Artigos de revistas: "Lagged variable"

1

Keele, Luke, e Nathan J. Kelly. "Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables". Political Analysis 14, n.º 2 (2006): 186–205. http://dx.doi.org/10.1093/pan/mpj006.

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A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effects in political processes and as a method for ridding the model of autocorrelation. But recent work contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residual autocorrelation is present, the lagged dependent variable causes the coefficients for explanatory variables to be biased downward. We use a Monte Carlo analysis to assess empirically how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. In our analysis, we compare the performance of the lagged dependent variable model to several other time series models. We show that while the lagged dependent variable is inappropriate in some circumstances, it remains an appropriate model for the dynamic theories often tested by applied analysts. From the analysis, we develop several practical suggestions on when and how to use lagged dependent variables on the right-hand side of a model.

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Matyjaszek, Marta, Gregorio Fidalgo Valverde, Alicja Krzemień, Krzysztof Wodarski e Pedro Riesgo Fernández. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production". Energies 13, n.º 8 (18 de abril de 2020): 2017. http://dx.doi.org/10.3390/en13082017.

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This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-delayed terms or past values forming the lagged variables and (2) to improve the forecast accuracy by adding intrinsic signals to the lagged variables. The conclusions clearly are in opposition to the actual scientific literature: when addressing the lagged variable size, the results do not confirm relationships among their size, representativeness and estimation accuracy. It is also possible to verify an important effect of the results on the lagged variable size. Finally, adding the order in the time series of the lagged variables to form the predictor variables improves the forecast accuracy in most cases.

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Grubb, David, e James Symons. "Bias in Regressions With a Lagged Dependent Variable". Econometric Theory 3, n.º 3 (junho de 1987): 371–86. http://dx.doi.org/10.1017/s0266466600010458.

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We give an expression to order O(T-1), where T is the sample size, for bias to the estimated coefficient on a lagged dependent variable when all other regressors are exogenous. The general expression is a nonlinear function of the coefficient on the lagged dependent variable, the autoregressive structure of the exogenous variables, and the coefficients on the exogenous variables. The maximum bias that can arise is a linear function of the number of exogenous regressors in the estimating equation.

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Višić, Josipa, e Blanka Škrabić Perić. "The determinants of value of incoming cross-border mergers & acquisitions in European transition countries". Communist and Post-Communist Studies 44, n.º 3 (10 de agosto de 2011): 173–82. http://dx.doi.org/10.1016/j.postcomstud.2011.07.004.

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This research aims to determine variables that affect the aggregate value of incoming cross-border M&As in European transitional countries. Dynamic panel models have been estimated using Arellano and Bond GMM estimator for period between year 1994 and 2008. The ratio of the total value of cross-border M&A to GDP of the country is the dependent variable. Independent variables include following indicators: lagged value of cross-border M&A to GDP, lagged GDP per capita, lagged GDP growth, inflation, interest rate spread, lagged private credit to GDP ratio, market capitalization to GDP ratio, lagged rule of law and lagged control of corruption.

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Wirjanto, Tony S., e Robert A. Amano. "Nonstationary regression models with a lagged dependent variable". Communications in Statistics - Theory and Methods 25, n.º 7 (janeiro de 1996): 1489–503. http://dx.doi.org/10.1080/03610929608831780.

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6

Marsh, Patrick. "Constructing Optimal tests on a Lagged dependent variable". Journal of Time Series Analysis 28, n.º 5 (setembro de 2007): 723–43. http://dx.doi.org/10.1111/j.1467-9892.2007.00536.x.

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7

Hamid, Kashif, Zahid Hussain e Muhammad Mudasar Ghafoor. "Abnormal Returns, Corporate Financial Policies and the Dynamics of Leverage: Empirical Evidence from Non-Financial Sector of Pakistan". Review of Economics and Development Studies 6, n.º 1 (31 de março de 2020): 153–66. http://dx.doi.org/10.47067/reads.v6i1.193.

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The aim of this study is to evaluate impact of corporate financial policies and the dynamics of leverage on financial performance of non-financial sector in Pakistan. In this study we used the data from Fertilizer, Chemical and Cement sector for the period 2008-2017. Abnormal return has been taken as dependent variable and Change in cash to lagged market values, Change in EBIT to lagged market values, Change in dividend to lagged market value, Net Financing to lagged market value, Lagged cash values to lagged market values, Lagged cash values to lagged market values crossed by change in cash to lagged market value, Change in total assets net of cash to lagged market values, Change in interest to lagged market values, Operating leverage, Financial leverage, Total leverage, Leverage ratio, Leverage ratio to change in cash crossed by lagged market values and WACC are taken as explanatory variables. OLS, Fixed effect and Random effect models has been used to express the impact of these variables on return. Hence it is concluded that leverage dynamics are significant contributors in designing the corporate financial policies. Corporate financial policies have significant impact on the financial performance of the non-financial sector of Pakistan.

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Pugh, Sierra, Matthew J. Heaton, Jeff Svedin e Neil Hansen. "Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture". Journal of Agricultural, Biological and Environmental Statistics 24, n.º 4 (3 de maio de 2019): 634–50. http://dx.doi.org/10.1007/s13253-019-00365-3.

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Maeshiro, Asatoshi. "Teaching Regressions with a Lagged Dependent Variable and Autocorrelated Disturbances". Journal of Economic Education 27, n.º 1 (janeiro de 1996): 72–84. http://dx.doi.org/10.1080/00220485.1996.10844896.

