WASHINGTON – As more than 33 million U.S. workers have lost their jobs since March amid the pandemic-induced economic crisis, immigrants are among the most vulnerable: They are more likely than the U.S. born to be laid off and to live in communities with high COVID-19 infection rates, and less likely to have health insurance coverage and access to a doctor or other usual source of health care.

Interested in answers to some of the most frequently asked questions about immigration and immigrants in the United States? This incredible resource collects in one place top statistics from authoritative government and nongovernmental sources, offering a snapshot of the immigrant population, visa and enforcement statistics, and data on emerging trends, including the slowing of growth of the foreign-born population, changing origins, and increasing educational levels.

This recipe was featured on Foodie Tuesday, a weekly segment with Raf Epstein on Drive, 774 ABC Melbourne, 3:30 PM, courtesy of Alice in Frames. www.aliceinframes.com

Growing numbers of African and Asian migrants are moving through Latin America, many hoping to reach the United States or Canada after expensive, arduous, and often dangerous journeys that can take months or even years. As more extracontinental migrants transit through South and Central America, Colombia, Panama, and Costa Rica have developed the most comprehensive policies to manage these flows, sometimes working in coordination with the U.S. government.

Until recently, the Venezuelan immigrant population in the United States was relatively small compared others from South America. But it has grown significantly, reaching 394,000 in 2018, as Venezuela's destabilization has driven large-scale emigration. Compared to other immigrants in the United States, Venezuelans have higher levels of education but are also more likely to live in poverty, as this Spotlight explores.

OBJECTIVE

To determine the prevalence of cognitive deficits and traditional diabetic complications and the association between metabolic factors and these outcomes.

OBJECTIVE

This study aimed to reveal the associations between the risk of new-onset type 2 diabetes and the duration of antidepressant use and the antidepressant dose, and between antidepressant use after diabetes onset and clinical outcomes.

This event hosted by MPI Europe and the Research Unit of the Expert Council of German Foundations on Integration and Migration featured a discussion on research into legal migration pathways for work and training for low- and middle-skilled migrants.

The first years of a child’s life are a time of immense growth, and exposure to trauma—if left unaddressed—can have significant, lifelong effects. This issue brief examines how young children of refugees and other immigrants may be affected by trauma, and what early childhood education and care programs, health-care providers, and others can do to mitigate its adverse effects.

A new Trump administration action requiring intending immigrants to prove they can purchase eligible health insurance within 30 days of arrival has the potential to block fully 65 percent of those who apply for a green card from abroad, MPI estimates.

Latinos and immigrants are at least twice as likely to lack health insurance coverage as the overall population in the Kansas City metropolitan area. This gap that has significant implications for the region, as Latinos and immigrants will form an ever-growing share of the area’s labor force and tax base amid anticipated declines in the native-born, non-Latino population.

As millions of U.S. workers lose jobs and the health insurance associated with them, Medicaid and similar programs are increasingly important for people seeking COVID-19 testing and treatment. Yet many low-income uninsured noncitizens, including green-card holders, are excluded from such programs because of their immigration status, as this fact sheet explores.

The COVID-19 coronavirus pandemic is causing a number of societal changes that are new to nearly all of us, with governments around the world locking down cities and countries in an attempt to slow the spread of the virus. This situation is pushing some people together, but pulling others apart—and we’ve never seen anything quite like it in the modern era. Countless media articles have been written about the ways in which this is affecting people’s sex lives and relationships; however, most of them are purely speculative. This led some of my colleagues at the Kinsey Institute and I to wonder what’s really happening—and we’ve designed a study to help us better understand how emergency situations like this affect people’s sexual and romantic lives, which may help us to better plan and prepare for similar events in the future.

Academic achievement at Ohio schools eligible for School Improvement Grants during the Obama administration increased for a few years, a new study says, but SIG's legacy remains complicated.

Georgia wants to allow districts to use a series of "formative assessments" instead of one big test at the end of the year.

The NFL is indeed discussing the possibility of playing games on Saturdays in the event that college football doesn't proceed with a season from September through December. As one source with direct knowledge of the discussions tells PFT, however, the league's strong preference is for college football to happen, as scheduled. No college football season [more]

Caterpillars -- Australia, Southern -- Identification -- Pictorial works.

