with the aim of producing a user friendly toolkit such that, just a few lines of code written in MATLAB are able to perform a comprehensive analysis of time series. The toolbox is supplied with an in-depth documentation system and online help and is available on the internet. The paper describes the main functionalities of the toolbox, and its power is shown working on several real examples.

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“…State space methods have been implemented in some statistical software packages, such as STAMP (Koopman, Harvey, Doornik, and Shephard 2009;Mendelssohn 2011), REGCMPNT (Bell 2011), R (Petris and Petrone 2011), State Space Models (SSM) toolbox for MATLAB (Peng and Aston 2011), SAS (Selukar 2011), EViews (Van den Bossche 2011), GAUSS (Aptech Systems, Inc. 2006), Stata (Drukker and Gates 2011), gretl (Lucchetti 2011), RATS (Doan 2011) and SsfPack (Pelagatti 2011). See the Special Volume 41 (Commandeur, Koopman, and Ooms 2011) of the Journal of Statistical Software for a discussion of these packages.…”

Section: Introductionmentioning

confidence: 99%

SSMMATLAB: A Set ofMATLABPrograms for the Statistical Analysis of State Space Models

Gómez¹

2015

J. Stat. Soft.

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This article discusses and describes SSMMATLAB, a set of programs written by the author in MATLAB for the statistical analysis of state space models. The state space model considered is very general. It may have univariate or multivariate observations, time-varying system matrices, exogenous inputs, regression effects, incompletely specified initial conditions, such as those that arise with cointegrated VARMA models, and missing values. There are functions to put frequently used models, such as multiplicative VARMA models, VARMAX models in echelon form, cointegrated VARMA models, and univariate structural or ARIMA model-based unobserved components models, into state space form. There are also functions to implement the Hillmer-Tiao canonical decomposition and the smooth trend and cycle estimation proposed by Gómez (2001). Once the model is in state space form, other functions can be used for likelihood evaluation, model estimation, forecasting and smoothing. A set of examples is presented in the SSMMATLAB manual to illustrate the use of these functions.

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State Space Modeling UsingSAS

Cited by 13 publications

References 13 publications

Measuring the Real-Time Stock Market Impact of Firm-Generated Content

Measuring the Real-Time Stock Market Impact of Firm-Generated Content

Time series analysis and forecasting with ECOTOOL

SSMMATLAB: A Set ofMATLABPrograms for the Statistical Analysis of State Space Models

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