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Kiviet, Jan F., e Garry D. A. Phillips. "Alternative Bias Approximations in Regressions with a Lagged-Dependent Variable". Econometric Theory 9, n.º 1 (janeiro de 1993): 62–80. http://dx.doi.org/10.1017/s0266466600007337.

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The small sample bias of the least-squares coefficient estimator is examined in the dynamic multiple linear regression model with normally distributed whitenoise disturbances and an arbitrary number of regressors which are all exogenous except for the one-period lagged-dependent variable. We employ large sample (T → ∞) and small disturbance (σ → 0) asymptotic theory and derive and compare expressions to O(T−1) and to O(σ2), respectively, for the bias in the least-squares coefficient vector. In some simulations and for an empirical example, we examine the mean (squared) error of these expressions and of corrected estimation procedures that yield estimates that are unbiased to O(T−l) and to O(σ2), respectively. The large sample approach proves to be superior, easily applicable, and capable of generating more efficient and less biased estimators.

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Maeshiro, Asatoshi. "Teaching Regressions with a Lagged Dependent Variable and Autocorrelated Disturbances". Journal of Economic Education 27, n.º 1 (1996): 72. http://dx.doi.org/10.2307/1183011.

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Srivastava, Virendra K., e Aman Ullah. "Stein-rule estimation in models with a lagged-dependemt variable". Communications in Statistics - Theory and Methods 24, n.º 5 (janeiro de 1995): 1343–53. http://dx.doi.org/10.1080/03610929508831557.

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13

Liu, Tzu-Ming. "Habit formation or word of mouth: What does lagged dependent variable in tourism demand models imply?" Tourism Economics 26, n.º 3 (12 de abril de 2019): 461–74. http://dx.doi.org/10.1177/1354816619843041.

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The effects of habit formation/persistence (HFP) and word of mouth (WOM) each play a critical role in influencing tourists’ decisions regarding whether to visit tourism destinations and therefore tourism policies and tourism management resource allocations. Nevertheless, in previous tourism demand studies, the two effects have been represented by the same time-lagged dependent variable, which makes the variable have an ambiguous meaning and biases the empirical results. The purpose of this study is to solve the ambiguity of a lagged dependent variable in tourism demand. We used economic theories regarding internal habits and external habits to clarify the meanings of HFP and WOM and revised the tourism demand model into a spatial dynamic panel model (SDPM). The empirical results suggested that an SDPM is a more accurate model for modeling tourism demand. The effects of variables in an SDPM are more consistent with theoretical expectations.

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14

Parasuraman, N. R., P. Janaki Ramudu e Nusrathuunisa . "Does Lintner model of dividend payout hold good? An Empirical evidence from BSE SENSEX firms." SDMIMD Journal of Management 3, n.º 2 (1 de setembro de 2012): 63. http://dx.doi.org/10.18311/sdmimd/2012/2743.

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This study primarily investigates into as to what influenced the dividends payment of BSE constituent companies for the years 2002 through as latest as 2011. The primary model used is that of Lintner (1956) with addition of relevant factors. The study tests three models including Lintner's basic model. While dividends paid is criterion variable in all the models, basic earnings and lagged dividends are predictor variables in the first model (Lintner model, 1956), cash earnings and lagged dividends in the second model and growth opportunities (depreciation and capital expenditure) in the third model are the predictor variables. The study tests the hypotheses if the dividends paid (criterion variable) depended on basic earnings, lagged dividends, cash earnings and capital expenditure. The multiple regression analysis has been performed using SPSS 15.0 version through ENTER method for every year and for all the years on an aggregate basis across the sample companies. Significance 'F' revealed that in all the three models dividends paid depended significantly (at 5% significance level) on all predictors variables. The value of multiple 'R' indicated that the models were very strong. Coefficient of determination (R2) also revealed that the explained portion of the relationship between criterion and predictor variables has been very high and significant enough to accept the model fit. However, standardized beta co-efficients (â) and 't' statistic revealed that basic earnings, cash earnings and lagged dividends exercised highest impact on dividends paid in most of the years during the study period. On the other hand, other predictor variables, depreciation and capital expenditure, did not have any significant impact on the dividends paid. The Durbin Watson coefficient indicated that multi co-linearity among predictor variables was strong enough to accept the validity of the model almost during the entire period of the study. Thus, the results and findings of the study support the prevalence and relevance of Lintner model of dividend policy. This means that the finance manager can't afford to ignore the variables like earnings capacity and lagged dividends while framing a dividend policy.

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Aladag, Cagdas Hakan, Ufuk Yolcu, Erol Egrioglu e Eren Bas. "Fuzzy lagged variable selection in fuzzy time series with genetic algorithms". Applied Soft Computing 22 (setembro de 2014): 465–73. http://dx.doi.org/10.1016/j.asoc.2014.03.028.

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Ningrum, Dewi Kusuma, e Sugiyarto Surono. "Comparison the Error Rate of Autoregressive Distributed Lag (ARDL) and Vector Autoregressive (VAR) (Case study: Forecast of Export Quantities in DIY)". JURNAL EKSAKTA 18, n.º 2 (27 de setembro de 2018): 167–77. http://dx.doi.org/10.20885/eksakta.vol18.iss2.art8.