Teachers across the state are outraged over a new rule that requires 15 hours of workforce-related professional development.

Paris : Rueff, 1896.

London : J. Murray, 1877.

London : W. and R. Chambers, 1859.

London : Sherwood, Gilbert, & Piper, 1844.

It's pretty obvious by now that many Democrats are growing increasingly uncomfortable supporting charter schools. But New Hampshire lawmakers have taken the unusual step of rejecting federal charter school grant money.

Rockville, Maryland : National Institute on Drug Abuse, 1988.

Beverly Hills : Sage Publications, 1976.

Scott H. Holan, Robert Lund, Ginger Davis

Source: Statist. Surv., Volume 4, 232--274.

Abstract:
Autoregressive moving-average (ARMA) difference equations are ubiquitous models for short memory time series and have parsimoniously described many stationary series. Variants of ARMA models have been proposed to describe more exotic series features such as long memory autocovariances, periodic autocovariances, and count support set structures. This review paper enumerates, compares, and contrasts the common variants of ARMA models in today’s literature. After the basic properties of ARMA models are reviewed, we tour ARMA variants that describe seasonal features, long memory behavior, multivariate series, changing variances (stochastic volatility) and integer counts. A list of ARMA variant acronyms is provided.

References:
Aknouche, A. and Guerbyenne, H. (2006). Recursive estimation of GARCH models. Communications in Statistics-Simulation and Computation 35 925–938.

Alzaid, A. A. and Al-Osh, M. (1990). An integer-valued pth-order autoregressive structure (INAR (p)) process. Journal of Applied Probability 27 314–324.

Anderson, P. L., Tesfaye, Y. G. and Meerschaert, M. M. (2007). Fourier-PARMA models and their application to river flows. Journal of Hydrologic Engineering 12 462–472.

Ansley, C. F. (1979). An algorithm for the exact likelihood of a mixed autoregressive-moving average process. Biometrika 66 59–65.

Basawa, I. V. and Lund, R. (2001). Large sample properties of parameter estimates for periodic ARMA models. Journal of Time Series Analysis 22 651–663.

Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics 21 79–109.

Bertelli, S. and Caporin, M. (2002). A note on calculating autocovariances of long-memory processes. Journal of Time Series Analysis 23 503–508.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 307–327.

Bollerslev, T. (2008). Glossary to ARCH (GARCH). CREATES Research Paper 2008-49.

Bollerslev, T., Engle, R. F. and Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. The Journal of Political Economy 96 116–131.

Bondon, P. and Palma, W. (2007). A class of antipersistent processes. Journal of Time Series Analysis 28 261–273.

Bougerol, P. and Picard, N. (1992). Strict stationarity of generalized autoregressive processes. The Annals of Probability 20 1714–1730.

Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (2008). Time Series Analysis: Forecasting and Control, 4th ed. Wiley, New Jersey.

Breidt, F. J., Davis, R. A. and Trindade, A. A. (2001). Least absolute deviation estimation for all-pass time series models. Annals of Statistics 29 919–946.

Brockwell, P. J. (1994). On continuous-time threshold ARMA processes. Journal of Statistical Planning and Inference 39 291–303.

Brockwell, P. J. (2001). Continuous-time ARMA processes. In Stochastic Processes: Theory and Methods, ( D. N. Shanbhag and C. R. Rao, eds.). Handbook of Statistics 19 249–276. Elsevier.

Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, 2nd ed. Springer, New York.

Brockwell, P. J. and Davis, R. A. (2002). Introduction to Time Series and Forecasting, 2nd ed. Springer, New York.

Brockwell, P. J. and Marquardt, T. (2005). Lèvy-driven and fractionally integrated ARMA processes with continuous-time paramaters. Statistica Sinica 15 477–494.

Chan, K. S. (1990). Testing for threshold autoregression. Annals of Statistics 18 1886–1894.

Chan, N. H. (2002). Time Series: Applications to Finance. John Wiley & Sons, New York.

Chan, N. H. and Palma, W. (1998). State space modeling of long-memory processes. Annals of Statistics 26 719–740.