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Forecasting is estimating the size or number of something in the future. Regression model that enters current independent variable value, and lagged value is called distributed-lag model, if it enters one or more lagged value, it is called autoregressive. Koyck method is used for dynamic model which the lagged length is unknown, for the known lagged length it is used the Almon method. Vector Autoregressive (VAR) is a method that explains every variable in the model depend on the lag movement from the variable itself and all the others variable. This research aimed to explain the application of Autoregressive distributed-lag model and Vector Autoregressive (VAR) method for the forecasting for export amount in DIY. It takes export amount in DIY and inflation data, kurs, and Indonesias foreign exchange reserve. Forecasting formation: defining Koyck and Almon distributed-lag dynamic model, then the best model is chosen and distribution-lag dynamic forecasting is performed. After that it is performed stationary test, co-integration test, optimal lag examination, granger causality test, parameter estimation, VAR model stability, and performs forecasting with VAR method. The forecasting result shows MAPE value from ARDL method obtained is 0.475812%, while MAPE value from VAR method is 0.464473%. Thus it can be concluded that Vector Autoregressive (VAR) method is more effective to be used in case study of export amount in DIY forecasting. Keywords: Koyck; Almon; Lag; Autoregressive Distributed-Lag; Vector Autoregressive;

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17

Vrac, Mathieu, e Soulivanh Thao. "R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resampling". Geoscientific Model Development 13, n.º 11 (6 de novembro de 2020): 5367–87. http://dx.doi.org/10.5194/gmd-13-5367-2020.

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Abstract. Over the last few years, multivariate bias correction methods have been developed to adjust spatial and/or inter-variable dependence properties of climate simulations. Most of them do not correct – and sometimes even degrade – the associated temporal features. Here, we propose a multivariate method to adjust the spatial and/or inter-variable properties while also accounting for the temporal dependence, such as autocorrelations. Our method consists of an extension of a previously developed approach that relies on an analogue-based method applied to the ranks of the time series to be corrected rather than to their “raw” values. Several configurations are tested and compared on daily temperature and precipitation simulations over Europe from one Earth system model. Those differ by the conditioning information used to compute the analogues and can include multiple variables at each given time, a univariate variable lagged over several time steps or both – multiple variables lagged over time steps. Compared to the initial approach, results of the multivariate corrections show that, while the spatial and inter-variable correlations are still satisfactorily corrected even when increasing the dimension of the conditioning, the temporal autocorrelations are improved with some of the tested configurations of this extension. A major result is also that the choice of the information to condition the analogues is key since it partially drives the capability of the proposed method to reconstruct proper multivariate dependences.

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18

Ding, Peng, e Fan Li. "A Bracketing Relationship between Difference-in-Differences and Lagged-Dependent-Variable Adjustment". Political Analysis 27, n.º 4 (11 de julho de 2019): 605–15. http://dx.doi.org/10.1017/pan.2019.25.

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Difference-in-differences is a widely used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale-dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, Angrist and Pischke (2009) show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting. We provide three examples to illustrate the theoretical results with replication files in Ding and Li (2019).

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Ahn, So-Yeon, Se-Jun Jin e Seung-Hoon Yoo. "Estimation of the electricity demand function using a lagged dependent variable model". Journal of Energy Engineering 25, n.º 2 (30 de junho de 2016): 37–44. http://dx.doi.org/10.5855/energy.2016.25.2.037.

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Klavans, Jeremy M., Amy C. Clement e Mark A. Cane. "Variable External Forcing Obscures the Weak Relationship between the NAO and North Atlantic Multidecadal SST Variability". Journal of Climate 32, n.º 13 (10 de junho de 2019): 3847–64. http://dx.doi.org/10.1175/jcli-d-18-0409.1.

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Abstract North Atlantic sea surface temperatures (SST) exhibit a lagged response to the North Atlantic Oscillation (NAO) in both models and observations, which has previously been attributed to changes in ocean heat transport. Here we examine the lagged relationship between the NAO and Atlantic multidecadal variability (AMV) in the context of the two other major components of the AMV: atmospheric noise and external forcing. In preindustrial control runs, we generally find that after accounting for spurious signals introduced by filtering, the SST response to the NAO is only statistically significant in the subpolar gyre. Further, the lagged SST response to the NAO is small in magnitude and offers a limited contribution to the AMV pattern, statistics, or predictability. When climate models include variable external forcing, the relationship between the NAO and AMV is obscured and becomes inconsistent. In these historically forced runs, knowledge of the prior NAO offers reduced predictability. The differences between the preindustrial and the historically forced ensembles suggest that we do not yet have enough observational data to surmise the true NAO–AMV relationship and add evidence that external forcing plays a substantial role in producing the AMV.

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Risma, Okta Rabiana, T. Zulham e Taufiq C. Dawood. "PENGARUH SUKU BUNGA, PRODUK DOMESTIK BRUTO DAN NILAI TUKAR TERHADAP EKSPOR DI INDONESIA". JURNAL PERSPEKTIF EKONOMI DARUSSALAM 4, n.º 2 (1 de julho de 2019): 300–317. http://dx.doi.org/10.24815/jped.v4i2.13027.

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This research aims to analyze the level of exports in Indonesia by using Time Series data from the year 1990 to 2015 against a variable interest rate loands, gross domestic product, and the exchange rate. Methods of analysis used i.e, Auto Regressive Distributed Lagged (ARDL). The results showed that the three variables have no Granger which is caused by the difference of the order on the test stasioner. Based on a test of wald for the short term that gained and the long-term gross domestic product, exchange rates and interest rates significantly influential credit toward export.Keywords:ARDL, export, interest rate loands, gross domestic product, exchange rates.AbstrakPenelitian ini bertujuan untuk menganalisis tingkat ekspor di Indonesia dengan menggunakan data Time Series dari tahun 1990 sampai 2015 terhadap variabel suku bunga kredit, produk domestik bruto, dan nilai tukar. Metode analisis yang digunakan yaitu AutoRegressive Distributed Lagged (ARDL).Hasil penelitian menunjukkan bahwa ketiga variabel tidak memiliki kointegrasi yang disebabkan oleh perbedaan ordo pada uji stasionernya. Berdasarkan uji wald didapat bahwa untuk jangka pendek dan jangka panjang produk domestik bruto, nilai tukar dan suku bunga kredit berpengaruh secara signifikan terhadap ekspor.

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Sher, Kenneth J., Mark D. Wood, Phillip K. Wood e Gail Raskin. "Alcohol outcome expectancies and alcohol use: A latent variable cross-lagged panel study." Journal of Abnormal Psychology 105, n.º 4 (novembro de 1996): 561–74. http://dx.doi.org/10.1037/0021-843x.105.4.561.

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Huynh, Frank C. H. "Testing of functional forms of regressions with lagged dependent variable and autocorrelated errors". Economics Letters 18, n.º 4 (janeiro de 1985): 345–49. http://dx.doi.org/10.1016/0165-1765(85)90050-3.

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Ugbaje, Sabastine, e Thomas Bishop. "Hydrological Control of Vegetation Greenness Dynamics in Africa: A Multivariate Analysis Using Satellite Observed Soil Moisture, Terrestrial Water Storage and Precipitation". Land 9, n.º 1 (10 de janeiro de 2020): 15. http://dx.doi.org/10.3390/land9010015.

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Vegetation activity in many parts of Africa is constrained by dynamics in the hydrologic cycle. Using satellite products, the relative importance of soil moisture, rainfall, and terrestrial water storage (TWS) on vegetation greenness seasonality and anomaly over Africa were assessed for the period between 2003 and 2015. The possible delayed response of vegetation to water availability was considered by including 0–6 and 12 months of the hydrological variables lagged in time prior to the vegetation greenness observations. Except in the drylands, the relationship between vegetation greenness seasonality and the hydrological measures was generally strong across Africa. Contrarily, anomalies in vegetation greenness were generally less coupled to anomalies in water availability, except in some parts of eastern and southern Africa where a moderate relationship was evident. Soil moisture was the most important variable driving vegetation greenness in more than 50% of the areas studied, followed by rainfall when seasonality was considered, and by TWS when the monthly anomalies were used. Soil moisture and TWS were generally concurrent or lagged vegetation by 1 month, whereas precipitation lagged vegetation by 1–2 months. Overall, the results underscore the pre-eminence of soil moisture as an indicator of vegetation greenness among satellite measured hydrological variables.

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Rich, Alexander R., e Martha Scovel. "Causes of Depression in College Students: A Cross-Lagged Panel Correlational Analysis". Psychological Reports 60, n.º 1 (fevereiro de 1987): 27–30. http://dx.doi.org/10.2466/pr0.1987.60.1.27.

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A study was conducted over a 6-wk. period to investigate factors associated with the development of depression among college students. Subjects were 134 first-semester freshmen conscripted from the general psychology subject pool. Scores were obtained from self-report questionnaires involving measures of life events, social support, and cognitive appraisal at the beginning of the semester and again later. The result of stepwise multiple regression analyses of Time 1 and Time 2 administrations indicated that the variables most strongly and consistently associated with depression were loneliness, interpersonal mistrust, and neuroticism. Two-panel cross-lagged correlational analyses indicated that loneliness preceded depression. Moreover, the data suggested that interpersonal mistrust and neuroticism were related to depression through the variable of loneliness.

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Ali, Hina, Fatima Farooq e Najma Mumtaz. "Trade Openness, External Debt and Growth Nexus in Pakistan: Empirical Evidence from ARDL Modeling Approach & Co-Integration Causality Analysis". Review of Economics and Development Studies 2, n.º 2 (31 de dezembro de 2016): 93–102. http://dx.doi.org/10.26710/reads.v2i2.127.

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This Empirical study Explores the Influence of trade openness and external debt on economic growth by using time series data from 1974 -2016. Gross domestic Product (GDP) as dependent variable while Foreign Direct Investment, Inflation, External debt, Capital formation and Trade as explanatory variable are used. Unit Root Test applies to check the stationary of data in which GDP & INF are integrate at level 1(0) while the channel of variables like FDI, T, ED, CF are integrate at 1stdifference. Auto-regressive distributed lagged model (ARDL) technique applies for estimation. The study finds out the relation between channels of variable that how these variables are interrelated. The findings indicate that External debt and capital formation has Inverse influence on Economic growth while Trade Openness, Inflation, foreign Direct Investment has positive impact on economic growth.

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Chen, Yun, Xiaofeng Wu e Qian Lin. "Global Lagged Finite-Time Synchronization of Two Chaotic Lur’e Systems Subject to Time Delay". International Journal of Bifurcation and Chaos 25, n.º 12 (novembro de 2015): 1550161. http://dx.doi.org/10.1142/s0218127415501618.