Chan, N. H. and Palma, W. (2006). Estimation of long-memory time series models: A survey of different likelihood-based methods. Advances in Econometrics 20 89–121.

Chatfield, C. (2003). The Analysis of Time Series: An Introduction, 6th ed. Chapman & Hall/CRC, Boca Raton.

Chen, W., Hurvich, C. M. and Lu, Y. (2006). On the correlation matrix of the discrete Fourier transform and the fast solution of large Toeplitz systems for long-memory time series. Journal of the American Statistical Association 101 812–822.

Chernick, M. R., Hsing, T. and McCormick, W. P. (1991). Calculating the extremal index for a class of stationary sequences. Advances in Applied Probability 23 835–850.

Chib, S., Nardari, F. and Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics 134 341–371.

Cryer, J. D. and Chan, K. S. (2008). Time Series Analysis: With Applications in R. Springer, New York.

Cui, Y. and Lund, R. (2009). A new look at time series of counts. Biometrika 96 781–792.

Davis, R. A., Dunsmuir, W. T. M. and Wang, Y. (1999). Modeling time series of count data. In Asymptotics, Nonparametrics and Time Series, ( S. Ghosh, ed.). Statistics Textbooks Monograph 63–113. Marcel Dekker, New York.

Davis, R. A., Dunsmuir, W. and Streett, S. B. (2003). Observation-driven models for Poisson counts. Biometrika 90 777–790.

Davis, R. A. and Resnick, S. I. (1996). Limit theory for bilinear processes with heavy-tailed noise. The Annals of Applied Probability 6 1191–1210.

Deistler, M. and Hannan, E. J. (1981). Some properties of the parameterization of ARMA systems with unknown order. Journal of Multivariate Analysis 11 474–484.

Dufour, J. M. and Jouini, T. (2005). Asymptotic distribution of a simple linear estimator for VARMA models in echelon form. Statistical Modeling and Analysis for Complex Data Problems 209–240.

Dunsmuir, W. and Hannan, E. J. (1976). Vector linear time series models. Advances in Applied Probability 8 339–364.

Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford University Press, Oxford.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 987–1007.

Engle, R. F. (2002). Dynamic conditional correlation. Journal of Business and Economic Statistics 20 339–350.

Engle, R. F. and Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews 5 1–50.

Fuller, W. A. (1996). Introduction to Statistical Time Series, 2nd ed. John Wiley & Sons, New York.

Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis 4 221–238.

Gladyšhev, E. G. (1961). Periodically correlated random sequences. Soviet Math 2 385–388.

Granger, C. W. J. (1982). Acronyms in time series analysis (ATSA). Journal of Time Series Analysis 3 103–107.

Granger, C. W. J. and Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht Göttingen.

Granger, C. W. J. and Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1 15–29.

Gray, H. L., Zhang, N. F. and Woodward, W. A. (1989). On generalized fractional processes. Journal of Time Series Analysis 10 233–257.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press, Princeton, New Jersey.

Hannan, E. J. (1955). A test for singularities in Sydney rainfall. Australian Journal of Physics 8 289–297.

Hannan, E. J. (1969). The identification of vector mixed autoregressive-moving average system. Biometrika 56 223–225.

Hannan, E. J. (1970). Multiple Time Series. John Wiley & Sons, New York.

Hannan, E. J. (1976). The identification and parameterization of ARMAX and state space forms. Econometrica 44 713–723.

Hannan, E. J. (1979). The Statistical Theory of Linear Systems. In Developments in Statistics ( P. R. Krishnaiah, ed.) 83–121. Academic Press, New York.

Hannan, E. J. and Deistler, M. (1987). The Statistical Theory of Linear Systems. John Wiley & Sons, New York.

Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.

Haslett, J. and Raftery, A. E. (1989). Space-time modelling with long-memory dependence: Assessing Ireland’s wind power resource. Applied Statistics 38 1–50.

Hosking, J. R. M. (1981). Fractional differencing. Biometrika 68 165–176.

Hui, Y. V. and Li, W. K. (1995). On fractionally differenced periodic processes. Sankhyā: The Indian Journal of Statistics, Series B 57 19–31.

Jacobs, P. A. and Lewis, P. A. W. (1978a). Discrete time series generated by mixtures. I: Correlational and runs properties. Journal of the Royal Statistical Society. Series B (Methodological) 40 94–105.

Jacobs, P. A. and Lewis, P. A. W. (1978b). Discrete time series generated by mixtures II: Asymptotic properties. Journal of the Royal Statistical Society. Series B (Methodological) 40 222–228.

Jacobs, P. A. and Lewis, P. A. W. (1983). Stationary discrete autoregressive-moving average time series generated by mixtures. Journal of Time Series Analysis 4 19–36.

Jones, R. H. (1980). Maximum likelihood fitting of ARMA models to time series with missing observations. Technometrics 22 389–395.

Jones, R. H. and Brelsford, W. M. (1967). Time series with periodic structure. Biometrika 54 403–408.

Kedem, B. and Fokianos, K. (2002). Regression Models for Time Series Analysis. John Wiley & Sons, New Jersey.

Ko, K. and Vannucci, M. (2006). Bayesian wavelet-based methods for the detection of multiple changes of the long memory parameter. IEEE Transactions on Signal Processing 54 4461–4470.

Kohn, R. (1979). Asymptotic estimation and hypothesis testing results for vector linear time series models. Econometrica 47 1005–1030.

Kokoszka, P. S. and Taqqu, M. S. (1995). Fractional ARIMA with stable innovations. Stochastic Processes and their Applications 60 19–47.

Kokoszka, P. S. and Taqqu, M. S. (1996). Parameter estimation for infinite variance fractional ARIMA. Annals of Statistics 24 1880–1913.

Lawrance, A. J. and Lewis, P. A. W. (1980). The exponential autoregressive-moving average EARMA(p,q) process. Journal of the Royal Statistical Society. Series B (Methodological) 42 150–161.

Ling, S. and Li, W. K. (1997). On fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity. Journal of the American Statistical Association 92 1184–1194.

Liu, J. and Brockwell, P. J. (1988). On the general bilinear time series model. Journal of Applied Probability 25 553–564.

Lund, R. and Basawa, I. V. (2000). Recursive prediction and likelihood evaluation for periodic ARMA models. Journal of Time Series Analysis 21 75–93.

Lund, R., Shao, Q. and Basawa, I. (2006). Parsimonious periodic time series modeling. Australian & New Zealand Journal of Statistics 48 33–47.

Lütkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag, New York.

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer, New York.

MacDonald, I. L. and Zucchini, W. (1997). Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman & Hall/CRC, Boca Raton.

Mann, H. B. and Wald, A. (1943). On the statistical treatment of linear stochastic difference equations. Econometrica 11 173–220.

Marriott, J., Ravishanker, N., Gelfand, A. and Pai, J. (1996). Bayesian analysis of ARMA processes: Complete sampling-based inference under exact likelihoods. In Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner ( D. Berry, K. Challoner and J. Geweke, eds.) 243–256. Wiley, New York.

McKenzie, E. (1988). Some ARMA models for dependent sequences of Poisson counts. Advances in Applied Probability 20 822–835.

Mikosch, T. and Starica, C. (2004). Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Review of Economics and Statistics 86 378–390.

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59 347–370.

Nelson, D. B. and Cao, C. Q. (1992). Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics 10 229–235.

Ooms, M. and Franses, P. H. (2001). A seasonal periodic long memory model for monthly river flows. Environmental Modelling & Software 16 559–569.

Pagano, M. (1978). On periodic and multiple autoregressions. Annals of Statistics 6 1310–1317.

Pai, J. S. and Ravishanker, N. (1998). Bayesian analysis of autoregressive fractionally integrated moving-average processes. Journal of Time Series Analysis 19 99–112.

Palma, W. (2007). Long-Memory Time Series: Theory and Methods. John Wiley & Sons, New Jersey.

Palma, W. and Chan, N. H. (2005). Efficient estimation of seasonal long-range-dependent processes. Journal of Time Series Analysis 26 863–892.

Pfeifer, P. E. and Deutsch, S. J. (1980). A three-stage iterative procedure for space-time modeling. Technometrics 22 35–47.

Prado, R. and West, M. (2010). Time Series Modeling, Computation and Inference. Chapman & Hall/CRC, Boca Raton.