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This paper investigates the global lagged finite-time synchronization of the master-slave Lur’e systems subject to time delay of signal transmission. By designing a variable-substitution and feedback controller, a master-slave finite-time synchronization scheme for the Lur’e systems with time delay is built up. Two delay-independent global lagged finite-time synchronization criteria are proved in the forms of linear matrix inequalities (LMIs), and the corresponding settling time of synchronization is analytically estimated. The obtained LMI criteria are applied to Chua’s oscillators, obtaining some easily implemented algebraic criteria under various single-variable-substitution and feedback controller, which are then optimized to improve their conservative property. Finally, several numerical examples are illustrated to verify the effectiveness of the optimized criteria.

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Bhar, Ramaprasad, Anastasios G. Malliaris e Mary Malliaris. "What Has Driven the U.S. Monthly Oil Production Since 2009? Empirical Results from Two Modeling Approaches". Journal of Risk and Financial Management 14, n.º 2 (18 de fevereiro de 2021): 81. http://dx.doi.org/10.3390/jrfm14020081.

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From the early 1970s to the Global Financial Crisis of 2007–09, U.S. crude oil production followed a declining trend. After the Global Financial Crisis, U.S. crude oil production increased rapidly. This paper addresses the important question “what economic factors have driven U.S. crude oil production since the Global Financial Crisis?”. We propose that factors such as: the price of oil, the one period lagged price of oil, the price of copper, the crude oil price volatility, the Trade Weighted U.S. Dollar Index, and the high yield index spread, are important explanatory variables. Using two modeling approaches, namely, multiple regression, and the random tree methodology, we conclude that the one month lagged price of oil is the most significant explanatory variable, among all considered, for the upward trend of U.S. oil production from 2009 to early 2020.

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Little, Todd D., Kristopher J. Preacher, James P. Selig e Noel A. Card. "New developments in latent variable panel analyses of longitudinal data". International Journal of Behavioral Development 31, n.º 4 (julho de 2007): 357–65. http://dx.doi.org/10.1177/0165025407077757.

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We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

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Zwick, Thomas. "The Impact of ICT Investment on Establishment Productivity". National Institute Economic Review 184 (abril de 2003): 99–110. http://dx.doi.org/10.1177/0027950103184001009.

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This paper finds substantial effects of ICT investments on productivity for a large and representative German establishment panel data set. In contrast to the bulk of the literature also establishments without ICT capital are included and lagged effects of ICT investments are analysed. In addition, a broad range of establishment and employee characteristics are taken account of in order to avoid omitted variable bias. It is shown that taking into account unobserved heterogeneity of the establishments and endogeneity of ICT investments increases the estimated lagged productivity impact of ICT investments.

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Beaudoin, Christopher E. "Media Effects on Public Safety following a Natural Disaster: Testing Lagged Dependent Variable Models". Journalism & Mass Communication Quarterly 84, n.º 4 (dezembro de 2007): 695–712. http://dx.doi.org/10.1177/107769900708400403.

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Prescott, D., e T. Stengos. "Hypothesis testing in regression models with AR(1) errors and a lagged dependent variable". Economics Letters 24, n.º 3 (janeiro de 1987): 237–42. http://dx.doi.org/10.1016/0165-1765(87)90123-6.

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Wesso, Gilbert. "The out-of-sample forecasting performance of variable parameter exchange rate models in South Africa". South African Journal of Business Management 26, n.º 2 (30 de junho de 1995): 64–71. http://dx.doi.org/10.4102/sajbm.v26i2.825.

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In this article the out-of-sample forecasting performance of exchange rate determination is examined without imposing the restriction that coefficients are fixed over time. Both fixed and variable coefficient versions of conventional structural models are considered, with and without a lagged dependent variable. A Variable Parameter Regression (VPR) technique based on recursive application of the Kalman filter is used to improve the predictive performance of a class oi monetary exchange rate models.

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Chen, Mingyue, Wanqiu Wang e Arun Kumar. "Lagged Ensembles, Forecast Configuration, and Seasonal Predictions". Monthly Weather Review 141, n.º 10 (25 de setembro de 2013): 3477–97. http://dx.doi.org/10.1175/mwr-d-12-00184.1.

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Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

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Pasara, Michael Takudzwa, e Michael Zuze. "CAN REMITTANCES BOOST TAX REVENUES IN ZIMBABWE? A SECONDARY QUARTERLY TIME-SERIES ANALYSIS". EURASIAN JOURNAL OF ECONOMICS AND FINANCE 9, n.º 2 (2021): 128–44. http://dx.doi.org/10.15604/ejef.2021.09.02.005.

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The study applied the ordinary least squares (OLS) technique on quarterly time-series data to analyze if remittances can boost tax revenue in Zimbabwe. The main challenge faced in Zimbabwe is the insufficient tax revenues to finance growing public spending needs. Results indicate that the share of remittances both in the current and lagged period significantly influenced income tax revenue and the volume of manufacturing. Trade openness was found to be insignificant. Similar results were also observed for the variables when value-added tax to total revenue was the dependent variable. When lagged variables were taken into account, results showedthat only remittances were significant. Thus, increased remittance inflows have significant potential to generate more taxes for the government through income and consumption taxes. The study recommends the creation of platforms, which stimulate and attract more remittances, such as reducing costs of sending remittances through formal channels. Secondly, good governance and quality institutions provide appropriate economic environment and growth policies. Economic growth fosters increased and sustainable tax due to an increased tax base.

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Adepoju, Adedayo A., Oluwayemisi O, Alaba e P. Ogundunmadetayo. "Bayesian estimation of simultaneous equation model with lagged endogenous variables and first order serially correlated errors". Global Journal of Pure and Applied Sciences 24, n.º 2 (18 de dezembro de 2018): 235–44. http://dx.doi.org/10.4314/gjpas.v24i2.14.