Quoreshi, A. M. M. S. (2008). A long memory count data time series model for financial application. Preprint.

R Development Core Team, (2010). R: A Language and Environment for Statistical Computing. http://www.R-project.org.

Ravishanker, N. and Ray, B. K. (1997). Bayesian analysis of vector ARMA models using Gibbs sampling. Journal of Forecasting 16 177–194.

Ravishanker, N. and Ray, B. K. (2002). Bayesian prediction for vector ARFIMA processes. International Journal of Forecasting 18 207–214.

Reinsel, G. C. (1997). Elements of Multivariate Time Series Analysis. Springer, New York.

Resnick, S. I. and Willekens, E. (1991). Moving averages with random coefficients and random coefficient autoregressive models. Communications in Statistics. Stochastic Models 7 511–525.

Rootzén, H. (1986). Extreme value theory for moving average processes. The Annals of Probability 14 612–652.

Scotto, M. G. (2007). Extremes for solutions to stochastic difference equations with regularly varying tails. REVSTAT–Statistical Journal 5 229–247.

Shao, Q. and Lund, R. (2004). Computation and characterization of autocorrelations and partial autocorrelations in periodic ARMA models. Journal of Time Series Analysis 25 359–372.

Shumway, R. H. and Stoffer, D. S. (2006). Time Series Analysis and its Applications: With R Examples, 2nd ed. Springer, New York.

Silvennoinen, A. and Teräsvirta, T. (2009). Multivariate GARCH models. In Handbook of Financial Time Series ( T. Andersen, R. Davis, J. Kreib, and T. Mikosch, eds.) Springer, New York.

Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics 53 165–188.

Startz, R. (2008). Binomial autoregressive moving average models with an application to U.S. recessions. Journal of Business and Economic Statistics 26 1–8.

Stramer, O., Tweedie, R. L. and Brockwell, P. J. (1996). Existence and stability of continuous time threshold ARMA processes. Statistica Sinica 6 715–732.

Subba Rao, T. (1981). On the theory of bilinear time series models. Journal of the Royal Statistical Society. Series B (Methodological) 43 244–255.

Tong, H. and Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological) 42 245–292.

Troutman, B. M. (1979). Some results in periodic autoregression. Biometrika 66 219–228.

Tsai, H. (2009). On continuous-time autoregressive fractionally integrated moving average processes. Bernoulli 15 178–194.

Tsai, H. and Chan, K. S. (2000). A note on the covariance structure of a continuous-time ARMA process. Statistica Sinica 10 989–998.

Tsai, H. and Chan, K. S. (2005). Maximum likelihood estimation of linear continuous time long memory processes with discrete time data. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 67 703–716.

Tsai, H. and Chan, K. S. (2008). A note on inequality constraints in the GARCH model. Econometric Theory 24 823–828.

Tsay, R. S. (1989). Parsimonious parameterization of vector autoregressive moving average models. Journal of Business and Economic Statistics 7 327–341.

Tunnicliffe-Wilson, G. (1979). Some efficient computational procedures for high order ARMA models. Journal of Statistical Computation and Simulation 8 301–309.

Ursu, E. and Duchesne, P. (2009). On modelling and diagnostic checking of vector periodic autoregressive time series models. Journal of Time Series Analysis 30 70–96.

Vecchia, A. V. (1985a). Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics 27 375–384.

Vecchia, A. V. (1985b). Periodic autoregressive-moving average (PARMA) modeling with applications to water resources. Journal of the American Water Resources Association 21 721–730.

Vidakovic, B. (1999). Statistical Modeling by Wavelets. John Wiley & Sons, New York.

West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd ed. Springer, New York.

Wold, H. (1954). A Study in the Analysis of Stationary Time Series. Almquist & Wiksell, Stockholm.

Woodward, W. A., Cheng, Q. C. and Gray, H. L. (1998). A k-factor GARMA long-memory model. Journal of Time Series Analysis 19 485–504.

Zivot, E. and Wang, J. (2006). Modeling Financial Time Series with S-PLUS, 2nd ed. Springer, New York.

Names, Personal -- England.

Klemm (Family)

Names, Personal -- Welsh.

Names, Personal -- Scottish.

Cheesman, John -- Family.