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Most simultaneous equation models involve the inclusion of lagged endogenous and/or exogenous variables and sometimes it may be misleading to assume that the errors are normally distributed when in reality they exhibit functional formsthat are not normal especially in practical situations. The classical methods of estimating parameters of simultaneous equation models are usually affected by the presence of autocorrelation among the error terms. Unfortunately, in practice the form of correlation between the pairs of the random deviates is unknown.In this paper classical and Bayesian methods for the estimation of simultaneous equation model withlagged endogenous variables and first order serially correlated errors are considered. The smallsample properties of the methods at different levels of correlation for ρ = 0.2, 0.5 and 0.8are compared.Better parameter estimates were produced by the Bayesian estimator with smaller standard errors compared to the classical method. The standard deviations of the Bayesian estimator are consistently better than those of the OLS estimator for the sample sizes considered. For example, the standard deviations of the Bayesian for b14 (the coefficient of the lagged endogenous variable,y 1t-1) when ρ = 0.2 at N = 10, 15, 20 and 25 were 0.07712781, 0.05433923, 0.03230012 and 0.03177252 respectively while those of OLS were 0.0784732, 0.4718914, 0.05701936 and 1.31422868. However, when ρ = 0.8, the standard deviations were 0.0548055, 0.03860254, 0.02572899 and 0.02126175 for Bayesian and 0.0562190, 0.03882345, 0.053676 and 0.0315632 for OLS. Interestingly, notice that even at high correlation level, the estimates produced by the Bayesian method are closer to the parameter values and the standard deviations decrease as the sample size increases. Hence, the Bayesian estimation method might be a better choice when lagged endogenous variables are included in a simultaneous equation model with auto-correlated disturbances since it appeared to give better results compared to the classical approach.Keywords: Bayesian estimation, Lagged endogenous variables, Simultaneous equations, Monte-Carlo Simulation, First-order autoregressive process.

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Widyawati, Dessy, e Astiwi Indriani. "Determinants of dividend payout ratio: evidence from Indonesian manufacturing companies". Diponegoro International Journal of Business 2, n.º 2 (30 de dezembro de 2019): 112. http://dx.doi.org/10.14710/dijb.2.2.2019.112-121.

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The purpose of this study is to investigate and analyze the relationship between return on assets, growth sales, debt to equity ratio, lagged dividend to dividend payout ratio and size as control variable. Data collected from manufacturing industries in Indonesian Stock Exchange 2011-2017. The method of this study is ordinary least square regression. The results of this study shows that return on assets and lagged dividend have positive impact to dividend payout ratio. Growth sales has insignificance negative relationship to dividend payout ratio. Debt to equity ratio has positive relationship to dividend payout ratio and has insignificance sign

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Haltuch, Melissa A., Owen S. Hamel, Kevin R. Piner, Patrick McDonald, Craig R. Kastelle e John C. Field. "A California Current bomb radiocarbon reference chronology and petrale sole (Eopsetta jordani) age validation". Canadian Journal of Fisheries and Aquatic Sciences 70, n.º 1 (janeiro de 2013): 22–31. http://dx.doi.org/10.1139/cjfas-2011-0504.

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As petrale sole (Eopsetta jordani) is a valuable groundfish harvested in the California Current, proper ageing is important for its assessment and management. This study presents the first bomb radiocarbon reference chronology for the California Current and petrale sole age validation. Break-and-burn and surface ages are negatively biased by approximately 1 year and 2–3 years, respectively. The reference and validation curves are more variable and show a lag in the rate of radiocarbon increase in comparison to most other time series of bomb radiocarbon in marine systems. Upwelling in the California Current produces a lagged rate of increase in radiocarbon levels owing to the introduction and mixing of radiocarbon-depleted deep waters with surface waters that interact with the atmosphere. The variable and lagged rate of radiocarbon increase in the petrale sole data may be due to their spending a substantial portion of their first year of life in areas subject to variable upwelling, illustrating the importance of using reference curves for age validation that are region and species specific when possible.

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Kaplan, David. "Modeling Sustained Educational Change With Panel Data: The Case for Dynamic Multiplier Analysis". Journal of Educational and Behavioral Statistics 27, n.º 2 (junho de 2002): 85–103. http://dx.doi.org/10.3102/10769986027002085.

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This article considers the problem of modeling sustained educational change via the use of dynamic multipliers applied to panel data. Dynamic multipliers arise from the incorporation of lagged endogenous variables in linear models. Three types of dynamic multipliers can be defined: (a) the impact multiplier, (b) interim multipliers, and (c) the long-run equilibrium multiplier. An impact multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable in the particular sample period. An interim multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable when that effect is sustained for a specified amount of time. A long-run equilibrium multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable when sustained into the indefinite future. This article seeks to develop and advocate dynamic multiplier analysis for education research. Extensions to multivariate dynamic linear models and multilevel linear models are provided. Three examples are presented to illustrate the methodology. The article closes with a discussion of the implications of dynamic multiplier analysis for education policy analysis.

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Susanti, Weni, Kamaludin Kamaludin, Rini Indriani e Fachruzzaman Fachruzzaman. "Dividend policy on regional development banks in Indonesia". Accounting 7, n.º 7 (2021): 1635–44. http://dx.doi.org/10.5267/j.ac.2021.5.007.

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This study aims to analyze the variable confirmation between the dividend payout ratio variable with the profitability variable and the lagged dividend variable by looking at the role of the share ownership variable as a dummy mediate variable. The research subject was carried out at the Regional Development Bank (BPD) in Indonesia. This study uses data and samples taken from data issued by the OJK (Financial Services Authority). Regional development banks were chosen because they have a different role in determining their dividend policy compared to other types of banks, but although this bank is different in its dividend distribution process, it is still capable of surviving even in times of crisis (Covid-19). By using OLS regression analysis, this study divides the research sample into a dummy group consisting of share ownership variables, these subsections are things that must be considered because they can be the key to why this type of bank is able to survive when other banks start to rush. goofy in giving dividends.

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Beck, Nathaniel, e Jonathan N. Katz. "Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models". Political Analysis 6 (1996): 1–36. http://dx.doi.org/10.1093/pan/6.1.1.

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In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.

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Osabuohien, Evans, Uchenna R. Efobi e Ciliaka M. Gitau. "Environment challenges in Africa: further dimensions to the trade, MNCs and energy debate". Management of Environmental Quality: An International Journal 26, n.º 1 (12 de janeiro de 2015): 118–37. http://dx.doi.org/10.1108/meq-04-2014-0058.

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Purpose – The purpose of this paper is to examine the linkage between environmental challenges, multinational corporations (MNCs) activities, trade and energy in Africa; and further elaborate on the role of institutions, as an intervening variable. Design/methodology/approach – In this study, the authors extended the Environmental Kuznets Curve (EKC) model by including indicators of the presence of MNCs, trade and energy in the basic EKC model that has measures of environmental pollution (CO2), economic growth (gross domestic product per capita) and its squared value. The role of institutions was also considered and included as an inter-mediating variable. This model was tested on a sample of 27 African countries, for the period 1996-2010. The systems GMM was applied for the empirical analysis. This approach was aimed at circumventing the possibility of reverse causality and endogenous explanatory variables-such as institutions. Findings – Trade and MNCs’ activities may not have much contemporaneous impact on the environment. However, their lagged values have adverse and significant influence on the current values of environmental challenge. This implies that environmental policies regarding trade and MNCs require time response lag. Energy was significant only at contemporaneous value but not at its lagged value. Institutional development helps to suppress the negative excesses (like pollution) from the activities of trade, MNCs and energy, and consequently reduce environmental pollution. Originality/value – This paper included the role of institutions in the environmental pollution, trade, MNCs and energy debate. Empirical studies in this regard have inadvertently excluded this variable, but have, at best, included it as part of policy recommendations.

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Jiang, Wenxin, e Martin A. Tanner. "RISK MINIMIZATION FOR TIME SERIES BINARY CHOICE WITH VARIABLE SELECTION". Econometric Theory 26, n.º 5 (5 de março de 2010): 1437–52. http://dx.doi.org/10.1017/s0266466609990636.

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This paper considers the problem of predicting binary choices by selecting from a possibly large set of candidate explanatory variables, which can include both exogenous variables and lagged dependent variables. We consider risk minimization with the risk function being the predictive classification error. We study the convergence rates of empirical risk minimization in both the frequentist and Bayesian approaches. The Bayesian treatment uses a Gibbs posterior constructed directly from the empirical risk instead of using the usual likelihood-based posterior. Therefore these approaches do not require a correctly specified probability model. We show that the proposed methods have near optimal performance relative to a class of linear classification rules with selected variables. Such results in classification are obtained in a framework of dependent data with strong mixing.

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Lewis-Beck, Michael S., e Charles Tien. "The Referendum Model: A 2010 Congressional Forecast". PS: Political Science & Politics 43, n.º 04 (outubro de 2010): 637–38. http://dx.doi.org/10.1017/s1049096510001071.

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Congressional election forecasting has experienced steady growth. Currently fashionable models stress prediction over explanation. The independent variables do not offer a substantive account of the election outcome. Instead, these variables aretrackingvariables—that is, indicators that may trace the result but fail to explain it. The outstanding example is the generic ballot measure, which asks respondents for whom they plan to vote in the upcoming congressional race. While this variable correlates highly with presidential party House seat share, it is bereft of substance. The generic ballot measure is the archetypical tracking variable, and it holds pride of place in the Abramowitz (2010) model. Other examples of such tracking variables are exposed seats or lagged seats, features of the Campbell (2010) model. The difficulty with such tracking models is twofold. First, they are not based on a theory of the congressional vote. Second, because they are predictive models, they offer a suboptimal forecasting instrument when compared to models specified according to strong theory.

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PRATIWI, LUH PUTU SAFITRI, I. GUSTI AYU MADE SRINADI e MADE SUSILAWATI. "ANALISIS KEMISKINAN DENGAN PENDEKATAN MODEL REGRESI SPASIAL DURBIN (Studi Kasus: Kabupaten Gianyar)". E-Jurnal Matematika 2, n.º 3 (30 de agosto de 2013): 11. http://dx.doi.org/10.24843/mtk.2013.v02.i03.p042.

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Poverty still a complex problems for both at national and regional level, so it requires an appropriate and sustainable strategy mitigation. Every household in the different regions has different characteristics and influenced factors, so it requires the identification of the factors that influence poverty by paying attention to the influence of the area using the Spatial Durbin Model (SDM). The purpose of SDM modeling is to determine the spatial dependency relationship which occur not only in the dependent variables, but also on the independent variables. The result shows that the significant lagged dependent variable is indicated by the parameter Lag significant independent variables are independent variables with a significant weighting, but there are no independent variables that are significant with the weighting.

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Yamada, Hirokazu, e Yuji Nakayama. "Japanese R&D Profitability". International Journal of Systems and Service-Oriented Engineering 8, n.º 2 (abril de 2018): 16–29. http://dx.doi.org/10.4018/ijssoe.2018040102.

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This article examines the contribution to profit from research and development (R&D) using industry-level accounting panel data for eight industries in Japan from 1986 to 2012. Problematically, simple least-squares regression estimation of production functions, where the authors specify sales or value-added as the explained variable and investment in R&D as the explanatory variable, involve endogeneity. Two possible ways of addressing this are the instrumental variables method and another that utilizes the orthogonality between error terms and appropriately time-lagged explanatory variables. The authors compare how both these methods eliminate endogeneity in the estimated production function and thus improve the accuracy of estimates of the rate of return on R&D. These findings thus contribute to the managerial decision-making process on R&D strategy by providing insights into the precise contribution of firm R&D to profit.

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Owusu-Ansah, Anthony. "Modelling the supply of new residential construction in Aberdeen, UK". International Journal of Housing Markets and Analysis 7, n.º 3 (29 de julho de 2014): 346–62. http://dx.doi.org/10.1108/ijhma-07-2013-0043.

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Purpose – The purpose of this paper is to use local-level time series data to examine the determinants of housing starts and the price elasticity of supply for the Aberdeen local housing market. Design/methodology/approach – Seven time series models are used in the analysis. The basic model treats housing starts as a function of the changes of current and lagged house prices, interest rate and construction cost. The other six models which are extensions of the basic model include other variables like time on the market, planning constraints and future expectations. Findings – It is found that the local variables – changes in house prices, time on the market, planning regulation, lagged stock and lagged and future housing starts – are the main factors that influence new residential construction in Aberdeen. None of the national variables is significant, confirming the importance of limiting housing market analysis to the local level. The price elasticity of supply estimated is in the range of 2.0 to 3.2 for housing starts and 0.01 to 0.02 for housing stock. These estimates are higher than most of the elasticities for the other UK local markets. Originality/value – There is the need to better understand the supply of housing at the various local housing markets. Unfortunately, however, most housing supply studies use national data. Because national data are aggregation of local data, using national studies results for local markets may be uninformative. Also, the few existing local studies use typically cross-section data or at least time series over relatively short time spans. This paper makes an effort to use quarterly time series data over a 25-year period for a local market and also include a planning variable which is different from local markets and often ignored in national or regional studies.

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Rötheli, Tobias. "Generalization of information, Granger causality and forecasting". foresight 19, n.º 6 (13 de novembro de 2017): 604–14. http://dx.doi.org/10.1108/fs-06-2017-0017.

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Purpose This paper aims to analyze forecasting problems from the perspective of information extraction. Circumstances are studied under which the forecast of an economic variable from one domain (country, industry, market segment) should rely on information regarding the same type of variable from another domain even if the two variables are not causally linked. It is shown that Granger causality linking variables from different domains is the rule and should be exploited for forecasting. Design/methodology/approach This paper applies information economics, in particular the study of rational information extraction, to shed light on the debate on causality and forecasting. Findings It is shown that the rational generalization of information across domains can lead to effects that are hard to square with economic intuition but worth considering for forecasting. Information from one domain is shown to affect that from another domain if there is at least one common factor affecting both domains, which is not (or not yet) observed when a forecast has to be made. The analysis suggests the theoretical possibility that the direction of such effects across domains can be counter-intuitive. In time-series econometrics, such effects will show up in estimated coefficients with the “wrong” sign. Practical implications This study helps forecasters by indicating a wider set of variables relevant for prediction. The analysis offers a theoretical basis for using lagged values from the type of variable to be forecast but from another domain. For example, when forecasting the bond risk spread in one country, introducing in the time-series model the lagged value of the risk spread from another country is suggested. Two empirical examples illustrate this principle for specifying models for prediction. The application to risk spreads and inflation rates illustrates the principles of the approach suggested here which is widely applicable. Originality/value The present study builds on a probability theoretic analysis to inform the specification of time-series forecasting models.

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De Blander, Rembert. "Iterative estimation correcting for error auto-correlation in short panels, applied to lagged dependent variable models". Econometrics and Statistics 15 (julho de 2020): 3–29. http://dx.doi.org/10.1016/j.ecosta.2020.02.001.

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Mietule, Iveta, e Gajane Gukasjan. "ECONOMETRIC MODELING OF THE ECONOMY OF LATVIA". Latgale National Economy Research 1, n.º 5 (21 de outubro de 2013): 167. http://dx.doi.org/10.17770/lner2013vol1.5.1158.

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The article is devoted to the estimation of econometric models of the Latvian economy. The Klein's simplified macroeconomic model of the Latvian economy is discussed. The endogenous variables are consumption, net investment, gross domestic product (excluding net exports and additions to reserves). An exogenous variable is the government spending. The model is just-identified, and Two-stage least squares (2SLS) method provides consistent estimates of the parameters of a structural equation. The modified Keynеsian model was also considered, where the lagged variable - gross domestic product of the previos period is presented. It is proved that the model is over-identified, and the Two-stage least squares (2SLS) method provides estimates of the parameters of a structural equation. We have estimated the models with annual time-series data of the Latvia economy for the years 1995 through 2011 (at basic prices in 2000).

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